diff --git a/.github/workflows/main.yml b/.github/workflows/aux_tests.yml similarity index 93% rename from .github/workflows/main.yml rename to .github/workflows/aux_tests.yml index 68be9274..d5fe1bb3 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/aux_tests.yml @@ -1,4 +1,4 @@ -name: CI +name: aux-tests on: push: @@ -45,8 +45,5 @@ jobs: run: | python3 -m pip install .[all] python3 setup.py egg_info - - name: Lint check - run: | - make lint - name: Unit tests - run: make test + run: make test_aux diff --git a/.github/workflows/style_check.yml b/.github/workflows/style_check.yml new file mode 100644 index 00000000..4a30c26d --- /dev/null +++ b/.github/workflows/style_check.yml @@ -0,0 +1,50 @@ +name: style-check + +on: + push: + branches: + - main + pull_request: + types: [opened, synchronize, reopened] +jobs: + check_skip: + runs-on: ubuntu-latest + if: "! contains(github.event.head_commit.message, '[ci skip]')" + steps: + - run: echo "${{ github.event.head_commit.message }}" + + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: [3.9] + experimental: [false] + steps: + - uses: actions/checkout@v2 + - uses: actions/cache@v1 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('**/setup.py') }} + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + - name: check OS + run: cat /etc/os-release + - name: Install dependencies + run: | + sudo apt update + sudo apt install -y git make + sudo apt install -y python3-wheel gcc + make system-deps + - name: Upgrade pip + run: python3 -m pip install --upgrade pip + - name: Install TTS + run: | + python3 -m pip install .[all] + python3 setup.py egg_info + - name: Lint check + run: | + make lint \ No newline at end of file diff --git a/.github/workflows/tts_tests.yml b/.github/workflows/tts_tests.yml new file mode 100644 index 00000000..d05dca90 --- /dev/null +++ b/.github/workflows/tts_tests.yml @@ -0,0 +1,49 @@ +name: tts-tests + +on: + push: + branches: + - main + pull_request: + types: [opened, synchronize, reopened] +jobs: + check_skip: + runs-on: ubuntu-latest + if: "! contains(github.event.head_commit.message, '[ci skip]')" + steps: + - run: echo "${{ github.event.head_commit.message }}" + + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + experimental: [false] + steps: + - uses: actions/checkout@v2 + - uses: actions/cache@v1 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('**/setup.py') }} + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + - name: check OS + run: cat /etc/os-release + - name: Install dependencies + run: | + sudo apt update + sudo apt install -y git make + sudo apt install -y python3-wheel gcc + make system-deps + - name: Upgrade pip + run: python3 -m pip install --upgrade pip + - name: Install TTS + run: | + python3 -m pip install .[all] + python3 setup.py egg_info + - name: Unit tests + run: make test_tts diff --git a/.github/workflows/vocoder_tests.yml b/.github/workflows/vocoder_tests.yml new file mode 100644 index 00000000..69e74dbf --- /dev/null +++ b/.github/workflows/vocoder_tests.yml @@ -0,0 +1,49 @@ +name: vocoder-tests + +on: + push: + branches: + - main + pull_request: + types: [opened, synchronize, reopened] +jobs: + check_skip: + runs-on: ubuntu-latest + if: "! contains(github.event.head_commit.message, '[ci skip]')" + steps: + - run: echo "${{ github.event.head_commit.message }}" + + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + experimental: [false] + steps: + - uses: actions/checkout@v2 + - uses: actions/cache@v1 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('**/setup.py') }} + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + - name: check OS + run: cat /etc/os-release + - name: Install dependencies + run: | + sudo apt update + sudo apt install -y git make + sudo apt install -y python3-wheel gcc + make system-deps + - name: Upgrade pip + run: python3 -m pip install --upgrade pip + - name: Install TTS + run: | + python3 -m pip install .[all] + python3 setup.py egg_info + - name: Unit tests + run: make test_vocoder diff --git a/.gitignore b/.gitignore index 95939d32..64d1f0d5 100644 --- a/.gitignore +++ b/.gitignore @@ -124,6 +124,15 @@ version.py # jupyter dummy files core +# ignore local datasets +recipes/WIP/* +recipes/ljspeech/LJSpeech-1.1/* +recipes/vctk/VCTK/* +VCTK-Corpus-removed-silence/* + +# ignore training logs +trainer_*_log.txt + # files used internally fro dev, test etc. tests/outputs/* tests/train_outputs/* @@ -131,12 +140,9 @@ TODO.txt .vscode/* data/* notebooks/data/* -TTS/tts/layers/glow_tts/monotonic_align/core.c +TTS/tts/utils/monotonic_align/core.c .vscode-upload.json temp_build/* -recipes/WIP/* -recipes/ljspeech/LJSpeech-1.1/* -recipes/ljspeech/tacotron2-DDC/LJSpeech-1.1/* events.out* old_configs/* model_importers/* diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 89138e47..7175cf34 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -76,7 +76,8 @@ The following steps are tested on an Ubuntu system. 8. Run the tests to see how your updates work with the rest of the project. You can repeat this step multiple times as you implement your changes to make sure you are on the right direction. ```bash - $ make tests + $ make test # stop at the first error + $ make test_all # run all the tests, report all the errors ``` 9. Format your code. We use ```black``` for code and ```isort``` for ```import``` formatting. diff --git a/Makefile b/Makefile index bffed7ff..c2091ca0 100644 --- a/Makefile +++ b/Makefile @@ -12,6 +12,15 @@ test_all: ## run tests and don't stop on an error. test: ## run tests. nosetests -x --with-cov -cov --cover-erase --cover-package TTS tests --nologcapture --with-id + +test_vocoder: ## run vocoder tests. + nosetests tests.vocoder_tests -x --with-cov -cov --cover-erase --cover-package TTS tests.vocoder_tests --nologcapture --with-id + +test_tts: ## run tts tests. + nosetests tests.tts_tests -x --with-cov -cov --cover-erase --cover-package TTS tests.tts_tests --nologcapture --with-id + +test_aux: ## run aux tests. + nosetests tests.aux_tests -x --with-cov -cov --cover-erase --cover-package TTS tests.aux_tests --nologcapture --with-id ./run_bash_tests.sh test_failed: ## only run tests failed the last time. diff --git a/README.md b/README.md index 577eb3e9..fd9cd27c 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,8 @@ 📄 [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers) + + ## 💬 Where to ask questions Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it. @@ -154,5 +156,3 @@ If you are on Windows, 👑@GuyPaddock wrote installation instructions [here](ht |- vocoder/ (Vocoder models.) |- (same) ``` - - \ No newline at end of file diff --git a/TTS/.models.json b/TTS/.models.json index 37288dc5..6d353764 100644 --- a/TTS/.models.json +++ b/TTS/.models.json @@ -4,7 +4,7 @@ "ek1": { "tacotron2": { "description": "EK1 en-rp tacotron2 by NMStoker", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.1.0/tts_models--en--ek1--tacotron2.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.1.0/tts_models--en--ek1--tacotron2.zip", "default_vocoder": "vocoder_models/en/ek1/wavegrad", "commit": "c802255" } @@ -12,7 +12,7 @@ "ljspeech": { "tacotron2-DDC": { "description": "Tacotron2 with Double Decoder Consistency.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.12/tts_models--en--ljspeech--tacotron2-DDC.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.12/tts_models--en--ljspeech--tacotron2-DDC.zip", "default_vocoder": "vocoder_models/en/ljspeech/hifigan_v2", "commit": "bae2ad0f", "author": "Eren Gölge @erogol", @@ -21,7 +21,7 @@ }, "tacotron2-DDC_ph": { "description": "Tacotron2 with Double Decoder Consistency with phonemes.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.2.0/tts_models--en--ljspeech--tacotronDDC_ph.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.2.0/tts_models--en--ljspeech--tacotronDDC_ph.zip", "default_vocoder": "vocoder_models/en/ljspeech/univnet", "commit": "3900448", "author": "Eren Gölge @erogol", @@ -30,7 +30,7 @@ }, "glow-tts": { "description": "", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/tts_models--en--ljspeech--glow-tts.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/tts_models--en--ljspeech--glow-tts.zip", "stats_file": null, "default_vocoder": "vocoder_models/en/ljspeech/multiband-melgan", "commit": "", @@ -40,7 +40,7 @@ }, "speedy-speech": { "description": "Speedy Speech model trained on LJSpeech dataset using the Alignment Network for learning the durations.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.3.0/tts_models--en--ljspeech--speedy_speech.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.3.0/tts_models--en--ljspeech--speedy_speech.zip", "stats_file": null, "default_vocoder": "vocoder_models/en/ljspeech/hifigan_v2", "commit": "4581e3d", @@ -50,7 +50,7 @@ }, "tacotron2-DCA": { "description": "", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/tts_models--en--ljspeech--tacotron2-DCA.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/tts_models--en--ljspeech--tacotron2-DCA.zip", "default_vocoder": "vocoder_models/en/ljspeech/multiband-melgan", "commit": "", "author": "Eren Gölge @erogol", @@ -59,7 +59,7 @@ }, "vits": { "description": "VITS is an End2End TTS model trained on LJSpeech dataset with phonemes.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.2.0/tts_models--en--ljspeech--vits.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.2.0/tts_models--en--ljspeech--vits.zip", "default_vocoder": null, "commit": "3900448", "author": "Eren Gölge @erogol", @@ -68,7 +68,7 @@ }, "fast_pitch": { "description": "FastPitch model trained on LJSpeech using the Aligner Network", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.2.2/tts_models--en--ljspeech--fast_pitch.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.2.2/tts_models--en--ljspeech--fast_pitch.zip", "default_vocoder": "vocoder_models/en/ljspeech/hifigan_v2", "commit": "b27b3ba", "author": "Eren Gölge @erogol", @@ -79,7 +79,7 @@ "vctk": { "sc-glow-tts": { "description": "Multi-Speaker Transformers based SC-Glow model from https://arxiv.org/abs/2104.05557.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.1.0/tts_models--en--vctk--sc-glow-tts.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.1.0/tts_models--en--vctk--sc-glow-tts.zip", "default_vocoder": "vocoder_models/en/vctk/hifigan_v2", "commit": "b531fa69", "author": "Edresson Casanova", @@ -88,18 +88,27 @@ }, "vits": { "description": "VITS End2End TTS model trained on VCTK dataset with 109 different speakers with EN accent.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.2.0/tts_models--en--vctk--vits.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.2.0/tts_models--en--vctk--vits.zip", "default_vocoder": null, "commit": "3900448", "author": "Eren @erogol", "license": "", "contact": "egolge@coqui.ai" + }, + "fast_pitch":{ + "description": "FastPitch model trained on VCTK dataseset.", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.4.0/tts_models--en--vctk--fast_pitch.zip", + "default_vocoder": "vocoder_models/en/vctk/hifigan_v2", + "commit": "bdab788d", + "author": "Eren @erogol", + "license": "CC BY-NC-ND 4.0", + "contact": "egolge@coqui.ai" } }, "sam": { "tacotron-DDC": { "description": "Tacotron2 with Double Decoder Consistency trained with Aceenture's Sam dataset.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.13/tts_models--en--sam--tacotron_DDC.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.13/tts_models--en--sam--tacotron_DDC.zip", "default_vocoder": "vocoder_models/en/sam/hifigan_v2", "commit": "bae2ad0f", "author": "Eren Gölge @erogol", @@ -111,7 +120,7 @@ "es": { "mai": { "tacotron2-DDC": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/tts_models--es--mai--tacotron2-DDC.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/tts_models--es--mai--tacotron2-DDC.zip", "default_vocoder": "vocoder_models/universal/libri-tts/fullband-melgan", "commit": "", "author": "Eren Gölge @erogol", @@ -123,7 +132,7 @@ "fr": { "mai": { "tacotron2-DDC": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/tts_models--fr--mai--tacotron2-DDC.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/tts_models--fr--mai--tacotron2-DDC.zip", "default_vocoder": "vocoder_models/universal/libri-tts/fullband-melgan", "commit": "", "author": "Eren Gölge @erogol", @@ -132,10 +141,21 @@ } } }, + "uk":{ + "mai": { + "glow-tts": { + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.4.0/tts_models--uk--mailabs--glow-tts.zip", + "author":"@robinhad", + "commit": "bdab788d", + "license": "MIT", + "contact": "" + } + } + }, "zh-CN": { "baker": { "tacotron2-DDC-GST": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.10/tts_models--zh-CN--baker--tacotron2-DDC-GST.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.10/tts_models--zh-CN--baker--tacotron2-DDC-GST.zip", "commit": "unknown", "author": "@kirianguiller", "default_vocoder": null @@ -145,7 +165,7 @@ "nl": { "mai": { "tacotron2-DDC": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.10/tts_models--nl--mai--tacotron2-DDC.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.10/tts_models--nl--mai--tacotron2-DDC.zip", "author": "@r-dh", "default_vocoder": "vocoder_models/nl/mai/parallel-wavegan", "stats_file": null, @@ -156,7 +176,7 @@ "de": { "thorsten": { "tacotron2-DCA": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.11/tts_models--de--thorsten--tacotron2-DCA.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.11/tts_models--de--thorsten--tacotron2-DCA.zip", "default_vocoder": "vocoder_models/de/thorsten/fullband-melgan", "author": "@thorstenMueller", "commit": "unknown" @@ -166,7 +186,7 @@ "ja": { "kokoro": { "tacotron2-DDC": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.15/tts_models--jp--kokoro--tacotron2-DDC.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.15/tts_models--jp--kokoro--tacotron2-DDC.zip", "default_vocoder": "vocoder_models/ja/kokoro/hifigan_v1", "description": "Tacotron2 with Double Decoder Consistency trained with Kokoro Speech Dataset.", "author": "@kaiidams", @@ -179,14 +199,14 @@ "universal": { "libri-tts": { "wavegrad": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/vocoder_models--universal--libri-tts--wavegrad.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/vocoder_models--universal--libri-tts--wavegrad.zip", "commit": "ea976b0", "author": "Eren Gölge @erogol", "license": "MPL", "contact": "egolge@coqui.com" }, "fullband-melgan": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/vocoder_models--universal--libri-tts--fullband-melgan.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/vocoder_models--universal--libri-tts--fullband-melgan.zip", "commit": "4132240", "author": "Eren Gölge @erogol", "license": "MPL", @@ -198,13 +218,13 @@ "ek1": { "wavegrad": { "description": "EK1 en-rp wavegrad by NMStoker", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.10/vocoder_models--en--ek1--wavegrad.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.10/vocoder_models--en--ek1--wavegrad.zip", "commit": "c802255" } }, "ljspeech": { "multiband-melgan": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.9/vocoder_models--en--ljspeech--mulitband-melgan.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.9/vocoder_models--en--ljspeech--mulitband-melgan.zip", "commit": "ea976b0", "author": "Eren Gölge @erogol", "license": "MPL", @@ -212,7 +232,7 @@ }, "hifigan_v2": { "description": "HiFiGAN_v2 LJSpeech vocoder from https://arxiv.org/abs/2010.05646.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.12/vocoder_model--en--ljspeech-hifigan_v2.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.12/vocoder_model--en--ljspeech-hifigan_v2.zip", "commit": "bae2ad0f", "author": "@erogol", "license": "", @@ -220,7 +240,7 @@ }, "univnet": { "description": "UnivNet model finetuned on TacotronDDC_ph spectrograms for better compatibility.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.3.0/vocoder_models--en--ljspeech--univnet_v2.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.3.0/vocoder_models--en--ljspeech--univnet_v2.zip", "commit": "4581e3d", "author": "Eren @erogol", "license": "TBD", @@ -230,7 +250,7 @@ "vctk": { "hifigan_v2": { "description": "Finetuned and intended to be used with tts_models/en/vctk/sc-glow-tts", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.12/vocoder_model--en--vctk--hifigan_v2.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.12/vocoder_model--en--vctk--hifigan_v2.zip", "commit": "2f07160", "author": "Edresson Casanova", "license": "", @@ -240,7 +260,7 @@ "sam": { "hifigan_v2": { "description": "Finetuned and intended to be used with tts_models/en/sam/tacotron_DDC", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.13/vocoder_models--en--sam--hifigan_v2.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.13/vocoder_models--en--sam--hifigan_v2.zip", "commit": "2f07160", "author": "Eren Gölge @erogol", "license": "", @@ -251,7 +271,7 @@ "nl": { "mai": { "parallel-wavegan": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.10/vocoder_models--nl--mai--parallel-wavegan.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.10/vocoder_models--nl--mai--parallel-wavegan.zip", "author": "@r-dh", "commit": "unknown" } @@ -260,12 +280,12 @@ "de": { "thorsten": { "wavegrad": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.11/vocoder_models--de--thorsten--wavegrad.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.0.11/vocoder_models--de--thorsten--wavegrad.zip", "author": "@thorstenMueller", "commit": "unknown" }, "fullband-melgan": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.1.3/vocoder_models--de--thorsten--fullband-melgan.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.1.3/vocoder_models--de--thorsten--fullband-melgan.zip", "author": "@thorstenMueller", "commit": "unknown" } @@ -274,7 +294,7 @@ "ja": { "kokoro": { "hifigan_v1": { - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.2.0/vocoder_models--ja--kokoro--hifigan_v1.zip", + "github_rls_url": "https://coqui.gateway.scarf.sh/v0.2.0/vocoder_models--ja--kokoro--hifigan_v1.zip", "description": "HifiGAN model trained for kokoro dataset by @kaiidams", "author": "@kaiidams", "commit": "3900448" diff --git a/TTS/bin/compute_embeddings.py b/TTS/bin/compute_embeddings.py index 8c4d275f..83a5aeae 100644 --- a/TTS/bin/compute_embeddings.py +++ b/TTS/bin/compute_embeddings.py @@ -5,7 +5,7 @@ from argparse import RawTextHelpFormatter from tqdm import tqdm from TTS.config import load_config -from TTS.tts.datasets import load_meta_data +from TTS.tts.datasets import load_tts_samples from TTS.tts.utils.speakers import SpeakerManager parser = argparse.ArgumentParser( @@ -36,7 +36,7 @@ args = parser.parse_args() c_dataset = load_config(args.config_dataset_path) -meta_data_train, meta_data_eval = load_meta_data(c_dataset.datasets, eval_split=args.eval) +meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=args.eval) wav_files = meta_data_train + meta_data_eval speaker_manager = SpeakerManager( diff --git a/TTS/bin/compute_statistics.py b/TTS/bin/compute_statistics.py index 6179dafc..e1974ae7 100755 --- a/TTS/bin/compute_statistics.py +++ b/TTS/bin/compute_statistics.py @@ -10,7 +10,7 @@ from tqdm import tqdm # from TTS.utils.io import load_config from TTS.config import load_config -from TTS.tts.datasets import load_meta_data +from TTS.tts.datasets import load_tts_samples from TTS.utils.audio import AudioProcessor @@ -41,7 +41,7 @@ def main(): if args.data_path: dataset_items = glob.glob(os.path.join(args.data_path, "**", "*.wav"), recursive=True) else: - dataset_items = load_meta_data(CONFIG.datasets)[0] # take only train data + dataset_items = load_tts_samples(CONFIG.datasets)[0] # take only train data print(f" > There are {len(dataset_items)} files.") mel_sum = 0 diff --git a/TTS/bin/extract_tts_spectrograms.py b/TTS/bin/extract_tts_spectrograms.py index 681fcc36..0af98ff1 100755 --- a/TTS/bin/extract_tts_spectrograms.py +++ b/TTS/bin/extract_tts_spectrograms.py @@ -10,8 +10,7 @@ from torch.utils.data import DataLoader from tqdm import tqdm from TTS.config import load_config -from TTS.tts.datasets import load_meta_data -from TTS.tts.datasets.TTSDataset import TTSDataset +from TTS.tts.datasets import TTSDataset, load_tts_samples from TTS.tts.models import setup_model from TTS.tts.utils.speakers import get_speaker_manager from TTS.utils.audio import AudioProcessor @@ -230,7 +229,7 @@ def main(args): # pylint: disable=redefined-outer-name ap = AudioProcessor(**c.audio) # load data instances - meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=args.eval) + meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=args.eval) # use eval and training partitions meta_data = meta_data_train + meta_data_eval diff --git a/TTS/bin/find_unique_chars.py b/TTS/bin/find_unique_chars.py index 16768e43..437c2d60 100644 --- a/TTS/bin/find_unique_chars.py +++ b/TTS/bin/find_unique_chars.py @@ -3,7 +3,7 @@ import argparse from argparse import RawTextHelpFormatter from TTS.config import load_config -from TTS.tts.datasets import load_meta_data +from TTS.tts.datasets import load_tts_samples def main(): @@ -23,7 +23,7 @@ def main(): c = load_config(args.config_path) # load all datasets - train_items, eval_items = load_meta_data(c.datasets, eval_split=True) + train_items, eval_items = load_tts_samples(c.datasets, eval_split=True) items = train_items + eval_items texts = "".join(item[0] for item in items) diff --git a/TTS/bin/train_encoder.py b/TTS/bin/train_encoder.py index 7ff35486..ad6d95f7 100644 --- a/TTS/bin/train_encoder.py +++ b/TTS/bin/train_encoder.py @@ -12,9 +12,9 @@ from torch.utils.data import DataLoader from TTS.speaker_encoder.dataset import SpeakerEncoderDataset from TTS.speaker_encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss from TTS.speaker_encoder.utils.generic_utils import save_best_model, setup_model +from TTS.speaker_encoder.utils.training import init_training from TTS.speaker_encoder.utils.visual import plot_embeddings -from TTS.trainer import init_training -from TTS.tts.datasets import load_meta_data +from TTS.tts.datasets import load_tts_samples from TTS.utils.audio import AudioProcessor from TTS.utils.generic_utils import count_parameters, remove_experiment_folder, set_init_dict from TTS.utils.io import load_fsspec @@ -156,7 +156,7 @@ def main(args): # pylint: disable=redefined-outer-name optimizer = RAdam(model.parameters(), lr=c.lr) # pylint: disable=redefined-outer-name - meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=False) + meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=False) data_loader, num_speakers = setup_loader(ap, is_val=False, verbose=True) @@ -208,7 +208,7 @@ def main(args): # pylint: disable=redefined-outer-name if __name__ == "__main__": - args, c, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = init_training(sys.argv) + args, c, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = init_training() try: main(args) diff --git a/TTS/bin/train_tts.py b/TTS/bin/train_tts.py index 863bd3b9..e28e9dec 100644 --- a/TTS/bin/train_tts.py +++ b/TTS/bin/train_tts.py @@ -1,12 +1,71 @@ -import sys +import os -from TTS.trainer import Trainer, init_training +from TTS.config import load_config, register_config +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models import setup_model +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor def main(): - """Run 🐸TTS trainer from terminal. This is also necessary to run DDP training by ```distribute.py```""" - args, config, output_path, _, c_logger, dashboard_logger = init_training(sys.argv) - trainer = Trainer(args, config, output_path, c_logger, dashboard_logger, cudnn_benchmark=False) + """Run `tts` model training directly by a `config.json` file.""" + # init trainer args + train_args = TrainingArgs() + parser = train_args.init_argparse(arg_prefix="") + + # override trainer args from comman-line args + args, config_overrides = parser.parse_known_args() + train_args.parse_args(args) + + # load config.json and register + if args.config_path or args.continue_path: + if args.config_path: + # init from a file + config = load_config(args.config_path) + if len(config_overrides) > 0: + config.parse_known_args(config_overrides, relaxed_parser=True) + elif args.continue_path: + # continue from a prev experiment + config = load_config(os.path.join(args.continue_path, "config.json")) + if len(config_overrides) > 0: + config.parse_known_args(config_overrides, relaxed_parser=True) + else: + # init from console args + from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel + + config_base = BaseTrainingConfig() + config_base.parse_known_args(config_overrides) + config = register_config(config_base.model)() + + # load training samples + train_samples, eval_samples = load_tts_samples(config.datasets, eval_split=True) + + # setup audio processor + ap = AudioProcessor(**config.audio) + + # init speaker manager + if config.use_speaker_embedding: + speaker_manager = SpeakerManager(data_items=train_samples + eval_samples) + elif config.use_d_vector_file: + speaker_manager = SpeakerManager(d_vectors_file_path=config.d_vector_file) + else: + speaker_manager = None + + # init the model from config + model = setup_model(config, speaker_manager) + + # init the trainer and 🚀 + trainer = Trainer( + train_args, + config, + config.output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, + parse_command_line_args=False, + ) trainer.fit() diff --git a/TTS/bin/train_vocoder.py b/TTS/bin/train_vocoder.py index 000083e0..cd665f29 100644 --- a/TTS/bin/train_vocoder.py +++ b/TTS/bin/train_vocoder.py @@ -1,26 +1,69 @@ import os -import sys -import traceback -from TTS.trainer import Trainer, init_training -from TTS.utils.generic_utils import remove_experiment_folder +from TTS.config import load_config, register_config +from TTS.trainer import Trainer, TrainingArgs +from TTS.utils.audio import AudioProcessor +from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data +from TTS.vocoder.models import setup_model def main(): - try: - args, config, output_path, _, c_logger, dashboard_logger = init_training(sys.argv) - trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) - trainer.fit() - except KeyboardInterrupt: - remove_experiment_folder(output_path) - try: - sys.exit(0) - except SystemExit: - os._exit(0) # pylint: disable=protected-access - except Exception: # pylint: disable=broad-except - remove_experiment_folder(output_path) - traceback.print_exc() - sys.exit(1) + """Run `tts` model training directly by a `config.json` file.""" + # init trainer args + train_args = TrainingArgs() + parser = train_args.init_argparse(arg_prefix="") + + # override trainer args from comman-line args + args, config_overrides = parser.parse_known_args() + train_args.parse_args(args) + + # load config.json and register + if args.config_path or args.continue_path: + if args.config_path: + # init from a file + config = load_config(args.config_path) + if len(config_overrides) > 0: + config.parse_known_args(config_overrides, relaxed_parser=True) + elif args.continue_path: + # continue from a prev experiment + config = load_config(os.path.join(args.continue_path, "config.json")) + if len(config_overrides) > 0: + config.parse_known_args(config_overrides, relaxed_parser=True) + else: + # init from console args + from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel + + config_base = BaseTrainingConfig() + config_base.parse_known_args(config_overrides) + config = register_config(config_base.model)() + + # load training samples + if "feature_path" in config and config.feature_path: + # load pre-computed features + print(f" > Loading features from: {config.feature_path}") + eval_samples, train_samples = load_wav_feat_data(config.data_path, config.feature_path, config.eval_split_size) + else: + # load data raw wav files + eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) + + # setup audio processor + ap = AudioProcessor(**config.audio) + + # init the model from config + model = setup_model(config) + + # init the trainer and 🚀 + trainer = Trainer( + train_args, + config, + config.output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, + parse_command_line_args=False, + ) + trainer.fit() if __name__ == "__main__": diff --git a/TTS/config/__init__.py b/TTS/config/__init__.py index ea98f431..f626163f 100644 --- a/TTS/config/__init__.py +++ b/TTS/config/__init__.py @@ -36,10 +36,11 @@ def register_config(model_name: str) -> Coqpit: Coqpit: config class. """ config_class = None + config_name = model_name + "_config" paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.speaker_encoder"] for path in paths: try: - config_class = find_module(path, model_name + "_config") + config_class = find_module(path, config_name) except ModuleNotFoundError: pass if config_class is None: diff --git a/TTS/model.py b/TTS/model.py index cfd1ec62..532d05a6 100644 --- a/TTS/model.py +++ b/TTS/model.py @@ -6,8 +6,6 @@ import torch from coqpit import Coqpit from torch import nn -from TTS.utils.audio import AudioProcessor - # pylint: skip-file @@ -22,6 +20,14 @@ class BaseModel(nn.Module, ABC): - 1D tensors `batch x 1` """ + def __init__(self, config: Coqpit): + super().__init__() + self._set_model_args(config) + + def _set_model_args(self, config: Coqpit): + """Set model arguments from the config. Override this.""" + pass + @abstractmethod def forward(self, input: torch.Tensor, *args, aux_input={}, **kwargs) -> Dict: """Forward pass for the model mainly used in training. @@ -73,7 +79,7 @@ class BaseModel(nn.Module, ABC): ... return outputs_dict, loss_dict - def train_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]: + def train_log(self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int) -> None: """Create visualizations and waveform examples for training. For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to @@ -87,7 +93,7 @@ class BaseModel(nn.Module, ABC): Returns: Tuple[Dict, np.ndarray]: training plots and output waveform. """ - return None, None + pass @abstractmethod def eval_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]: @@ -106,9 +112,9 @@ class BaseModel(nn.Module, ABC): ... return outputs_dict, loss_dict - def eval_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]: + def eval_log(self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int) -> None: """The same as `train_log()`""" - return None, None + pass @abstractmethod def load_checkpoint(self, config: Coqpit, checkpoint_path: str, eval: bool = False) -> None: diff --git a/TTS/speaker_encoder/utils/training.py b/TTS/speaker_encoder/utils/training.py new file mode 100644 index 00000000..a32f43bd --- /dev/null +++ b/TTS/speaker_encoder/utils/training.py @@ -0,0 +1,94 @@ +import os + +from coqpit import Coqpit + +from TTS.config import load_config, register_config +from TTS.trainer import TrainingArgs +from TTS.tts.utils.text.symbols import parse_symbols +from TTS.utils.generic_utils import get_experiment_folder_path, get_git_branch +from TTS.utils.io import copy_model_files +from TTS.utils.logging import init_dashboard_logger +from TTS.utils.logging.console_logger import ConsoleLogger +from TTS.utils.trainer_utils import get_last_checkpoint + + +def getarguments(): + train_config = TrainingArgs() + parser = train_config.init_argparse(arg_prefix="") + return parser + + +def process_args(args, config=None): + """Process parsed comand line arguments and initialize the config if not provided. + Args: + args (argparse.Namespace or dict like): Parsed input arguments. + config (Coqpit): Model config. If none, it is generated from `args`. Defaults to None. + Returns: + c (TTS.utils.io.AttrDict): Config paramaters. + out_path (str): Path to save models and logging. + audio_path (str): Path to save generated test audios. + c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does + logging to the console. + dashboard_logger (WandbLogger or TensorboardLogger): Class that does the dashboard Logging + TODO: + - Interactive config definition. + """ + if isinstance(args, tuple): + args, coqpit_overrides = args + if args.continue_path: + # continue a previous training from its output folder + experiment_path = args.continue_path + args.config_path = os.path.join(args.continue_path, "config.json") + args.restore_path, best_model = get_last_checkpoint(args.continue_path) + if not args.best_path: + args.best_path = best_model + # init config if not already defined + if config is None: + if args.config_path: + # init from a file + config = load_config(args.config_path) + else: + # init from console args + from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel + + config_base = BaseTrainingConfig() + config_base.parse_known_args(coqpit_overrides) + config = register_config(config_base.model)() + # override values from command-line args + config.parse_known_args(coqpit_overrides, relaxed_parser=True) + experiment_path = args.continue_path + if not experiment_path: + experiment_path = get_experiment_folder_path(config.output_path, config.run_name) + audio_path = os.path.join(experiment_path, "test_audios") + config.output_log_path = experiment_path + # setup rank 0 process in distributed training + dashboard_logger = None + if args.rank == 0: + new_fields = {} + if args.restore_path: + new_fields["restore_path"] = args.restore_path + new_fields["github_branch"] = get_git_branch() + # if model characters are not set in the config file + # save the default set to the config file for future + # compatibility. + if config.has("characters") and config.characters is None: + used_characters = parse_symbols() + new_fields["characters"] = used_characters + copy_model_files(config, experiment_path, new_fields) + dashboard_logger = init_dashboard_logger(config) + c_logger = ConsoleLogger() + return config, experiment_path, audio_path, c_logger, dashboard_logger + + +def init_arguments(): + train_config = TrainingArgs() + parser = train_config.init_argparse(arg_prefix="") + return parser + + +def init_training(config: Coqpit = None): + """Initialization of a training run.""" + parser = init_arguments() + args = parser.parse_known_args() + config, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = process_args(args, config) + return args[0], config, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger diff --git a/TTS/trainer.py b/TTS/trainer.py index 8589ae5c..9fcd77a7 100644 --- a/TTS/trainer.py +++ b/TTS/trainer.py @@ -4,16 +4,14 @@ import importlib import multiprocessing import os import platform -import re import sys import time import traceback from argparse import Namespace from dataclasses import dataclass, field -from typing import Dict, List, Tuple, Union -from urllib.parse import urlparse +from inspect import signature +from typing import Callable, Dict, List, Tuple, Union -import fsspec import torch import torch.distributed as dist from coqpit import Coqpit @@ -21,11 +19,6 @@ from torch import nn from torch.nn.parallel import DistributedDataParallel as DDP_th from torch.utils.data import DataLoader -from TTS.config import load_config, register_config -from TTS.tts.datasets import load_meta_data -from TTS.tts.models import setup_model as setup_tts_model -from TTS.tts.utils.text.symbols import parse_symbols -from TTS.utils.audio import AudioProcessor from TTS.utils.callbacks import TrainerCallback from TTS.utils.distribute import init_distributed from TTS.utils.generic_utils import ( @@ -39,9 +32,13 @@ from TTS.utils.generic_utils import ( ) from TTS.utils.io import copy_model_files, load_fsspec, save_best_model, save_checkpoint from TTS.utils.logging import ConsoleLogger, TensorboardLogger, WandbLogger, init_dashboard_logger -from TTS.utils.trainer_utils import get_optimizer, get_scheduler, is_apex_available, setup_torch_training_env -from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data -from TTS.vocoder.models import setup_model as setup_vocoder_model +from TTS.utils.trainer_utils import ( + get_last_checkpoint, + get_optimizer, + get_scheduler, + is_apex_available, + setup_torch_training_env, +) multiprocessing.set_start_method("fork") @@ -80,6 +77,9 @@ class TrainingArgs(Coqpit): "help": "Best model file to be used for extracting the best loss. If not specified, the latest best model in continue path is used" }, ) + skip_train_epoch: bool = field( + default=False, metadata={"help": "Run only evaluation iteration. Useful for debugging."} + ) config_path: str = field(default="", metadata={"help": "Path to the configuration file."}) rank: int = field(default=0, metadata={"help": "Process rank in distributed training."}) group_id: str = field(default="", metadata={"help": "Process group id in distributed training."}) @@ -90,7 +90,7 @@ class TrainingArgs(Coqpit): class Trainer: - def __init__( + def __init__( # pylint: disable=dangerous-default-value self, args: Union[Coqpit, Namespace], config: Coqpit, @@ -98,7 +98,13 @@ class Trainer: c_logger: ConsoleLogger = None, dashboard_logger: Union[TensorboardLogger, WandbLogger] = None, model: nn.Module = None, + get_model: Callable = None, + get_data_samples: Callable = None, + train_samples: List = None, + eval_samples: List = None, cudnn_benchmark: bool = False, + training_assets: Dict = {}, + parse_command_line_args: bool = True, ) -> None: """Simple yet powerful 🐸💬 TTS trainer for PyTorch. It can train all the available `tts` and `vocoder` models or easily be customized. @@ -127,24 +133,44 @@ class Trainer: model (nn.Module, optional): Initialized and ready-to-train model. If it is not defined, `Trainer` initializes a model from the provided config. Defaults to None. + get_model (Callable): + A function that returns a model. It is used to initialize the model when `model` is not provided. + It either takes the config as the only argument or does not take any argument. + Defaults to None + + get_data_samples (Callable): + A function that returns a list of training and evaluation samples. Used if `train_samples` and + `eval_samples` are None. Defaults to None. + + train_samples (List): + A list of training samples used by the model's `get_data_loader` to init the `dataset` and the + `data_loader`. Defaults to None. + + eval_samples (List): + A list of evaluation samples used by the model's `get_data_loader` to init the `dataset` and the + `data_loader`. Defaults to None. + cudnn_benchmark (bool): enable/disable PyTorch cudnn benchmarking. It is better to disable if the model input length is changing batch to batch along the training. + training_assets (Dict): + A dictionary of assets to be used at training and passed to the model's ```train_log(), eval_log(), get_data_loader()``` + during training. It can include `AudioProcessor` or/and `Tokenizer`. Defaults to {}. + + parse_command_line_args (bool): + If true, parse command-line arguments and update `TrainingArgs` and model `config` values. Set it + to false if you parse the arguments yourself. Defaults to True. + Examples: - Running trainer on a model. + Running trainer with HifiGAN model. >>> args = TrainingArgs(...) >>> config = HifiganConfig(...) >>> model = GANModel(config) - >>> trainer = Trainer(args, config, output_path, model=model) - >>> trainer.fit() - - Running trainer on a config. - - >>> config = WavegradConfig(data_path="/home/erogol/nvme/gdrive/Datasets/LJSpeech-1.1/wavs/", output_path=output_path,) - >>> args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) - >>> trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + >>> ap = AudioProcessor(**config.audio) + >>> assets = {"audio_processor": ap} + >>> trainer = Trainer(args, config, output_path, model=model, training_assets=assets) >>> trainer.fit() TODO: @@ -154,20 +180,41 @@ class Trainer: - Profiler integration. - Overfitting to a batch. - TPU training + - NOTE: Consider moving `training_assets` to the model implementation. """ - if config is None: - # parse config from console arguments - config, output_path, _, c_logger, dashboard_logger = process_args(args) + if parse_command_line_args: + # parse command-line arguments for TrainerArgs() + args, coqpit_overrides = self.parse_argv(args) + # get ready for training and parse command-line arguments for the model config + config = self.init_training(args, coqpit_overrides, config) + + # set the output path + if args.continue_path: + # use the same path as the continuing run + output_path = args.continue_path + else: + # override the output path if it is provided + output_path = config.output_path if output_path is None else output_path + # create a new output folder name + output_path = get_experiment_folder_path(config.output_path, config.run_name) + os.makedirs(output_path, exist_ok=True) + + # copy training assets to the output folder + copy_model_files(config, output_path, new_fields=None) + + # init class members self.args = args self.config = config self.output_path = output_path self.config.output_log_path = output_path + self.training_assets = training_assets # setup logging log_file = os.path.join(self.output_path, f"trainer_{args.rank}_log.txt") self._setup_logger_config(log_file) + time.sleep(1.0) # wait for the logger to be ready # set and initialize Pytorch runtime self.use_cuda, self.num_gpus = setup_torch_training_env(True, cudnn_benchmark, args.use_ddp) @@ -196,33 +243,22 @@ class Trainer: self.use_apex = self._is_apex_available() self.use_amp_scaler = self.config.mixed_precision and self.use_cuda - # init audio processor - self.ap = AudioProcessor(**self.config.audio.to_dict()) - # load data samples - # TODO: refactor this - if "datasets" in self.config: - # load data for `tts` models - self.data_train, self.data_eval = load_meta_data(self.config.datasets) - elif self.config.feature_path is not None: - # load pre-comnputed features for `vocoder`models - print(f" > Loading features from: {self.config.feature_path}") - self.data_eval, self.data_train = load_wav_feat_data( - self.config.data_path, self.config.feature_path, self.config.eval_split_size - ) + if train_samples is None and get_data_samples is None: + raise ValueError("[!] `train_samples` and `get_data_samples` cannot both be None.") + if train_samples is not None: + self.train_samples = train_samples + self.eval_samples = eval_samples else: - # load data for `vocoder`models - self.data_eval, self.data_train = load_wav_data(self.config.data_path, self.config.eval_split_size) + self.train_samples, self.eval_samples = self.run_get_data_samples(config, get_data_samples) # init TTS model + if model is None and get_model is None: + raise ValueError("[!] `model` and `get_model` cannot both be None.") if model is not None: self.model = model else: - self.model = self.get_model(self.config) - - # init multispeaker settings of the model - if hasattr(self.model, "init_multispeaker"): - self.model.init_multispeaker(self.config, self.data_train + self.data_eval) + self.run_get_model(self.config, get_model) # setup criterion self.criterion = self.get_criterion(self.model) @@ -247,7 +283,7 @@ class Trainer: # setup optimizer self.optimizer = self.get_optimizer(self.model, self.config) - # callback + # CALLBACK self.callbacks = TrainerCallback(self) self.callbacks.on_init_start() @@ -280,7 +316,7 @@ class Trainer: else: self.scheduler.last_epoch = self.restore_step - # DISTRUBUTED + # DISTRIBUTED if self.num_gpus > 1: self.model = DDP_th(self.model, device_ids=[args.rank], output_device=args.rank) @@ -291,8 +327,56 @@ class Trainer: self.callbacks.on_init_end() @staticmethod - def get_model(config: Coqpit) -> nn.Module: - """Initialize model from config. + def parse_argv(args: Union[Coqpit, List]): + """Parse command line arguments to init or override `TrainingArgs()`.""" + if isinstance(args, Coqpit): + parser = args.init_argparse(arg_prefix="") + else: + train_config = TrainingArgs() + parser = train_config.init_argparse(arg_prefix="") + training_args, coqpit_overrides = parser.parse_known_args() + args.parse_args(training_args) + return args, coqpit_overrides + + def init_training( + self, args: TrainingArgs, coqpit_overrides: Dict, config: Coqpit = None + ): # pylint: disable=no-self-use + """Initialize training and update model configs from command line arguments. + + Args: + args (argparse.Namespace or dict like): Parsed input arguments. + config_overrides (argparse.Namespace or dict like): Parsed config overriding arguments. + config (Coqpit): Model config. If none, it is generated from `args`. Defaults to None. + + Returns: + c (TTS.utils.io.AttrDict): Config paramaters. + """ + # set arguments for continuing training + if args.continue_path: + experiment_path = args.continue_path + args.config_path = os.path.join(args.continue_path, "config.json") + args.restore_path, best_model = get_last_checkpoint(args.continue_path) + if not args.best_path: + args.best_path = best_model + + # override config values from command-line args + # TODO: Maybe it is better to do it outside + if len(coqpit_overrides) > 0: + config.parse_known_args(coqpit_overrides, arg_prefix="coqpit", relaxed_parser=True) + experiment_path = args.continue_path + + # update the config.json fields and copy it to the output folder + if args.rank == 0: + new_fields = {} + if args.restore_path: + new_fields["restore_path"] = args.restore_path + new_fields["github_branch"] = get_git_branch() + copy_model_files(config, experiment_path, new_fields) + return config + + @staticmethod + def run_get_model(config: Coqpit, get_model: Callable) -> nn.Module: + """Run the `get_model` function and return the model. Args: config (Coqpit): Model config. @@ -300,12 +384,22 @@ class Trainer: Returns: nn.Module: initialized model. """ - try: - model = setup_vocoder_model(config) - except ModuleNotFoundError: - model = setup_tts_model(config) + if len(signature(get_model).sig.parameters) == 1: + model = get_model(config) + else: + model = get_model() return model + @staticmethod + def run_get_data_samples(config: Coqpit, get_data_samples: Callable) -> nn.Module: + if callable(get_data_samples): + if len(signature(get_data_samples).sig.parameters) == 1: + train_samples, eval_samples = get_data_samples(config) + else: + train_samples, eval_samples = get_data_samples() + return train_samples, eval_samples + return None, None + def restore_model( self, config: Coqpit, @@ -366,11 +460,15 @@ class Trainer: torch.cuda.empty_cache() return model, optimizer, scaler, restore_step + ######################### + # DATA LOADING FUNCTIONS + ######################### + def _get_loader( self, model: nn.Module, config: Coqpit, - ap: AudioProcessor, + assets: Dict, is_eval: bool, data_items: List, verbose: bool, @@ -379,14 +477,14 @@ class Trainer: if num_gpus > 1: if hasattr(model.module, "get_data_loader"): loader = model.module.get_data_loader( - config, ap, is_eval, data_items, verbose, num_gpus, self.args.rank + config, assets, is_eval, data_items, verbose, num_gpus, self.args.rank ) else: if hasattr(model, "get_data_loader"): - loader = model.get_data_loader(config, ap, is_eval, data_items, verbose, num_gpus) + loader = model.get_data_loader(config, assets, is_eval, data_items, verbose, num_gpus) return loader - def get_train_dataloader(self, ap: AudioProcessor, data_items: List, verbose: bool) -> DataLoader: + def get_train_dataloader(self, training_assets: Dict, data_items: List, verbose: bool) -> DataLoader: """Initialize and return a training data loader. Args: @@ -397,10 +495,10 @@ class Trainer: Returns: DataLoader: Initialized training data loader. """ - return self._get_loader(self.model, self.config, ap, False, data_items, verbose, self.num_gpus) + return self._get_loader(self.model, self.config, training_assets, False, data_items, verbose, self.num_gpus) - def get_eval_dataloader(self, ap: AudioProcessor, data_items: List, verbose: bool) -> DataLoader: - return self._get_loader(self.model, self.config, ap, True, data_items, verbose, self.num_gpus) + def get_eval_dataloader(self, training_assets: Dict, data_items: List, verbose: bool) -> DataLoader: + return self._get_loader(self.model, self.config, training_assets, True, data_items, verbose, self.num_gpus) def format_batch(self, batch: List) -> Dict: """Format the dataloader output and return a batch. @@ -420,6 +518,10 @@ class Trainer: batch[k] = to_cuda(v) return batch + ###################### + # TRAIN FUNCTIONS + ###################### + @staticmethod def master_params(optimizer: torch.optim.Optimizer): """Generator over parameters owned by the optimizer. @@ -516,10 +618,8 @@ class Trainer: else: grad_clip = 0.0 # meaning no gradient clipping - if grad_clip <= 0: - grad_norm = 0 - # optimizer step + grad_norm = 0 update_lr_scheduler = True if self.use_amp_scaler: if self.use_apex: @@ -527,31 +627,29 @@ class Trainer: # https://nvidia.github.io/apex/advanced.html?highlight=accumulate#backward-passes-with-multiple-optimizers with amp.scale_loss(loss_dict["loss"], optimizer) as scaled_loss: scaled_loss.backward() - grad_norm = torch.nn.utils.clip_grad_norm_( - amp.master_params(optimizer), grad_clip, error_if_nonfinite=False - ) + grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), grad_clip) else: # model optimizer step in mixed precision mode scaler.scale(loss_dict["loss"]).backward() if grad_clip > 0: scaler.unscale_(optimizer) - grad_norm = torch.nn.utils.clip_grad_norm_( - self.master_params(optimizer), grad_clip, error_if_nonfinite=False - ) - # pytorch skips the step when the norm is 0. So ignore the norm value when it is NaN - if torch.isnan(grad_norm) or torch.isinf(grad_norm): - grad_norm = 0 + grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params(optimizer), grad_clip) scale_prev = scaler.get_scale() scaler.step(optimizer) scaler.update() update_lr_scheduler = scale_prev <= scaler.get_scale() + loss_dict["amp_scaler"] = scaler.get_scale() # for logging else: # main model optimizer step loss_dict["loss"].backward() if grad_clip > 0: - grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip, error_if_nonfinite=False) + grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) optimizer.step() + # pytorch skips the step when the norm is 0. So ignore the norm value when it is NaN + if isinstance(grad_norm, torch.Tensor) and (torch.isnan(grad_norm) or torch.isinf(grad_norm)): + grad_norm = 0 + step_time = time.time() - step_start_time # setup lr @@ -567,24 +665,6 @@ class Trainer: loss_dict["grad_norm"] = grad_norm return outputs, loss_dict, step_time - @staticmethod - def _detach_loss_dict(loss_dict: Dict) -> Dict: - """Detach loss values from autograp. - - Args: - loss_dict (Dict): losses. - - Returns: - Dict: losses detached from autograph. - """ - loss_dict_detached = {} - for key, value in loss_dict.items(): - if isinstance(value, (int, float)): - loss_dict_detached[key] = value - else: - loss_dict_detached[key] = value.item() - return loss_dict_detached - def train_step(self, batch: Dict, batch_n_steps: int, step: int, loader_start_time: float) -> Tuple[Dict, Dict]: """Perform a training step on a batch of inputs and log the process. @@ -700,15 +780,14 @@ class Trainer: self.dashboard_logger.log_artifact(self.output_path, "checkpoint", "model", aliases) # training visualizations - figures, audios = None, None if hasattr(self.model, "module") and hasattr(self.model.module, "train_log"): - figures, audios = self.model.module.train_log(self.ap, batch, outputs) + self.model.module.train_log( + batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done + ) elif hasattr(self.model, "train_log"): - figures, audios = self.model.train_log(self.ap, batch, outputs) - if figures is not None: - self.dashboard_logger.train_figures(self.total_steps_done, figures) - if audios is not None: - self.dashboard_logger.train_audios(self.total_steps_done, audios, self.ap.sample_rate) + self.model.train_log( + batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done + ) self.dashboard_logger.flush() @@ -718,11 +797,13 @@ class Trainer: def train_epoch(self) -> None: """Main entry point for the training loop. Run training on the all training samples.""" + # initialize the data loader self.train_loader = self.get_train_dataloader( - self.ap, - self.data_train, + self.training_assets, + self.train_samples, verbose=True, ) + # set model to training mode if self.num_gpus > 1: self.model.module.train() else: @@ -734,11 +815,12 @@ class Trainer: batch_num_steps = int(len(self.train_loader.dataset) / self.config.batch_size) self.c_logger.print_train_start() loader_start_time = time.time() + # iterate over the training samples for cur_step, batch in enumerate(self.train_loader): _, _ = self.train_step(batch, batch_num_steps, cur_step, loader_start_time) loader_start_time = time.time() epoch_time = time.time() - epoch_start_time - # Plot self.epochs_done Stats + # plot self.epochs_done Stats if self.args.rank == 0: epoch_stats = {"epoch_time": epoch_time} epoch_stats.update(self.keep_avg_train.avg_values) @@ -754,6 +836,10 @@ class Trainer: else: self.scheduler.step() + ####################### + # EVAL FUNCTIONS + ####################### + @staticmethod def _model_eval_step( batch: Dict, model: nn.Module, criterion: nn.Module, optimizer_idx: int = None @@ -803,7 +889,7 @@ class Trainer: loss_dict_new[f"loss_{idx}"] = loss_dict_new.pop("loss") loss_dict.update(loss_dict_new) - loss_dict = self._detach_loss_dict(loss_dict) + loss_dict = self._detach_loss_dict(loss_dict) # update avg stats update_eval_values = {} @@ -819,8 +905,8 @@ class Trainer: """Main entry point for the evaluation loop. Run evaluation on the all validation samples.""" self.eval_loader = ( self.get_eval_dataloader( - self.ap, - self.data_eval, + self.training_assets, + self.eval_samples, verbose=True, ) if self.config.run_eval @@ -840,15 +926,12 @@ class Trainer: loader_start_time = time.time() # plot epoch stats, artifacts and figures if self.args.rank == 0: - figures, audios = None, None if hasattr(self.model, "module") and hasattr(self.model.module, "eval_log"): - figures, audios = self.model.module.eval_log(self.ap, batch, outputs) + self.model.module.eval_log( + batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done + ) elif hasattr(self.model, "eval_log"): - figures, audios = self.model.eval_log(self.ap, batch, outputs) - if figures is not None: - self.dashboard_logger.eval_figures(self.total_steps_done, figures) - if audios is not None: - self.dashboard_logger.eval_audios(self.total_steps_done, audios, self.ap.sample_rate) + self.model.eval_log(batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done) self.dashboard_logger.eval_stats(self.total_steps_done, self.keep_avg_eval.avg_values) def test_run(self) -> None: @@ -857,22 +940,22 @@ class Trainer: if hasattr(self.model, "test_run") or (self.num_gpus > 1 and hasattr(self.model.module, "test_run")): if self.eval_loader is None: self.eval_loader = self.get_eval_dataloader( - self.ap, - self.data_eval, + self.training_assets, + self.eval_samples, verbose=True, ) if hasattr(self.eval_loader.dataset, "load_test_samples"): samples = self.eval_loader.dataset.load_test_samples(1) if self.num_gpus > 1: - figures, audios = self.model.module.test_run(self.ap, samples, None) + figures, audios = self.model.module.test_run(self.training_assets, samples, None) else: - figures, audios = self.model.test_run(self.ap, samples, None) + figures, audios = self.model.test_run(self.training_assets, samples, None) else: if self.num_gpus > 1: - figures, audios = self.model.module.test_run(self.ap) + figures, audios = self.model.module.test_run(self.training_assets) else: - figures, audios = self.model.test_run(self.ap) + figures, audios = self.model.test_run(self.training_assets) self.dashboard_logger.test_audios(self.total_steps_done, audios, self.config.audio["sample_rate"]) self.dashboard_logger.test_figures(self.total_steps_done, figures) @@ -886,6 +969,10 @@ class Trainer: self.best_loss = ch["model_loss"] print(f" > Starting with loaded last best loss {self.best_loss}.") + ################################### + # FIT FUNCTIONS + ################################### + def _fit(self) -> None: """🏃 train -> evaluate -> test for the number of epochs.""" self._restore_best_loss() @@ -901,7 +988,8 @@ class Trainer: self.keep_avg_eval = KeepAverage() if self.config.run_eval else None self.epochs_done = epoch self.c_logger.print_epoch_start(epoch, self.config.epochs, self.output_path) - self.train_epoch() + if not self.args.skip_train_epoch: + self.train_epoch() if self.config.run_eval: self.eval_epoch() if epoch >= self.config.test_delay_epochs and self.args.rank <= 0: @@ -939,24 +1027,6 @@ class Trainer: traceback.print_exc() sys.exit(1) - def _pick_target_avg_loss(self, keep_avg_target: KeepAverage) -> Dict: - """Pick the target loss to compare models""" - target_avg_loss = None - - # return if target loss defined in the model config - if "target_loss" in self.config and self.config.target_loss: - return keep_avg_target[f"avg_{self.config.target_loss}"] - - # take the average of loss_{optimizer_idx} as the target loss when there are multiple optimizers - if isinstance(self.optimizer, list): - target_avg_loss = 0 - for idx in range(len(self.optimizer)): - target_avg_loss += keep_avg_target[f"avg_loss_{idx}"] - target_avg_loss /= len(self.optimizer) - else: - target_avg_loss = keep_avg_target["avg_loss"] - return target_avg_loss - def save_best_model(self) -> None: """Save the best model. It only saves if the current target loss is smaller then the previous.""" @@ -978,35 +1048,9 @@ class Trainer: keep_after=self.config.keep_after, ) - def _setup_logger_config(self, log_file: str) -> None: - """Write log strings to a file and print logs to the terminal. - TODO: Causes formatting issues in pdb debugging.""" - - class Logger(object): - def __init__(self, print_to_terminal=True): - self.print_to_terminal = print_to_terminal - self.terminal = sys.stdout - self.log_file = log_file - - def write(self, message): - if self.print_to_terminal: - self.terminal.write(message) - with open(self.log_file, "a", encoding="utf-8") as f: - f.write(message) - - def flush(self): - # this flush method is needed for python 3 compatibility. - # this handles the flush command by doing nothing. - # you might want to specify some extra behavior here. - pass - - # don't let processes rank > 0 write to the terminal - sys.stdout = Logger(self.args.rank == 0) - - @staticmethod - def _is_apex_available() -> bool: - """Check if Nvidia's APEX is available.""" - return importlib.util.find_spec("apex") is not None + ##################### + # GET FUNCTIONS + ##################### @staticmethod def get_optimizer(model: nn.Module, config: Coqpit) -> Union[torch.optim.Optimizer, List]: @@ -1084,154 +1128,72 @@ class Trainer: criterion = model.get_criterion() return criterion + #################### + # HELPER FUNCTIONS + #################### -def getarguments(): - train_config = TrainingArgs() - parser = train_config.init_argparse(arg_prefix="") - return parser + @staticmethod + def _detach_loss_dict(loss_dict: Dict) -> Dict: + """Detach loss values from autograp. + Args: + loss_dict (Dict): losses. -def get_last_checkpoint(path: str) -> Tuple[str, str]: - """Get latest checkpoint or/and best model in path. + Returns: + Dict: losses detached from autograph. + """ + loss_dict_detached = {} + for key, value in loss_dict.items(): + if isinstance(value, (int, float)): + loss_dict_detached[key] = value + else: + loss_dict_detached[key] = value.detach().item() + return loss_dict_detached - It is based on globbing for `*.pth.tar` and the RegEx - `(checkpoint|best_model)_([0-9]+)`. + def _pick_target_avg_loss(self, keep_avg_target: KeepAverage) -> Dict: + """Pick the target loss to compare models""" + target_avg_loss = None - Args: - path: Path to files to be compared. + # return if target loss defined in the model config + if "target_loss" in self.config and self.config.target_loss: + return keep_avg_target[f"avg_{self.config.target_loss}"] - Raises: - ValueError: If no checkpoint or best_model files are found. - - Returns: - Path to the last checkpoint - Path to best checkpoint - """ - fs = fsspec.get_mapper(path).fs - file_names = fs.glob(os.path.join(path, "*.pth.tar")) - scheme = urlparse(path).scheme - if scheme: # scheme is not preserved in fs.glob, add it back - file_names = [scheme + "://" + file_name for file_name in file_names] - last_models = {} - last_model_nums = {} - for key in ["checkpoint", "best_model"]: - last_model_num = None - last_model = None - # pass all the checkpoint files and find - # the one with the largest model number suffix. - for file_name in file_names: - match = re.search(f"{key}_([0-9]+)", file_name) - if match is not None: - model_num = int(match.groups()[0]) - if last_model_num is None or model_num > last_model_num: - last_model_num = model_num - last_model = file_name - - # if there is no checkpoint found above - # find the checkpoint with the latest - # modification date. - key_file_names = [fn for fn in file_names if key in fn] - if last_model is None and len(key_file_names) > 0: - last_model = max(key_file_names, key=os.path.getctime) - last_model_num = load_fsspec(last_model)["step"] - - if last_model is not None: - last_models[key] = last_model - last_model_nums[key] = last_model_num - - # check what models were found - if not last_models: - raise ValueError(f"No models found in continue path {path}!") - if "checkpoint" not in last_models: # no checkpoint just best model - last_models["checkpoint"] = last_models["best_model"] - elif "best_model" not in last_models: # no best model - # this shouldn't happen, but let's handle it just in case - last_models["best_model"] = last_models["checkpoint"] - # finally check if last best model is more recent than checkpoint - elif last_model_nums["best_model"] > last_model_nums["checkpoint"]: - last_models["checkpoint"] = last_models["best_model"] - - return last_models["checkpoint"], last_models["best_model"] - - -def process_args(args, config=None): - """Process parsed comand line arguments and initialize the config if not provided. - - Args: - args (argparse.Namespace or dict like): Parsed input arguments. - config (Coqpit): Model config. If none, it is generated from `args`. Defaults to None. - - Returns: - c (TTS.utils.io.AttrDict): Config paramaters. - out_path (str): Path to save models and logging. - audio_path (str): Path to save generated test audios. - c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does - logging to the console. - - dashboard_logger (WandbLogger or TensorboardLogger): Class that does the dashboard Logging - - TODO: - - Interactive config definition. - """ - if isinstance(args, tuple): - args, coqpit_overrides = args - if args.continue_path: - # continue a previous training from its output folder - experiment_path = args.continue_path - args.config_path = os.path.join(args.continue_path, "config.json") - args.restore_path, best_model = get_last_checkpoint(args.continue_path) - if not args.best_path: - args.best_path = best_model - # init config if not already defined - if config is None: - if args.config_path: - # init from a file - config = load_config(args.config_path) + # take the average of loss_{optimizer_idx} as the target loss when there are multiple optimizers + if isinstance(self.optimizer, list): + target_avg_loss = 0 + for idx in range(len(self.optimizer)): + target_avg_loss += keep_avg_target[f"avg_loss_{idx}"] + target_avg_loss /= len(self.optimizer) else: - # init from console args - from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel + target_avg_loss = keep_avg_target["avg_loss"] + return target_avg_loss - config_base = BaseTrainingConfig() - config_base.parse_known_args(coqpit_overrides) - config = register_config(config_base.model)() - # override values from command-line args - config.parse_known_args(coqpit_overrides, relaxed_parser=True) - experiment_path = args.continue_path - if not experiment_path: - experiment_path = get_experiment_folder_path(config.output_path, config.run_name) - audio_path = os.path.join(experiment_path, "test_audios") - config.output_log_path = experiment_path - # setup rank 0 process in distributed training - dashboard_logger = None - if args.rank == 0: - new_fields = {} - if args.restore_path: - new_fields["restore_path"] = args.restore_path - new_fields["github_branch"] = get_git_branch() - # if model characters are not set in the config file - # save the default set to the config file for future - # compatibility. - if config.has("characters") and config.characters is None: - used_characters = parse_symbols() - new_fields["characters"] = used_characters - copy_model_files(config, experiment_path, new_fields) - dashboard_logger = init_dashboard_logger(config) - c_logger = ConsoleLogger() - return config, experiment_path, audio_path, c_logger, dashboard_logger + def _setup_logger_config(self, log_file: str) -> None: + """Write log strings to a file and print logs to the terminal. + TODO: Causes formatting issues in pdb debugging.""" + class Logger(object): + def __init__(self, print_to_terminal=True): + self.print_to_terminal = print_to_terminal + self.terminal = sys.stdout + self.log_file = log_file -def init_arguments(): - train_config = TrainingArgs() - parser = train_config.init_argparse(arg_prefix="") - return parser + def write(self, message): + if self.print_to_terminal: + self.terminal.write(message) + with open(self.log_file, "a", encoding="utf-8") as f: + f.write(message) + def flush(self): + # this flush method is needed for python 3 compatibility. + # this handles the flush command by doing nothing. + # you might want to specify some extra behavior here. + pass -def init_training(argv: Union[List, Coqpit], config: Coqpit = None): - """Initialization of a training run.""" - if isinstance(argv, Coqpit): - parser = argv.init_argparse(arg_prefix="") - else: - parser = init_arguments() - args = parser.parse_known_args() - config, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = process_args(args, config) - return args[0], config, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger + # don't let processes rank > 0 write to the terminal + sys.stdout = Logger(self.args.rank == 0) + + @staticmethod + def _is_apex_available() -> bool: + """Check if Nvidia's APEX is available.""" + return importlib.util.find_spec("apex") is not None diff --git a/TTS/tts/configs/__init__.py b/TTS/tts/configs/__init__.py index 5ad4fe8c..3146ac1c 100644 --- a/TTS/tts/configs/__init__.py +++ b/TTS/tts/configs/__init__.py @@ -3,15 +3,15 @@ import os from inspect import isclass # import all files under configs/ -configs_dir = os.path.dirname(__file__) -for file in os.listdir(configs_dir): - path = os.path.join(configs_dir, file) - if not file.startswith("_") and not file.startswith(".") and (file.endswith(".py") or os.path.isdir(path)): - config_name = file[: file.find(".py")] if file.endswith(".py") else file - module = importlib.import_module("TTS.tts.configs." + config_name) - for attribute_name in dir(module): - attribute = getattr(module, attribute_name) +# configs_dir = os.path.dirname(__file__) +# for file in os.listdir(configs_dir): +# path = os.path.join(configs_dir, file) +# if not file.startswith("_") and not file.startswith(".") and (file.endswith(".py") or os.path.isdir(path)): +# config_name = file[: file.find(".py")] if file.endswith(".py") else file +# module = importlib.import_module("TTS.tts.configs." + config_name) +# for attribute_name in dir(module): +# attribute = getattr(module, attribute_name) - if isclass(attribute): - # Add the class to this package's variables - globals()[attribute_name] = attribute +# if isclass(attribute): +# # Add the class to this package's variables +# globals()[attribute_name] = attribute diff --git a/TTS/tts/configs/align_tts_config.py b/TTS/tts/configs/align_tts_config.py index 837cd519..317a01af 100644 --- a/TTS/tts/configs/align_tts_config.py +++ b/TTS/tts/configs/align_tts_config.py @@ -10,7 +10,7 @@ class AlignTTSConfig(BaseTTSConfig): """Defines parameters for AlignTTS model. Example: - >>> from TTS.tts.configs import AlignTTSConfig + >>> from TTS.tts.configs.align_tts_config import AlignTTSConfig >>> config = AlignTTSConfig() Args: diff --git a/TTS/tts/configs/fast_pitch_config.py b/TTS/tts/configs/fast_pitch_config.py index 668ea227..8f063102 100644 --- a/TTS/tts/configs/fast_pitch_config.py +++ b/TTS/tts/configs/fast_pitch_config.py @@ -11,7 +11,7 @@ class FastPitchConfig(BaseTTSConfig): Example: - >>> from TTS.tts.configs import FastPitchConfig + >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig >>> config = FastPitchConfig() Args: @@ -30,6 +30,10 @@ class FastPitchConfig(BaseTTSConfig): Activation Normalization that pre-computes normalization stats at the beginning and use the same values for the rest. Defaults to 10. + speakers_file (str): + Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to + speaker names. Defaults to `None`. + use_speaker_embedding (bool): enable / disable using speaker embeddings for multi-speaker models. If set True, the model is in the multi-speaker mode. Defaults to False. @@ -105,6 +109,8 @@ class FastPitchConfig(BaseTTSConfig): model_args: ForwardTTSArgs = ForwardTTSArgs() # multi-speaker settings + num_speakers: int = 0 + speakers_file: str = None use_speaker_embedding: bool = False use_d_vector_file: bool = False d_vector_file: str = False @@ -149,3 +155,22 @@ class FastPitchConfig(BaseTTSConfig): "Prior to November 22, 1963.", ] ) + + def __post_init__(self): + # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there. + if self.num_speakers > 0: + self.model_args.num_speakers = self.num_speakers + + # speaker embedding settings + if self.use_speaker_embedding: + self.model_args.use_speaker_embedding = True + if self.speakers_file: + self.model_args.speakers_file = self.speakers_file + + # d-vector settings + if self.use_d_vector_file: + self.model_args.use_d_vector_file = True + if self.d_vector_dim is not None and self.d_vector_dim > 0: + self.model_args.d_vector_dim = self.d_vector_dim + if self.d_vector_file: + self.model_args.d_vector_file = self.d_vector_file diff --git a/TTS/tts/configs/fast_speech_config.py b/TTS/tts/configs/fast_speech_config.py index bba47bb3..040a8910 100644 --- a/TTS/tts/configs/fast_speech_config.py +++ b/TTS/tts/configs/fast_speech_config.py @@ -11,7 +11,7 @@ class FastSpeechConfig(BaseTTSConfig): Example: - >>> from TTS.tts.configs import FastSpeechConfig + >>> from TTS.tts.configs.fast_speech_config import FastSpeechConfig >>> config = FastSpeechConfig() Args: @@ -30,6 +30,11 @@ class FastSpeechConfig(BaseTTSConfig): Activation Normalization that pre-computes normalization stats at the beginning and use the same values for the rest. Defaults to 10. + speakers_file (str): + Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to + speaker names. Defaults to `None`. + + use_speaker_embedding (bool): enable / disable using speaker embeddings for multi-speaker models. If set True, the model is in the multi-speaker mode. Defaults to False. @@ -105,6 +110,7 @@ class FastSpeechConfig(BaseTTSConfig): model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=False) # multi-speaker settings + speakers_file: str = None use_speaker_embedding: bool = False use_d_vector_file: bool = False d_vector_file: str = False @@ -149,3 +155,22 @@ class FastSpeechConfig(BaseTTSConfig): "Prior to November 22, 1963.", ] ) + + def __post_init__(self): + # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there. + if self.num_speakers > 0: + self.model_args.num_speakers = self.num_speakers + + # speaker embedding settings + if self.use_speaker_embedding: + self.model_args.use_speaker_embedding = True + if self.speakers_file: + self.model_args.speakers_file = self.speakers_file + + # d-vector settings + if self.use_d_vector_file: + self.model_args.use_d_vector_file = True + if self.d_vector_dim is not None and self.d_vector_dim > 0: + self.model_args.d_vector_dim = self.d_vector_dim + if self.d_vector_file: + self.model_args.d_vector_file = self.d_vector_file diff --git a/TTS/tts/configs/glow_tts_config.py b/TTS/tts/configs/glow_tts_config.py index 97fd3577..ce8eee6d 100644 --- a/TTS/tts/configs/glow_tts_config.py +++ b/TTS/tts/configs/glow_tts_config.py @@ -10,7 +10,7 @@ class GlowTTSConfig(BaseTTSConfig): Example: - >>> from TTS.tts.configs import GlowTTSConfig + >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig >>> config = GlowTTSConfig() Args: diff --git a/TTS/tts/configs/shared_configs.py b/TTS/tts/configs/shared_configs.py index e208c16c..60ef7276 100644 --- a/TTS/tts/configs/shared_configs.py +++ b/TTS/tts/configs/shared_configs.py @@ -218,7 +218,3 @@ class BaseTTSConfig(BaseTrainingConfig): lr_scheduler_params: dict = field(default_factory=lambda: {}) # testing test_sentences: List[str] = field(default_factory=lambda: []) - # multi-speaker - use_speaker_embedding: bool = False - use_d_vector_file: bool = False - d_vector_dim: int = 0 diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py index ba561c89..ea6866ed 100644 --- a/TTS/tts/configs/speedy_speech_config.py +++ b/TTS/tts/configs/speedy_speech_config.py @@ -11,7 +11,7 @@ class SpeedySpeechConfig(BaseTTSConfig): Example: - >>> from TTS.tts.configs import SpeedySpeechConfig + >>> from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig >>> config = SpeedySpeechConfig() Args: @@ -30,6 +30,10 @@ class SpeedySpeechConfig(BaseTTSConfig): Activation Normalization that pre-computes normalization stats at the beginning and use the same values for the rest. Defaults to 10. + speakers_file (str): + Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to + speaker names. Defaults to `None`. + use_speaker_embedding (bool): enable / disable using speaker embeddings for multi-speaker models. If set True, the model is in the multi-speaker mode. Defaults to False. @@ -117,12 +121,13 @@ class SpeedySpeechConfig(BaseTTSConfig): }, out_channels=80, hidden_channels=128, - num_speakers=0, positional_encoding=True, detach_duration_predictor=True, ) # multi-speaker settings + num_speakers: int = 0 + speakers_file: str = None use_speaker_embedding: bool = False use_d_vector_file: bool = False d_vector_file: str = False @@ -166,3 +171,22 @@ class SpeedySpeechConfig(BaseTTSConfig): "Prior to November 22, 1963.", ] ) + + def __post_init__(self): + # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there. + if self.num_speakers > 0: + self.model_args.num_speakers = self.num_speakers + + # speaker embedding settings + if self.use_speaker_embedding: + self.model_args.use_speaker_embedding = True + if self.speakers_file: + self.model_args.speakers_file = self.speakers_file + + # d-vector settings + if self.use_d_vector_file: + self.model_args.use_d_vector_file = True + if self.d_vector_dim is not None and self.d_vector_dim > 0: + self.model_args.d_vector_dim = self.d_vector_dim + if self.d_vector_file: + self.model_args.d_vector_file = self.d_vector_file diff --git a/TTS/tts/configs/tacotron2_config.py b/TTS/tts/configs/tacotron2_config.py index b622e640..95b65202 100644 --- a/TTS/tts/configs/tacotron2_config.py +++ b/TTS/tts/configs/tacotron2_config.py @@ -9,7 +9,7 @@ class Tacotron2Config(TacotronConfig): Example: - >>> from TTS.tts.configs import Tacotron2Config + >>> from TTS.tts.configs.tacotron2_config import Tacotron2Config >>> config = Tacotron2Config() Check `TacotronConfig` for argument descriptions. diff --git a/TTS/tts/configs/tacotron_config.py b/TTS/tts/configs/tacotron_config.py index 89fb8d81..d6edd267 100644 --- a/TTS/tts/configs/tacotron_config.py +++ b/TTS/tts/configs/tacotron_config.py @@ -10,7 +10,7 @@ class TacotronConfig(BaseTTSConfig): Example: - >>> from TTS.tts.configs import TacotronConfig + >>> from TTS.tts.configs.tacotron_config import TacotronConfig >>> config = TacotronConfig() Args: @@ -106,7 +106,7 @@ class TacotronConfig(BaseTTSConfig): Weight decay coefficient. Defaults to `1e-6`. grad_clip (float): Gradient clipping threshold. Defaults to `5`. - seq_len_notm (bool): + seq_len_norm (bool): enable / disable the sequnce length normalization in the loss functions. If set True, loss of a sample is divided by the sequence length. Defaults to False. loss_masking (bool): diff --git a/TTS/tts/configs/vits_config.py b/TTS/tts/configs/vits_config.py index 39479231..d490e6e6 100644 --- a/TTS/tts/configs/vits_config.py +++ b/TTS/tts/configs/vits_config.py @@ -90,7 +90,7 @@ class VitsConfig(BaseTTSConfig): Example: - >>> from TTS.tts.configs import VitsConfig + >>> from TTS.tts.configs.vits_config import VitsConfig >>> config = VitsConfig() """ @@ -139,3 +139,36 @@ class VitsConfig(BaseTTSConfig): "Prior to November 22, 1963.", ] ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: str = False + d_vector_dim: int = None + + def __post_init__(self): + # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there. + if self.num_speakers > 0: + self.model_args.num_speakers = self.num_speakers + + # speaker embedding settings + if self.use_speaker_embedding: + self.model_args.use_speaker_embedding = True + if self.speakers_file: + self.model_args.speakers_file = self.speakers_file + if self.speaker_embedding_channels: + self.model_args.speaker_embedding_channels = self.speaker_embedding_channels + + # d-vector settings + if self.use_d_vector_file: + self.model_args.use_d_vector_file = True + if self.d_vector_dim is not None and self.d_vector_dim > 0: + self.model_args.d_vector_dim = self.d_vector_dim + if self.d_vector_file: + self.model_args.d_vector_file = self.d_vector_file diff --git a/TTS/tts/datasets/__init__.py b/TTS/tts/datasets/__init__.py index c2e55038..78024936 100644 --- a/TTS/tts/datasets/__init__.py +++ b/TTS/tts/datasets/__init__.py @@ -1,15 +1,20 @@ import sys from collections import Counter from pathlib import Path -from typing import Dict, List, Tuple +from typing import Callable, Dict, List, Tuple, Union import numpy as np +from TTS.tts.datasets.dataset import * from TTS.tts.datasets.formatters import * -from TTS.tts.datasets.TTSDataset import TTSDataset def split_dataset(items): + """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training. + + Args: + items (List[List]): A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`. + """ speakers = [item[-1] for item in items] is_multi_speaker = len(set(speakers)) > 1 eval_split_size = min(500, int(len(items) * 0.01)) @@ -31,33 +36,47 @@ def split_dataset(items): return items[:eval_split_size], items[eval_split_size:] -def load_meta_data(datasets: List[Dict], eval_split=True) -> Tuple[List[List], List[List]]: - """Parse the dataset, load the samples as a list and load the attention alignments if provided. +def load_tts_samples( + datasets: Union[List[Dict], Dict], eval_split=True, formatter: Callable = None +) -> Tuple[List[List], List[List]]: + """Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided. + If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based + on the dataset name. Args: - datasets (List[Dict]): A list of dataset dictionaries or dataset configs. + datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are + in the list, they are all merged. + eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate an eval split automatically. Defaults to True. + formatter (Callable, optional): The preprocessing function to be applied to create the list of samples. It + must take the root_path and the meta_file name and return a list of samples in the format of + `[[audio_path, text, speaker_id], ...]]`. See the available formatters in `TTS.tts.dataset.formatter` as + example. Defaults to None. + Returns: Tuple[List[List], List[List]: training and evaluation splits of the dataset. """ meta_data_train_all = [] meta_data_eval_all = [] if eval_split else None + if not isinstance(datasets, list): + datasets = [datasets] for dataset in datasets: name = dataset["name"] root_path = dataset["path"] meta_file_train = dataset["meta_file_train"] meta_file_val = dataset["meta_file_val"] # setup the right data processor - preprocessor = _get_preprocessor_by_name(name) + if formatter is None: + formatter = _get_formatter_by_name(name) # load train set - meta_data_train = preprocessor(root_path, meta_file_train) + meta_data_train = formatter(root_path, meta_file_train) print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}") # load evaluation split if set if eval_split: if meta_file_val: - meta_data_eval = preprocessor(root_path, meta_file_val) + meta_data_eval = formatter(root_path, meta_file_val) else: meta_data_eval, meta_data_train = split_dataset(meta_data_train) meta_data_eval_all += meta_data_eval @@ -87,7 +106,7 @@ def load_attention_mask_meta_data(metafile_path): return meta_data -def _get_preprocessor_by_name(name): +def _get_formatter_by_name(name): """Returns the respective preprocessing function.""" thismodule = sys.modules[__name__] return getattr(thismodule, name.lower()) diff --git a/TTS/tts/datasets/TTSDataset.py b/TTS/tts/datasets/dataset.py similarity index 99% rename from TTS/tts/datasets/TTSDataset.py rename to TTS/tts/datasets/dataset.py index c81e0e6c..04314bab 100644 --- a/TTS/tts/datasets/TTSDataset.py +++ b/TTS/tts/datasets/dataset.py @@ -330,7 +330,7 @@ class TTSDataset(Dataset): if by_audio_len: lengths = [] for item in self.items: - lengths.append(os.path.getsize(item[1])) + lengths.append(os.path.getsize(item[1]) / 16 * 8) # assuming 16bit audio lengths = np.array(lengths) else: lengths = np.array([len(ins[0]) for ins in self.items]) @@ -419,6 +419,7 @@ class TTSDataset(Dataset): d_vectors = [self.d_vector_mapping[w]["embedding"] for w in wav_files_names] else: d_vectors = None + # get numerical speaker ids from speaker names if self.speaker_id_mapping: speaker_ids = [self.speaker_id_mapping[sn] for sn in batch["speaker_name"]] diff --git a/TTS/tts/datasets/formatters.py b/TTS/tts/datasets/formatters.py index eee407a8..dcd18740 100644 --- a/TTS/tts/datasets/formatters.py +++ b/TTS/tts/datasets/formatters.py @@ -308,14 +308,14 @@ def mls(root_path, meta_files=None): # ======================================== VOX CELEB =========================================== def voxceleb2(root_path, meta_file=None): """ - :param meta_file Used only for consistency with load_meta_data api + :param meta_file Used only for consistency with load_tts_samples api """ return _voxcel_x(root_path, meta_file, voxcel_idx="2") def voxceleb1(root_path, meta_file=None): """ - :param meta_file Used only for consistency with load_meta_data api + :param meta_file Used only for consistency with load_tts_samples api """ return _voxcel_x(root_path, meta_file, voxcel_idx="1") diff --git a/TTS/tts/layers/glow_tts/glow.py b/TTS/tts/layers/glow_tts/glow.py index 392447de..ff1b99e8 100644 --- a/TTS/tts/layers/glow_tts/glow.py +++ b/TTS/tts/layers/glow_tts/glow.py @@ -106,7 +106,6 @@ class InvConvNear(nn.Module): - x: :math:`[B, C, T]` - x_mask: :math:`[B, 1, T]` """ - b, c, t = x.size() assert c % self.num_splits == 0 if x_mask is None: diff --git a/TTS/tts/layers/losses.py b/TTS/tts/layers/losses.py index f465c638..0ea342e8 100644 --- a/TTS/tts/layers/losses.py +++ b/TTS/tts/layers/losses.py @@ -410,11 +410,6 @@ class TacotronLoss(torch.nn.Module): return_dict["postnet_ssim_loss"] = postnet_ssim_loss return_dict["loss"] = loss - - # check if any loss is NaN - for key, loss in return_dict.items(): - if torch.isnan(loss): - raise RuntimeError(f" [!] NaN loss with {key}.") return return_dict diff --git a/TTS/tts/layers/tacotron/attentions.py b/TTS/tts/layers/tacotron/attentions.py index a01ccc49..8c30a00a 100644 --- a/TTS/tts/layers/tacotron/attentions.py +++ b/TTS/tts/layers/tacotron/attentions.py @@ -126,27 +126,24 @@ class GravesAttention(nn.Module): class OriginalAttention(nn.Module): - """Bahdanau Attention with various optional modifications. Proposed below. + """Bahdanau Attention with various optional modifications. - Location sensitive attnetion: https://arxiv.org/abs/1712.05884 - Forward Attention: https://arxiv.org/abs/1807.06736 + state masking at inference - Using sigmoid instead of softmax normalization - Attention windowing at inference time Note: - Location Sensitive Attention is an attention mechanism that extends the additive attention mechanism - to use cumulative attention weights from previous decoder time steps as an additional feature. + Location Sensitive Attention extends the additive attention mechanism + to use cumulative attention weights from previous decoder time steps with the current time step features. - Forward attention considers only the alignment paths that satisfy the monotonic condition at each - decoder timestep. The modified attention probabilities at each timestep are computed recursively - using a forward algorithm. + Forward attention computes most probable monotonic alignment. The modified attention probabilities at each + timestep are computed recursively by the forward algorithm. - Transition agent for forward attention is further proposed, which helps the attention mechanism - to make decisions whether to move forward or stay at each decoder timestep. - - Attention windowing applies a sliding windows to time steps of the input tensor centering at the last - time step with the largest attention weight. It is especially useful at inference to keep the attention - alignment diagonal. + Transition agent in the forward attention explicitly gates the attention mechanism whether to move forward or + stay at each decoder timestep. + Attention windowing is a inductive prior that prevents the model from attending to previous and future timesteps + beyond a certain window. Args: query_dim (int): number of channels in the query tensor. diff --git a/TTS/tts/models/__init__.py b/TTS/tts/models/__init__.py index 1236fa76..780f22cd 100644 --- a/TTS/tts/models/__init__.py +++ b/TTS/tts/models/__init__.py @@ -2,7 +2,7 @@ from TTS.tts.utils.text.symbols import make_symbols, parse_symbols from TTS.utils.generic_utils import find_module -def setup_model(config): +def setup_model(config, speaker_manager: "SpeakerManager" = None): print(" > Using model: {}".format(config.model)) # fetch the right model implementation. if "base_model" in config and config["base_model"] is not None: @@ -31,7 +31,7 @@ def setup_model(config): config.model_params.num_chars = num_chars if "model_args" in config: config.model_args.num_chars = num_chars - model = MyModel(config) + model = MyModel(config, speaker_manager=speaker_manager) return model diff --git a/TTS/tts/models/align_tts.py b/TTS/tts/models/align_tts.py index 78fbaeab..2fc00b0b 100644 --- a/TTS/tts/models/align_tts.py +++ b/TTS/tts/models/align_tts.py @@ -1,5 +1,4 @@ from dataclasses import dataclass, field -from typing import Dict, Tuple import torch from coqpit import Coqpit @@ -12,8 +11,8 @@ from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.pos_encoding import PositionalEncoding from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask +from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.visual import plot_alignment, plot_spectrogram -from TTS.utils.audio import AudioProcessor from TTS.utils.io import load_fsspec @@ -93,7 +92,7 @@ class AlignTTS(BaseTTS): differently based on your requirements using ```encoder_type``` and ```decoder_type``` parameters. Examples: - >>> from TTS.tts.configs import AlignTTSConfig + >>> from TTS.tts.configs.align_tts_config import AlignTTSConfig >>> config = AlignTTSConfig() >>> model = AlignTTS(config) @@ -101,9 +100,10 @@ class AlignTTS(BaseTTS): # pylint: disable=dangerous-default-value - def __init__(self, config: Coqpit): + def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None): - super().__init__() + super().__init__(config) + self.speaker_manager = speaker_manager self.config = config self.phase = -1 self.length_scale = ( @@ -360,9 +360,7 @@ class AlignTTS(BaseTTS): return outputs, loss_dict - def train_log( - self, ap: AudioProcessor, batch: dict, outputs: dict - ) -> Tuple[Dict, Dict]: # pylint: disable=no-self-use + def _create_logs(self, batch, outputs, ap): # pylint: disable=no-self-use model_outputs = outputs["model_outputs"] alignments = outputs["alignments"] mel_input = batch["mel_input"] @@ -381,11 +379,22 @@ class AlignTTS(BaseTTS): train_audio = ap.inv_melspectrogram(pred_spec.T) return figures, {"audio": train_audio} + def train_log( + self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int + ) -> None: # pylint: disable=no-self-use + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.train_figures(steps, figures) + logger.train_audios(steps, audios, ap.sample_rate) + def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) - def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): - return self.train_log(ap, batch, outputs) + def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.eval_figures(steps, figures) + logger.eval_audios(steps, audios, ap.sample_rate) def load_checkpoint( self, config, checkpoint_path, eval=False diff --git a/TTS/tts/models/base_tacotron.py b/TTS/tts/models/base_tacotron.py index 8cfd750d..ca8f3bb9 100644 --- a/TTS/tts/models/base_tacotron.py +++ b/TTS/tts/models/base_tacotron.py @@ -1,59 +1,26 @@ import copy from abc import abstractmethod -from dataclasses import dataclass -from typing import Dict, List +from typing import Dict import torch -from coqpit import MISSING, Coqpit +from coqpit import Coqpit from torch import nn from TTS.tts.layers.losses import TacotronLoss from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import sequence_mask -from TTS.tts.utils.speakers import SpeakerManager, get_speaker_manager -from TTS.tts.utils.text import make_symbols from TTS.utils.generic_utils import format_aux_input from TTS.utils.io import load_fsspec from TTS.utils.training import gradual_training_scheduler -@dataclass -class BaseTacotronArgs(Coqpit): - """TODO: update Tacotron configs using it""" - - num_chars: int = MISSING - num_speakers: int = MISSING - r: int = MISSING - out_channels: int = 80 - decoder_output_dim: int = 80 - attn_type: str = "original" - attn_win: bool = False - attn_norm: str = "softmax" - prenet_type: str = "original" - prenet_dropout: bool = True - prenet_dropout_at_inference: bool = False - forward_attn: bool = False - trans_agent: bool = False - forward_attn_mask: bool = False - location_attn: bool = True - attn_K: int = 5 - separate_stopnet: bool = True - bidirectional_decoder: bool = False - double_decoder_consistency: bool = False - ddc_r: int = None - encoder_in_features: int = 512 - decoder_in_features: int = 512 - d_vector_dim: int = None - use_gst: bool = False - gst: bool = None - gradual_training: bool = None - - class BaseTacotron(BaseTTS): - def __init__(self, config: Coqpit): - """Abstract Tacotron class""" - super().__init__() + """Base class shared by Tacotron and Tacotron2""" + def __init__(self, config: Coqpit): + super().__init__(config) + + # pass all config fields as class attributes for key in config: setattr(self, key, config[key]) @@ -78,6 +45,7 @@ class BaseTacotron(BaseTTS): @staticmethod def _format_aux_input(aux_input: Dict) -> Dict: + """Set missing fields to their default values""" if aux_input: return format_aux_input({"d_vectors": None, "speaker_ids": None}, aux_input) return None @@ -86,14 +54,12 @@ class BaseTacotron(BaseTTS): # INIT FUNCTIONS ############################# - def _init_states(self): - self.embedded_speakers = None - self.embedded_speakers_projected = None - def _init_backward_decoder(self): + """Init the backward decoder for Forward-Backward decoding.""" self.decoder_backward = copy.deepcopy(self.decoder) def _init_coarse_decoder(self): + """Init the coarse decoder for Double-Decoder Consistency.""" self.coarse_decoder = copy.deepcopy(self.decoder) self.coarse_decoder.r_init = self.ddc_r self.coarse_decoder.set_r(self.ddc_r) @@ -113,6 +79,13 @@ class BaseTacotron(BaseTTS): def load_checkpoint( self, config, checkpoint_path, eval=False ): # pylint: disable=unused-argument, redefined-builtin + """Load model checkpoint and set up internals. + + Args: + config (Coqpi): model configuration. + checkpoint_path (str): path to checkpoint file. + eval (bool): whether to load model for evaluation. + """ state = load_fsspec(checkpoint_path, map_location=torch.device("cpu")) self.load_state_dict(state["model"]) # TODO: set r in run-time by taking it from the new config @@ -131,61 +104,9 @@ class BaseTacotron(BaseTTS): assert not self.training def get_criterion(self) -> nn.Module: + """Get the model criterion used in training.""" return TacotronLoss(self.config) - @staticmethod - def get_characters(config: Coqpit) -> str: - # TODO: implement CharacterProcessor - if config.characters is not None: - symbols, phonemes = make_symbols(**config.characters) - else: - from TTS.tts.utils.text.symbols import ( # pylint: disable=import-outside-toplevel - parse_symbols, - phonemes, - symbols, - ) - - config.characters = parse_symbols() - model_characters = phonemes if config.use_phonemes else symbols - return model_characters, config - - @staticmethod - def get_speaker_manager(config: Coqpit, restore_path: str, data: List, out_path: str = None) -> SpeakerManager: - return get_speaker_manager(config, restore_path, data, out_path) - - def get_aux_input(self, **kwargs) -> Dict: - """Compute Tacotron's auxiliary inputs based on model config. - - speaker d_vector - - style wav for GST - - speaker ID for speaker embedding - """ - # setup speaker_id - if self.config.use_speaker_embedding: - speaker_id = kwargs.get("speaker_id", 0) - else: - speaker_id = None - # setup d_vector - d_vector = ( - self.speaker_manager.get_d_vectors_by_speaker(self.speaker_manager.speaker_names[0]) - if self.config.use_d_vector_file and self.config.use_speaker_embedding - else None - ) - # setup style_mel - if "style_wav" in kwargs: - style_wav = kwargs["style_wav"] - elif self.config.has("gst_style_input"): - style_wav = self.config.gst_style_input - else: - style_wav = None - if style_wav is None and "use_gst" in self.config and self.config.use_gst: - # inicialize GST with zero dict. - style_wav = {} - print("WARNING: You don't provided a gst style wav, for this reason we use a zero tensor!") - for i in range(self.config.gst["gst_num_style_tokens"]): - style_wav[str(i)] = 0 - aux_inputs = {"speaker_id": speaker_id, "style_wav": style_wav, "d_vector": d_vector} - return aux_inputs - ############################# # COMMON COMPUTE FUNCTIONS ############################# @@ -231,15 +152,6 @@ class BaseTacotron(BaseTTS): # EMBEDDING FUNCTIONS ############################# - def compute_speaker_embedding(self, speaker_ids): - """Compute speaker embedding vectors""" - if hasattr(self, "speaker_embedding") and speaker_ids is None: - raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided") - if hasattr(self, "speaker_embedding") and speaker_ids is not None: - self.embedded_speakers = self.speaker_embedding(speaker_ids).unsqueeze(1) - if hasattr(self, "speaker_project_mel") and speaker_ids is not None: - self.embedded_speakers_projected = self.speaker_project_mel(self.embedded_speakers).squeeze(1) - def compute_gst(self, inputs, style_input, speaker_embedding=None): """Compute global style token""" if isinstance(style_input, dict): diff --git a/TTS/tts/models/base_tts.py b/TTS/tts/models/base_tts.py index 06c7cb2b..b77c1e23 100644 --- a/TTS/tts/models/base_tts.py +++ b/TTS/tts/models/base_tts.py @@ -1,4 +1,5 @@ import os +import random from typing import Dict, List, Tuple import torch @@ -9,20 +10,20 @@ from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from TTS.model import BaseModel -from TTS.tts.datasets import TTSDataset +from TTS.tts.configs.shared_configs import CharactersConfig +from TTS.tts.datasets.dataset import TTSDataset from TTS.tts.utils.speakers import SpeakerManager, get_speaker_manager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.text import make_symbols from TTS.tts.utils.visual import plot_alignment, plot_spectrogram -from TTS.utils.audio import AudioProcessor # pylint: skip-file class BaseTTS(BaseModel): - """Abstract `tts` class. Every new `tts` model must inherit this. + """Base `tts` class. Every new `tts` model must inherit this. - It defines `tts` specific functions on top of `Model`. + It defines common `tts` specific functions on top of `Model` implementation. Notes on input/output tensor shapes: Any input or output tensor of the model must be shaped as @@ -32,6 +33,30 @@ class BaseTTS(BaseModel): - 1D tensors `batch x 1` """ + def _set_model_args(self, config: Coqpit): + """Setup model args based on the config type. + + If the config is for training with a name like "*Config", then the model args are embeded in the + config.model_args + + If the config is for the model with a name like "*Args", then we assign the directly. + """ + # don't use isintance not to import recursively + if "Config" in config.__class__.__name__: + if "characters" in config: + _, self.config, num_chars = self.get_characters(config) + self.config.num_chars = num_chars + if hasattr(self.config, "model_args"): + config.model_args.num_chars = num_chars + self.args = self.config.model_args + else: + self.config = config + self.args = config.model_args + elif "Args" in config.__class__.__name__: + self.args = config + else: + raise ValueError("config must be either a *Config or *Args") + @staticmethod def get_characters(config: Coqpit) -> str: # TODO: implement CharacterProcessor @@ -40,7 +65,7 @@ class BaseTTS(BaseModel): else: from TTS.tts.utils.text.symbols import parse_symbols, phonemes, symbols - config.characters = parse_symbols() + config.characters = CharactersConfig(**parse_symbols()) model_characters = phonemes if config.use_phonemes else symbols num_chars = len(model_characters) + getattr(config, "add_blank", False) return model_characters, config, num_chars @@ -48,35 +73,18 @@ class BaseTTS(BaseModel): def get_speaker_manager(config: Coqpit, restore_path: str, data: List, out_path: str = None) -> SpeakerManager: return get_speaker_manager(config, restore_path, data, out_path) - def init_multispeaker(self, config: Coqpit, data: List = None): - """Initialize a speaker embedding layer if needen and define expected embedding channel size for defining - `in_channels` size of the connected layers. - - This implementation yields 3 possible outcomes: - - 1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing. - 2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512. - 3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of - `config.d_vector_dim` or 512. - - You can override this function for new models.0 + def init_multispeaker(self, config: Coqpit): + """Init speaker embedding layer if `use_speaker_embedding` is True and set the expected speaker embedding + vector dimension in the network. If model uses d-vectors, then it only sets the expected dimension. Args: config (Coqpit): Model configuration. - data (List, optional): Dataset items to infer number of speakers. Defaults to None. """ - # init speaker manager - self.speaker_manager = get_speaker_manager(config, data=data) - - # set number of speakers - if num_speakers is set in config, use it, otherwise use speaker_manager - if data is not None or self.speaker_manager.speaker_ids: + # set number of speakers + if self.speaker_manager is not None: self.num_speakers = self.speaker_manager.num_speakers - else: - self.num_speakers = ( - config.num_speakers - if "num_speakers" in config and config.num_speakers != 0 - else self.speaker_manager.num_speakers - ) + elif hasattr(config, "num_speakers"): + self.num_speakers = config.num_speakers # set ultimate speaker embedding size if config.use_speaker_embedding or config.use_d_vector_file: @@ -85,13 +93,10 @@ class BaseTTS(BaseModel): ) # init speaker embedding layer if config.use_speaker_embedding and not config.use_d_vector_file: + print(" > Init speaker_embedding layer.") self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim) self.speaker_embedding.weight.data.normal_(0, 0.3) - def get_aux_input(self, **kwargs) -> Dict: - """Prepare and return `aux_input` used by `forward()`""" - return {"speaker_id": None, "style_wav": None, "d_vector": None} - def format_batch(self, batch: Dict) -> Dict: """Generic batch formatting for `TTSDataset`. @@ -169,7 +174,7 @@ class BaseTTS(BaseModel): def get_data_loader( self, config: Coqpit, - ap: AudioProcessor, + assets: Dict, is_eval: bool, data_items: List, verbose: bool, @@ -179,14 +184,12 @@ class BaseTTS(BaseModel): if is_eval and not config.run_eval: loader = None else: + ap = assets["audio_processor"] + # setup multi-speaker attributes - if hasattr(self, "speaker_manager"): + if hasattr(self, "speaker_manager") and self.speaker_manager is not None: speaker_id_mapping = self.speaker_manager.speaker_ids if config.use_speaker_embedding else None - d_vector_mapping = ( - self.speaker_manager.d_vectors - if config.use_speaker_embedding and config.use_d_vector_file - else None - ) + d_vector_mapping = self.speaker_manager.d_vectors if config.use_d_vector_file else None else: speaker_id_mapping = None d_vector_mapping = None @@ -219,9 +222,7 @@ class BaseTTS(BaseModel): use_noise_augment=not is_eval, verbose=verbose, speaker_id_mapping=speaker_id_mapping, - d_vector_mapping=d_vector_mapping - if config.use_speaker_embedding and config.use_d_vector_file - else None, + d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None, ) # pre-compute phonemes @@ -280,19 +281,41 @@ class BaseTTS(BaseModel): ) return loader - def test_run(self, ap) -> Tuple[Dict, Dict]: + def _get_test_aux_input( + self, + ) -> Dict: + + d_vector = None + if self.config.use_d_vector_file: + d_vector = [self.speaker_manager.d_vectors[name]["embedding"] for name in self.speaker_manager.d_vectors] + d_vector = (random.sample(sorted(d_vector), 1),) + + aux_inputs = { + "speaker_id": None + if not self.config.use_speaker_embedding + else random.sample(sorted(self.speaker_manager.speaker_ids.values()), 1), + "d_vector": d_vector, + "style_wav": None, # TODO: handle GST style input + } + return aux_inputs + + def test_run(self, assets: Dict) -> Tuple[Dict, Dict]: """Generic test run for `tts` models used by `Trainer`. You can override this for a different behaviour. + Args: + assets (dict): A dict of training assets. For `tts` models, it must include `{'audio_processor': ap}`. + Returns: Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard. """ + ap = assets["audio_processor"] print(" | > Synthesizing test sentences.") test_audios = {} test_figures = {} test_sentences = self.config.test_sentences - aux_inputs = self.get_aux_input() + aux_inputs = self._get_test_aux_input() for idx, sen in enumerate(test_sentences): outputs_dict = synthesis( self, @@ -315,3 +338,17 @@ class BaseTTS(BaseModel): outputs_dict["outputs"]["alignments"], output_fig=False ) return test_figures, test_audios + + def on_init_start(self, trainer): + """Save the speaker.json at the beginning of the training. And update the config.json with the + speakers.json file path.""" + if self.speaker_manager is not None: + output_path = os.path.join(trainer.output_path, "speakers.json") + self.speaker_manager.save_speaker_ids_to_file(output_path) + trainer.config.speakers_file = output_path + # some models don't have `model_args` set + if hasattr(trainer.config, "model_args"): + trainer.config.model_args.speakers_file = output_path + trainer.config.save_json(os.path.join(trainer.output_path, "config.json")) + print(f" > `speakers.json` is saved to {output_path}.") + print(" > `speakers_file` is updated in the config.json.") diff --git a/TTS/tts/models/forward_tts.py b/TTS/tts/models/forward_tts.py index 9dce36fa..b2c41df5 100644 --- a/TTS/tts/models/forward_tts.py +++ b/TTS/tts/models/forward_tts.py @@ -13,8 +13,8 @@ from TTS.tts.layers.generic.pos_encoding import PositionalEncoding from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import average_over_durations, generate_path, maximum_path, sequence_mask +from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.visual import plot_alignment, plot_pitch, plot_spectrogram -from TTS.utils.audio import AudioProcessor @dataclass @@ -32,9 +32,6 @@ class ForwardTTSArgs(Coqpit): hidden_channels (int): Number of base hidden channels of the model. Defaults to 512. - num_speakers (int): - Number of speakers for the speaker embedding layer. Defaults to 0. - use_aligner (bool): Whether to use aligner network to learn the text to speech alignment or use pre-computed durations. If set False, durations should be computed by `TTS/bin/compute_attention_masks.py` and path to the @@ -87,12 +84,6 @@ class ForwardTTSArgs(Coqpit): decoder_params (str): Parameters of the decoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` - use_d_vetor (bool): - Whether to use precomputed d-vectors for multi-speaker training. Defaults to False. - - d_vector_dim (int): - Number of channels of the d-vectors. Defaults to 0. - detach_duration_predictor (bool): Detach the input to the duration predictor from the earlier computation graph so that the duraiton loss does not pass to the earlier layers. Defaults to True. @@ -100,12 +91,26 @@ class ForwardTTSArgs(Coqpit): max_duration (int): Maximum duration accepted by the model. Defaults to 75. + num_speakers (int): + Number of speakers for the speaker embedding layer. Defaults to 0. + + speakers_file (str): + Path to the speaker mapping file for the Speaker Manager. Defaults to None. + + speaker_embedding_channels (int): + Number of speaker embedding channels. Defaults to 256. + + use_d_vector_file (bool): + Enable/Disable the use of d-vectors for multi-speaker training. Defaults to False. + + d_vector_dim (int): + Number of d-vector channels. Defaults to 0. + """ num_chars: int = None out_channels: int = 80 hidden_channels: int = 384 - num_speakers: int = 0 use_aligner: bool = True use_pitch: bool = True pitch_predictor_hidden_channels: int = 256 @@ -126,10 +131,14 @@ class ForwardTTSArgs(Coqpit): decoder_params: dict = field( default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} ) - use_d_vector: bool = False - d_vector_dim: int = 0 detach_duration_predictor: bool = False max_duration: int = 75 + num_speakers: int = 1 + use_speaker_embedding: bool = False + speakers_file: str = None + use_d_vector_file: bool = False + d_vector_dim: int = None + d_vector_file: str = None class ForwardTTS(BaseTTS): @@ -151,6 +160,8 @@ class ForwardTTS(BaseTTS): Args: config (Coqpit): Model coqpit class. + speaker_manager (SpeakerManager): Speaker manager for multi-speaker training. Only used for multi-speaker models. + Defaults to None. Examples: >>> from TTS.tts.models.fast_pitch import ForwardTTS, ForwardTTSArgs @@ -159,26 +170,12 @@ class ForwardTTS(BaseTTS): """ # pylint: disable=dangerous-default-value - def __init__(self, config: Coqpit): + def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None): - super().__init__() + super().__init__(config) - # don't use isintance not to import recursively - if "Config" in config.__class__.__name__: - if "characters" in config: - # loading from FasrPitchConfig - _, self.config, num_chars = self.get_characters(config) - config.model_args.num_chars = num_chars - self.args = self.config.model_args - else: - # loading from ForwardTTSArgs - self.config = config - self.args = config.model_args - elif isinstance(config, ForwardTTSArgs): - self.args = config - self.config = config - else: - raise ValueError("config must be either a *Config or ForwardTTSArgs") + self.speaker_manager = speaker_manager + self.init_multispeaker(config) self.max_duration = self.args.max_duration self.use_aligner = self.args.use_aligner @@ -196,7 +193,7 @@ class ForwardTTS(BaseTTS): self.args.hidden_channels, self.args.encoder_type, self.args.encoder_params, - self.args.d_vector_dim, + self.embedded_speaker_dim, ) if self.args.positional_encoding: @@ -210,7 +207,7 @@ class ForwardTTS(BaseTTS): ) self.duration_predictor = DurationPredictor( - self.args.hidden_channels + self.args.d_vector_dim, + self.args.hidden_channels + self.embedded_speaker_dim, self.args.duration_predictor_hidden_channels, self.args.duration_predictor_kernel_size, self.args.duration_predictor_dropout_p, @@ -218,7 +215,7 @@ class ForwardTTS(BaseTTS): if self.args.use_pitch: self.pitch_predictor = DurationPredictor( - self.args.hidden_channels + self.args.d_vector_dim, + self.args.hidden_channels + self.embedded_speaker_dim, self.args.pitch_predictor_hidden_channels, self.args.pitch_predictor_kernel_size, self.args.pitch_predictor_dropout_p, @@ -230,19 +227,37 @@ class ForwardTTS(BaseTTS): padding=int((self.args.pitch_embedding_kernel_size - 1) / 2), ) - if self.args.num_speakers > 1 and not self.args.use_d_vector: - # speaker embedding layer - self.emb_g = nn.Embedding(self.args.num_speakers, self.args.d_vector_dim) - nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) - - if self.args.d_vector_dim > 0 and self.args.d_vector_dim != self.args.hidden_channels: - self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1) - if self.args.use_aligner: self.aligner = AlignmentNetwork( in_query_channels=self.args.out_channels, in_key_channels=self.args.hidden_channels ) + def init_multispeaker(self, config: Coqpit): + """Init for multi-speaker training. + + Args: + config (Coqpit): Model configuration. + """ + self.embedded_speaker_dim = 0 + # init speaker manager + if self.speaker_manager is None and (config.use_d_vector_file or config.use_speaker_embedding): + raise ValueError( + " > SpeakerManager is not provided. You must provide the SpeakerManager before initializing a multi-speaker model." + ) + # set number of speakers + if self.speaker_manager is not None: + self.num_speakers = self.speaker_manager.num_speakers + # init d-vector embedding + if config.use_d_vector_file: + self.embedded_speaker_dim = config.d_vector_dim + if self.args.d_vector_dim != self.args.hidden_channels: + self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1) + # init speaker embedding layer + if config.use_speaker_embedding and not config.use_d_vector_file: + print(" > Init speaker_embedding layer.") + self.emb_g = nn.Embedding(self.args.num_speakers, self.args.hidden_channels) + nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) + @staticmethod def generate_attn(dr, x_mask, y_mask=None): """Generate an attention mask from the durations. @@ -307,18 +322,6 @@ class ForwardTTS(BaseTTS): o_dr = torch.round(o_dr) return o_dr - @staticmethod - def _concat_speaker_embedding(o_en, g): - g_exp = g.expand(-1, -1, o_en.size(-1)) # [B, C, T_en] - o_en = torch.cat([o_en, g_exp], 1) - return o_en - - def _sum_speaker_embedding(self, x, g): - # project g to decoder dim. - if hasattr(self, "proj_g"): - g = self.proj_g(g) - return x + g - def _forward_encoder( self, x: torch.LongTensor, x_mask: torch.FloatTensor, g: torch.FloatTensor = None ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: @@ -327,7 +330,7 @@ class ForwardTTS(BaseTTS): 1. Embed speaker IDs if multi-speaker mode. 2. Embed character sequences. 3. Run the encoder network. - 4. Concat speaker embedding to the encoder output for the duration predictor. + 4. Sum encoder outputs and speaker embeddings Args: x (torch.LongTensor): Input sequence IDs. @@ -345,19 +348,18 @@ class ForwardTTS(BaseTTS): - g: :math:`(B, C)` """ if hasattr(self, "emb_g"): - g = nn.functional.normalize(self.emb_g(g)) # [B, C, 1] + g = self.emb_g(g) # [B, C, 1] if g is not None: g = g.unsqueeze(-1) # [B, T, C] x_emb = self.emb(x) # encoder pass o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask) - # speaker conditioning for duration predictor + # speaker conditioning + # TODO: try different ways of conditioning if g is not None: - o_en_dp = self._concat_speaker_embedding(o_en, g) - else: - o_en_dp = o_en - return o_en, o_en_dp, x_mask, g, x_emb + o_en = o_en + g + return o_en, x_mask, g, x_emb def _forward_decoder( self, @@ -391,9 +393,6 @@ class ForwardTTS(BaseTTS): # positional encoding if hasattr(self, "pos_encoder"): o_en_ex = self.pos_encoder(o_en_ex, y_mask) - # speaker embedding - if g is not None: - o_en_ex = self._sum_speaker_embedding(o_en_ex, g) # decoder pass o_de = self.decoder(o_en_ex, y_mask, g=g) return o_de.transpose(1, 2), attn.transpose(1, 2) @@ -475,6 +474,19 @@ class ForwardTTS(BaseTTS): alignment_soft = alignment_soft.squeeze(1).transpose(1, 2) return o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas + def _set_speaker_input(self, aux_input: Dict): + d_vectors = aux_input.get("d_vectors", None) + speaker_ids = aux_input.get("speaker_ids", None) + + if d_vectors is not None and speaker_ids is not None: + raise ValueError("[!] Cannot use d-vectors and speaker-ids together.") + + if speaker_ids is not None and not hasattr(self, "emb_g"): + raise ValueError("[!] Cannot use speaker-ids without enabling speaker embedding.") + + g = speaker_ids if speaker_ids is not None else d_vectors + return g + def forward( self, x: torch.LongTensor, @@ -505,17 +517,17 @@ class ForwardTTS(BaseTTS): - g: :math:`[B, C]` - pitch: :math:`[B, 1, T]` """ - g = aux_input["d_vectors"] if "d_vectors" in aux_input else None + g = self._set_speaker_input(aux_input) # compute sequence masks y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).float() x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).float() # encoder pass - o_en, o_en_dp, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g) + o_en, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g) # duration predictor pass if self.args.detach_duration_predictor: - o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask) + o_dr_log = self.duration_predictor(o_en.detach(), x_mask) else: - o_dr_log = self.duration_predictor(o_en_dp, x_mask) + o_dr_log = self.duration_predictor(o_en, x_mask) o_dr = torch.clamp(torch.exp(o_dr_log) - 1, 0, self.max_duration) # generate attn mask from predicted durations o_attn = self.generate_attn(o_dr.squeeze(1), x_mask) @@ -535,10 +547,12 @@ class ForwardTTS(BaseTTS): o_pitch = None avg_pitch = None if self.args.use_pitch: - o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en_dp, x_mask, pitch, dr) + o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en, x_mask, pitch, dr) o_en = o_en + o_pitch_emb # decoder pass - o_de, attn = self._forward_decoder(o_en, dr, x_mask, y_lengths, g=g) + o_de, attn = self._forward_decoder( + o_en, dr, x_mask, y_lengths, g=None + ) # TODO: maybe pass speaker embedding (g) too outputs = { "model_outputs": o_de, # [B, T, C] "durations_log": o_dr_log.squeeze(1), # [B, T] @@ -569,22 +583,22 @@ class ForwardTTS(BaseTTS): - x_lengths: [B] - g: [B, C] """ - g = aux_input["d_vectors"] if "d_vectors" in aux_input else None + g = self._set_speaker_input(aux_input) x_lengths = torch.tensor(x.shape[1:2]).to(x.device) x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype).float() # encoder pass - o_en, o_en_dp, x_mask, g, _ = self._forward_encoder(x, x_mask, g) + o_en, x_mask, g, _ = self._forward_encoder(x, x_mask, g) # duration predictor pass - o_dr_log = self.duration_predictor(o_en_dp, x_mask) + o_dr_log = self.duration_predictor(o_en, x_mask) o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1) y_lengths = o_dr.sum(1) # pitch predictor pass o_pitch = None if self.args.use_pitch: - o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en_dp, x_mask) + o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en, x_mask) o_en = o_en + o_pitch_emb # decoder pass - o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=g) + o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=None) outputs = { "model_outputs": o_de, "alignments": attn, @@ -634,7 +648,8 @@ class ForwardTTS(BaseTTS): return outputs, loss_dict - def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use + def _create_logs(self, batch, outputs, ap): + """Create common logger outputs.""" model_outputs = outputs["model_outputs"] alignments = outputs["alignments"] mel_input = batch["mel_input"] @@ -674,11 +689,22 @@ class ForwardTTS(BaseTTS): train_audio = ap.inv_melspectrogram(pred_spec.T) return figures, {"audio": train_audio} + def train_log( + self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int + ) -> None: # pylint: disable=no-self-use + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.train_figures(steps, figures) + logger.train_audios(steps, audios, ap.sample_rate) + def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) - def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): - return self.train_log(ap, batch, outputs) + def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.eval_figures(steps, figures) + logger.eval_audios(steps, audios, ap.sample_rate) def load_checkpoint( self, config, checkpoint_path, eval=False diff --git a/TTS/tts/models/glow_tts.py b/TTS/tts/models/glow_tts.py index 2e94659e..c1e4c2ac 100644 --- a/TTS/tts/models/glow_tts.py +++ b/TTS/tts/models/glow_tts.py @@ -1,19 +1,20 @@ import math +from typing import Dict, Tuple, Union import torch +from coqpit import Coqpit from torch import nn from torch.cuda.amp.autocast_mode import autocast from torch.nn import functional as F -from TTS.tts.configs import GlowTTSConfig +from TTS.tts.configs.glow_tts_config import GlowTTSConfig from TTS.tts.layers.glow_tts.decoder import Decoder from TTS.tts.layers.glow_tts.encoder import Encoder from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask -from TTS.tts.utils.speakers import get_speaker_manager +from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.visual import plot_alignment, plot_spectrogram -from TTS.utils.audio import AudioProcessor from TTS.utils.io import load_fsspec @@ -38,16 +39,18 @@ class GlowTTS(BaseTTS): Check :class:`TTS.tts.configs.glow_tts_config.GlowTTSConfig` for class arguments. Examples: - >>> from TTS.tts.configs import GlowTTSConfig + >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig >>> from TTS.tts.models.glow_tts import GlowTTS >>> config = GlowTTSConfig() >>> model = GlowTTS(config) """ - def __init__(self, config: GlowTTSConfig): + def __init__(self, config: GlowTTSConfig, speaker_manager: SpeakerManager = None): - super().__init__() + super().__init__(config) + + self.speaker_manager = speaker_manager # pass all config fields to `self` # for fewer code change @@ -58,19 +61,10 @@ class GlowTTS(BaseTTS): _, self.config, self.num_chars = self.get_characters(config) self.decoder_output_dim = config.out_channels + # init multi-speaker layers if necessary self.init_multispeaker(config) - # if is a multispeaker and c_in_channels is 0, set to 256 - self.c_in_channels = 0 - if self.num_speakers > 1: - if self.d_vector_dim: - self.c_in_channels = self.d_vector_dim - elif self.c_in_channels == 0 and not self.d_vector_dim: - # TODO: make this adjustable - self.c_in_channels = 256 - self.run_data_dep_init = config.data_dep_init_steps > 0 - self.encoder = Encoder( self.num_chars, out_channels=self.out_channels, @@ -98,28 +92,35 @@ class GlowTTS(BaseTTS): c_in_channels=self.c_in_channels, ) - def init_multispeaker(self, config: "Coqpit", data: list = None) -> None: - """Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer - or with external `d_vectors` computed from a speaker encoder model. - - If you need a different behaviour, override this function for your model. + def init_multispeaker(self, config: Coqpit): + """Init speaker embedding layer if `use_speaker_embedding` is True and set the expected speaker embedding + vector dimension in the network. If model uses d-vectors, then it only sets the expected dimension. Args: config (Coqpit): Model configuration. - data (List, optional): Dataset items to infer number of speakers. Defaults to None. """ + self.embedded_speaker_dim = 0 # init speaker manager - self.speaker_manager = get_speaker_manager(config, data=data) - self.num_speakers = self.speaker_manager.num_speakers - if config.use_d_vector_file: - self.external_d_vector_dim = config.d_vector_dim - else: - self.external_d_vector_dim = 0 + if self.speaker_manager is None and (self.use_speaker_embedding or self.use_d_vector_file): + raise ValueError( + " > SpeakerManager is not provided. You must provide the SpeakerManager before initializing a multi-speaker model." + ) + # set number of speakers - if num_speakers is set in config, use it, otherwise use speaker_manager + if self.speaker_manager is not None: + self.num_speakers = self.speaker_manager.num_speakers + # set ultimate speaker embedding size + if config.use_speaker_embedding or config.use_d_vector_file: + self.embedded_speaker_dim = ( + config.d_vector_dim if "d_vector_dim" in config and config.d_vector_dim is not None else 512 + ) # init speaker embedding layer if config.use_speaker_embedding and not config.use_d_vector_file: - self.embedded_speaker_dim = self.c_in_channels - self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim) + print(" > Init speaker_embedding layer.") + self.embedded_speaker_dim = self.hidden_channels_enc + self.emb_g = nn.Embedding(self.num_speakers, self.hidden_channels_enc) nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) + # set conditioning dimensions + self.c_in_channels = self.embedded_speaker_dim @staticmethod def compute_outputs(attn, o_mean, o_log_scale, x_mask): @@ -146,6 +147,35 @@ class GlowTTS(BaseTTS): if getattr(f, "set_ddi", False): f.set_ddi(False) + def _set_speaker_input(self, aux_input: Dict): + if aux_input is None: + d_vectors = None + speaker_ids = None + else: + d_vectors = aux_input.get("d_vectors", None) + speaker_ids = aux_input.get("speaker_ids", None) + + if d_vectors is not None and speaker_ids is not None: + raise ValueError("[!] Cannot use d-vectors and speaker-ids together.") + + if speaker_ids is not None and not hasattr(self, "emb_g"): + raise ValueError("[!] Cannot use speaker-ids without enabling speaker embedding.") + + g = speaker_ids if speaker_ids is not None else d_vectors + return g + + def _speaker_embedding(self, aux_input: Dict) -> Union[torch.tensor, None]: + g = self._set_speaker_input(aux_input) + # speaker embedding + if g is not None: + if hasattr(self, "emb_g"): + # use speaker embedding layer + g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1] + else: + # use d-vector + g = F.normalize(g).unsqueeze(-1) # [b, h, 1] + return g + def forward( self, x, x_lengths, y, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None} ): # pylint: disable=dangerous-default-value @@ -161,12 +191,7 @@ class GlowTTS(BaseTTS): y = y.transpose(1, 2) y_max_length = y.size(2) # norm speaker embeddings - g = aux_input["d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None - if self.use_speaker_embedding or self.use_d_vector_file: - if not self.use_d_vector_file: - g = F.normalize(g).unsqueeze(-1) - else: - g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1] + g = self._speaker_embedding(aux_input) # embedding pass o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x, x_lengths, g=g) # drop redisual frames wrt num_squeeze and set y_lengths. @@ -217,12 +242,7 @@ class GlowTTS(BaseTTS): y = y.transpose(1, 2) y_max_length = y.size(2) # norm speaker embeddings - g = aux_input["d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None - if self.use_speaker_embedding or self.use_d_vector_file: - if not self.use_d_vector_file: - g = F.normalize(g).unsqueeze(-1) - else: - g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1] + g = self._speaker_embedding(aux_input) # embedding pass o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x, x_lengths, g=g) # drop redisual frames wrt num_squeeze and set y_lengths. @@ -272,22 +292,12 @@ class GlowTTS(BaseTTS): """ y = y.transpose(1, 2) y_max_length = y.size(2) - g = aux_input["d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None - # norm speaker embeddings - if g is not None: - if self.external_d_vector_dim: - g = F.normalize(g).unsqueeze(-1) - else: - g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h, 1] - + g = self._speaker_embedding(aux_input) y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length), 1).to(y.dtype) - # decoder pass z, logdet = self.decoder(y, y_mask, g=g, reverse=False) - # reverse decoder and predict y, logdet = self.decoder(z, y_mask, g=g, reverse=True) - outputs = {} outputs["model_outputs"] = y.transpose(1, 2) outputs["logdet"] = logdet @@ -298,19 +308,12 @@ class GlowTTS(BaseTTS): self, x, aux_input={"x_lengths": None, "d_vectors": None, "speaker_ids": None} ): # pylint: disable=dangerous-default-value x_lengths = aux_input["x_lengths"] - g = aux_input["d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None - - if g is not None: - if self.d_vector_dim: - g = F.normalize(g).unsqueeze(-1) - else: - g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h] - + g = self._speaker_embedding(aux_input) # embedding pass o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x, x_lengths, g=g) # compute output durations w = (torch.exp(o_dur_log) - 1) * x_mask * self.length_scale - w_ceil = torch.ceil(w) + w_ceil = torch.clamp_min(torch.ceil(w), 1) y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() y_max_length = None # compute masks @@ -387,17 +390,17 @@ class GlowTTS(BaseTTS): ) return outputs, loss_dict - def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use + def _create_logs(self, batch, outputs, ap): alignments = outputs["alignments"] - text_input = batch["text_input"] + text_input = batch["text_input"][:1] if batch["text_input"] is not None else None text_lengths = batch["text_lengths"] mel_input = batch["mel_input"] - d_vectors = batch["d_vectors"] - speaker_ids = batch["speaker_ids"] + d_vectors = batch["d_vectors"][:1] if batch["d_vectors"] is not None else None + speaker_ids = batch["speaker_ids"][:1] if batch["speaker_ids"] is not None else None # model runs reverse flow to predict spectrograms pred_outputs = self.inference( - text_input[:1], + text_input, aux_input={"x_lengths": text_lengths[:1], "d_vectors": d_vectors, "speaker_ids": speaker_ids}, ) model_outputs = pred_outputs["model_outputs"] @@ -416,15 +419,26 @@ class GlowTTS(BaseTTS): train_audio = ap.inv_melspectrogram(pred_spec.T) return figures, {"audio": train_audio} + def train_log( + self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int + ) -> None: # pylint: disable=no-self-use + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.train_figures(steps, figures) + logger.train_audios(steps, audios, ap.sample_rate) + @torch.no_grad() def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) - def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): - return self.train_log(ap, batch, outputs) + def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.eval_figures(steps, figures) + logger.eval_audios(steps, audios, ap.sample_rate) @torch.no_grad() - def test_run(self, ap): + def test_run(self, assets: Dict) -> Tuple[Dict, Dict]: """Generic test run for `tts` models used by `Trainer`. You can override this for a different behaviour. @@ -432,11 +446,12 @@ class GlowTTS(BaseTTS): Returns: Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard. """ + ap = assets["audio_processor"] print(" | > Synthesizing test sentences.") test_audios = {} test_figures = {} test_sentences = self.config.test_sentences - aux_inputs = self.get_aux_input() + aux_inputs = self._get_test_aux_input() if len(test_sentences) == 0: print(" | [!] No test sentences provided.") else: diff --git a/TTS/tts/models/tacotron.py b/TTS/tts/models/tacotron.py index 84a256d5..4e46d252 100644 --- a/TTS/tts/models/tacotron.py +++ b/TTS/tts/models/tacotron.py @@ -1,29 +1,34 @@ # coding: utf-8 -from typing import Dict, Tuple - import torch from coqpit import Coqpit from torch import nn +from torch.cuda.amp.autocast_mode import autocast from TTS.tts.layers.tacotron.gst_layers import GST from TTS.tts.layers.tacotron.tacotron import Decoder, Encoder, PostCBHG from TTS.tts.models.base_tacotron import BaseTacotron from TTS.tts.utils.measures import alignment_diagonal_score +from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.visual import plot_alignment, plot_spectrogram -from TTS.utils.audio import AudioProcessor class Tacotron(BaseTacotron): """Tacotron as in https://arxiv.org/abs/1703.10135 It's an autoregressive encoder-attention-decoder-postnet architecture. Check `TacotronConfig` for the arguments. + + Args: + config (TacotronConfig): Configuration for the Tacotron model. + speaker_manager (SpeakerManager): Speaker manager to handle multi-speaker settings. Only use if the model is + a multi-speaker model. Defaults to None. """ - def __init__(self, config: Coqpit): + def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None): super().__init__(config) - chars, self.config = self.get_characters(config) + self.speaker_manager = speaker_manager + chars, self.config, _ = self.get_characters(config) config.num_chars = self.num_chars = len(chars) # pass all config fields to `self` @@ -243,40 +248,47 @@ class Tacotron(BaseTacotron): outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input) # compute loss - loss_dict = criterion( - outputs["model_outputs"], - outputs["decoder_outputs"], - mel_input, - linear_input, - outputs["stop_tokens"], - stop_targets, - stop_target_lengths, - mel_lengths, - outputs["decoder_outputs_backward"], - outputs["alignments"], - alignment_lengths, - outputs["alignments_backward"], - text_lengths, - ) + with autocast(enabled=False): # use float32 for the criterion + loss_dict = criterion( + outputs["model_outputs"].float(), + outputs["decoder_outputs"].float(), + mel_input.float(), + linear_input.float(), + outputs["stop_tokens"].float(), + stop_targets.float(), + stop_target_lengths, + mel_lengths, + None if outputs["decoder_outputs_backward"] is None else outputs["decoder_outputs_backward"].float(), + outputs["alignments"].float(), + alignment_lengths, + None if outputs["alignments_backward"] is None else outputs["alignments_backward"].float(), + text_lengths, + ) # compute alignment error (the lower the better ) align_error = 1 - alignment_diagonal_score(outputs["alignments"]) loss_dict["align_error"] = align_error return outputs, loss_dict - def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict) -> Tuple[Dict, Dict]: + def _create_logs(self, batch, outputs, ap): postnet_outputs = outputs["model_outputs"] + decoder_outputs = outputs["decoder_outputs"] alignments = outputs["alignments"] alignments_backward = outputs["alignments_backward"] mel_input = batch["mel_input"] + linear_input = batch["linear_input"] - pred_spec = postnet_outputs[0].data.cpu().numpy() - gt_spec = mel_input[0].data.cpu().numpy() + pred_linear_spec = postnet_outputs[0].data.cpu().numpy() + pred_mel_spec = decoder_outputs[0].data.cpu().numpy() + gt_linear_spec = linear_input[0].data.cpu().numpy() + gt_mel_spec = mel_input[0].data.cpu().numpy() align_img = alignments[0].data.cpu().numpy() figures = { - "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), - "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), + "pred_linear_spec": plot_spectrogram(pred_linear_spec, ap, output_fig=False), + "real_linear_spec": plot_spectrogram(gt_linear_spec, ap, output_fig=False), + "pred_mel_spec": plot_spectrogram(pred_mel_spec, ap, output_fig=False), + "real_mel_spec": plot_spectrogram(gt_mel_spec, ap, output_fig=False), "alignment": plot_alignment(align_img, output_fig=False), } @@ -284,11 +296,22 @@ class Tacotron(BaseTacotron): figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy(), output_fig=False) # Sample audio - train_audio = ap.inv_spectrogram(pred_spec.T) - return figures, {"audio": train_audio} + audio = ap.inv_spectrogram(pred_linear_spec.T) + return figures, {"audio": audio} - def eval_step(self, batch, criterion): + def train_log( + self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int + ) -> None: # pylint: disable=no-self-use + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.train_figures(steps, figures) + logger.train_audios(steps, audios, ap.sample_rate) + + def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) - def eval_log(self, ap, batch, outputs): - return self.train_log(ap, batch, outputs) + def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.eval_figures(steps, figures) + logger.eval_audios(steps, audios, ap.sample_rate) diff --git a/TTS/tts/models/tacotron2.py b/TTS/tts/models/tacotron2.py index 39ef12a8..ead3bf2b 100644 --- a/TTS/tts/models/tacotron2.py +++ b/TTS/tts/models/tacotron2.py @@ -1,28 +1,50 @@ # coding: utf-8 -from typing import Dict, Tuple +from typing import Dict import torch from coqpit import Coqpit from torch import nn +from torch.cuda.amp.autocast_mode import autocast from TTS.tts.layers.tacotron.gst_layers import GST from TTS.tts.layers.tacotron.tacotron2 import Decoder, Encoder, Postnet from TTS.tts.models.base_tacotron import BaseTacotron from TTS.tts.utils.measures import alignment_diagonal_score +from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.visual import plot_alignment, plot_spectrogram -from TTS.utils.audio import AudioProcessor class Tacotron2(BaseTacotron): - """Tacotron2 as in https://arxiv.org/abs/1712.05884 - Check `TacotronConfig` for the arguments. + """Tacotron2 model implementation inherited from :class:`TTS.tts.models.base_tacotron.BaseTacotron`. + + Paper:: + https://arxiv.org/abs/1712.05884 + + Paper abstract:: + This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. + The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character + embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize + timedomain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of 4.53 comparable + to a MOS of 4.58 for professionally recorded speech. To validate our design choices, we present ablation + studies of key components of our system and evaluate the impact of using mel spectrograms as the input to + WaveNet instead of linguistic, duration, and F0 features. We further demonstrate that using a compact acoustic + intermediate representation enables significant simplification of the WaveNet architecture. + + Check :class:`TTS.tts.configs.tacotron2_config.Tacotron2Config` for model arguments. + + Args: + config (TacotronConfig): + Configuration for the Tacotron2 model. + speaker_manager (SpeakerManager): + Speaker manager for multi-speaker training. Uuse only for multi-speaker training. Defaults to None. """ - def __init__(self, config: Coqpit): + def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None): super().__init__(config) - chars, self.config = self.get_characters(config) + self.speaker_manager = speaker_manager + chars, self.config, _ = self.get_characters(config) config.num_chars = len(chars) self.decoder_output_dim = config.out_channels @@ -31,9 +53,7 @@ class Tacotron2(BaseTacotron): for key in config: setattr(self, key, config[key]) - # set speaker embedding channel size for determining `in_channels` for the connected layers. - # `init_multispeaker` needs to be called once more in training to initialize the speaker embedding layer based - # on the number of speakers infered from the dataset. + # init multi-speaker layers if self.use_speaker_embedding or self.use_d_vector_file: self.init_multispeaker(config) self.decoder_in_features += self.embedded_speaker_dim # add speaker embedding dim @@ -103,6 +123,7 @@ class Tacotron2(BaseTacotron): @staticmethod def shape_outputs(mel_outputs, mel_outputs_postnet, alignments): + """Final reshape of the model output tensors.""" mel_outputs = mel_outputs.transpose(1, 2) mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2) return mel_outputs, mel_outputs_postnet, alignments @@ -110,13 +131,14 @@ class Tacotron2(BaseTacotron): def forward( # pylint: disable=dangerous-default-value self, text, text_lengths, mel_specs=None, mel_lengths=None, aux_input={"speaker_ids": None, "d_vectors": None} ): - """ + """Forward pass for training with Teacher Forcing. + Shapes: - text: [B, T_in] - text_lengths: [B] - mel_specs: [B, T_out, C] - mel_lengths: [B] - aux_input: 'speaker_ids': [B, 1] and 'd_vectors':[B, C] + text: :math:`[B, T_in]` + text_lengths: :math:`[B]` + mel_specs: :math:`[B, T_out, C]` + mel_lengths: :math:`[B]` + aux_input: 'speaker_ids': :math:`[B, 1]` and 'd_vectors': :math:`[B, C]` """ aux_input = self._format_aux_input(aux_input) outputs = {"alignments_backward": None, "decoder_outputs_backward": None} @@ -177,6 +199,12 @@ class Tacotron2(BaseTacotron): @torch.no_grad() def inference(self, text, aux_input=None): + """Forward pass for inference with no Teacher-Forcing. + + Shapes: + text: :math:`[B, T_in]` + text_lengths: :math:`[B]` + """ aux_input = self._format_aux_input(aux_input) embedded_inputs = self.embedding(text).transpose(1, 2) encoder_outputs = self.encoder.inference(embedded_inputs) @@ -210,18 +238,17 @@ class Tacotron2(BaseTacotron): } return outputs - def train_step(self, batch, criterion): - """Perform a single training step by fetching the right set if samples from the batch. + def train_step(self, batch: Dict, criterion: torch.nn.Module): + """A single training step. Forward pass and loss computation. Args: - batch ([type]): [description] - criterion ([type]): [description] + batch ([Dict]): A dictionary of input tensors. + criterion ([type]): Callable criterion to compute model loss. """ text_input = batch["text_input"] text_lengths = batch["text_lengths"] mel_input = batch["mel_input"] mel_lengths = batch["mel_lengths"] - linear_input = batch["linear_input"] stop_targets = batch["stop_targets"] stop_target_lengths = batch["stop_target_lengths"] speaker_ids = batch["speaker_ids"] @@ -248,28 +275,30 @@ class Tacotron2(BaseTacotron): outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input) # compute loss - loss_dict = criterion( - outputs["model_outputs"], - outputs["decoder_outputs"], - mel_input, - linear_input, - outputs["stop_tokens"], - stop_targets, - stop_target_lengths, - mel_lengths, - outputs["decoder_outputs_backward"], - outputs["alignments"], - alignment_lengths, - outputs["alignments_backward"], - text_lengths, - ) + with autocast(enabled=False): # use float32 for the criterion + loss_dict = criterion( + outputs["model_outputs"].float(), + outputs["decoder_outputs"].float(), + mel_input.float(), + None, + outputs["stop_tokens"].float(), + stop_targets.float(), + stop_target_lengths, + mel_lengths, + None if outputs["decoder_outputs_backward"] is None else outputs["decoder_outputs_backward"].float(), + outputs["alignments"].float(), + alignment_lengths, + None if outputs["alignments_backward"] is None else outputs["alignments_backward"].float(), + text_lengths, + ) # compute alignment error (the lower the better ) align_error = 1 - alignment_diagonal_score(outputs["alignments"]) loss_dict["align_error"] = align_error return outputs, loss_dict - def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict) -> Tuple[Dict, Dict]: + def _create_logs(self, batch, outputs, ap): + """Create dashboard log information.""" postnet_outputs = outputs["model_outputs"] alignments = outputs["alignments"] alignments_backward = outputs["alignments_backward"] @@ -289,11 +318,23 @@ class Tacotron2(BaseTacotron): figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy(), output_fig=False) # Sample audio - train_audio = ap.inv_melspectrogram(pred_spec.T) - return figures, {"audio": train_audio} + audio = ap.inv_melspectrogram(pred_spec.T) + return figures, {"audio": audio} - def eval_step(self, batch, criterion): + def train_log( + self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int + ) -> None: # pylint: disable=no-self-use + """Log training progress.""" + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.train_figures(steps, figures) + logger.train_audios(steps, audios, ap.sample_rate) + + def eval_step(self, batch: dict, criterion: nn.Module): return self.train_step(batch, criterion) - def eval_log(self, ap, batch, outputs): - return self.train_log(ap, batch, outputs) + def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: + ap = assets["audio_processor"] + figures, audios = self._create_logs(batch, outputs, ap) + logger.eval_figures(steps, figures) + logger.eval_audios(steps, audios, ap.sample_rate) diff --git a/TTS/tts/models/vits.py b/TTS/tts/models/vits.py index 87695774..5e4b408b 100644 --- a/TTS/tts/models/vits.py +++ b/TTS/tts/models/vits.py @@ -1,4 +1,5 @@ import math +import random from dataclasses import dataclass, field from itertools import chain from typing import Dict, List, Tuple @@ -14,10 +15,9 @@ from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlock from TTS.tts.layers.vits.stochastic_duration_predictor import StochasticDurationPredictor from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import generate_path, maximum_path, rand_segments, segment, sequence_mask -from TTS.tts.utils.speakers import get_speaker_manager +from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.visual import plot_alignment -from TTS.utils.audio import AudioProcessor from TTS.utils.trainer_utils import get_optimizer, get_scheduler from TTS.vocoder.models.hifigan_generator import HifiganGenerator from TTS.vocoder.utils.generic_utils import plot_results @@ -181,6 +181,7 @@ class VitsArgs(Coqpit): speakers_file: str = None speaker_embedding_channels: int = 256 use_d_vector_file: bool = False + d_vector_file: str = None d_vector_dim: int = 0 detach_dp_input: bool = True @@ -207,7 +208,7 @@ class Vits(BaseTTS): Check :class:`TTS.tts.configs.vits_config.VitsConfig` for class arguments. Examples: - >>> from TTS.tts.configs import VitsConfig + >>> from TTS.tts.configs.vits_config import VitsConfig >>> from TTS.tts.models.vits import Vits >>> config = VitsConfig() >>> model = Vits(config) @@ -215,12 +216,13 @@ class Vits(BaseTTS): # pylint: disable=dangerous-default-value - def __init__(self, config: Coqpit): + def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None): - super().__init__() + super().__init__(config) self.END2END = True + self.speaker_manager = speaker_manager if config.__class__.__name__ == "VitsConfig": # loading from VitsConfig if "num_chars" not in config: @@ -312,31 +314,42 @@ class Vits(BaseTTS): if args.init_discriminator: self.disc = VitsDiscriminator(use_spectral_norm=args.use_spectral_norm_disriminator) - def init_multispeaker(self, config: Coqpit, data: List = None): + def init_multispeaker(self, config: Coqpit): """Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer or with external `d_vectors` computed from a speaker encoder model. - If you need a different behaviour, override this function for your model. - Args: config (Coqpit): Model configuration. data (List, optional): Dataset items to infer number of speakers. Defaults to None. """ + self.embedded_speaker_dim = 0 if hasattr(config, "model_args"): config = config.model_args - self.embedded_speaker_dim = 0 - # init speaker manager - self.speaker_manager = get_speaker_manager(config, data=data) - if config.num_speakers > 0 and self.speaker_manager.num_speakers == 0: - self.speaker_manager.num_speakers = config.num_speakers - self.num_speakers = self.speaker_manager.num_speakers - # init speaker embedding layer - if config.use_speaker_embedding and not config.use_d_vector_file: - self.embedded_speaker_dim = config.speaker_embedding_channels - self.emb_g = nn.Embedding(config.num_speakers, config.speaker_embedding_channels) - # init d-vector usage + + self.num_speakers = config.num_speakers + + if config.use_speaker_embedding: + self._init_speaker_embedding(config) + if config.use_d_vector_file: - self.embedded_speaker_dim = config.d_vector_dim + self._init_d_vector(config) + + def _init_speaker_embedding(self, config): + # pylint: disable=attribute-defined-outside-init + if config.speakers_file is not None: + self.speaker_manager = SpeakerManager(speaker_id_file_path=config.speakers_file_path) + + if self.num_speakers > 0: + print(" > initialization of speaker-embedding layers.") + self.embedded_speaker_dim = config.speaker_embedding_channels + self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim) + + def _init_d_vector(self, config): + # pylint: disable=attribute-defined-outside-init + if hasattr(self, "emb_g"): + raise ValueError("[!] Speaker embedding layer already initialized before d_vector settings.") + self.speaker_manager = SpeakerManager(d_vectors_file_path=config.d_vector_file) + self.embedded_speaker_dim = config.d_vector_dim @staticmethod def _set_cond_input(aux_input: Dict): @@ -350,6 +363,10 @@ class Vits(BaseTTS): g = aux_input["d_vectors"] return sid, g + def get_aux_input(self, aux_input: Dict): + sid, g = self._set_cond_input(aux_input) + return {"speaker_id": sid, "style_wav": None, "d_vector": g} + def forward( self, x: torch.tensor, @@ -457,7 +474,7 @@ class Vits(BaseTTS): x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths) - if self.num_speakers > 0 and sid: + if self.num_speakers > 0 and sid is not None: g = self.emb_g(sid).unsqueeze(-1) if self.args.use_sdp: @@ -576,22 +593,7 @@ class Vits(BaseTTS): ) return outputs, loss_dict - def train_log( - self, ap: AudioProcessor, batch: Dict, outputs: List, name_prefix="train" - ): # pylint: disable=no-self-use - """Create visualizations and waveform examples. - - For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to - be projected onto Tensorboard. - - Args: - ap (AudioProcessor): audio processor used at training. - batch (Dict): Model inputs used at the previous training step. - outputs (Dict): Model outputs generated at the previoud training step. - - Returns: - Tuple[Dict, np.ndarray]: training plots and output waveform. - """ + def _log(self, ap, batch, outputs, name_prefix="train"): # pylint: disable=unused-argument,no-self-use y_hat = outputs[0]["model_outputs"] y = outputs[0]["waveform_seg"] figures = plot_results(y_hat, y, ap, name_prefix) @@ -609,12 +611,32 @@ class Vits(BaseTTS): return figures, audios + def train_log( + self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int + ): # pylint: disable=no-self-use + """Create visualizations and waveform examples. + + For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to + be projected onto Tensorboard. + + Args: + ap (AudioProcessor): audio processor used at training. + batch (Dict): Model inputs used at the previous training step. + outputs (Dict): Model outputs generated at the previoud training step. + + Returns: + Tuple[Dict, np.ndarray]: training plots and output waveform. + """ + ap = assets["audio_processor"] + self._log(ap, batch, outputs, "train") + @torch.no_grad() def eval_step(self, batch: dict, criterion: nn.Module, optimizer_idx: int): return self.train_step(batch, criterion, optimizer_idx) - def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): - return self.train_log(ap, batch, outputs, "eval") + def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None: + ap = assets["audio_processor"] + return self._log(ap, batch, outputs, "eval") @torch.no_grad() def test_run(self, ap) -> Tuple[Dict, Dict]: @@ -629,7 +651,15 @@ class Vits(BaseTTS): test_audios = {} test_figures = {} test_sentences = self.config.test_sentences - aux_inputs = self.get_aux_input() + aux_inputs = { + "speaker_id": None + if not self.config.use_speaker_embedding + else random.sample(sorted(self.speaker_manager.speaker_ids.values()), 1), + "d_vector": None + if not self.config.use_d_vector_file + else random.samples(sorted(self.speaker_manager.d_vectors.values()), 1), + "style_wav": None, + } for idx, sen in enumerate(test_sentences): wav, alignment, _, _ = synthesis( self, @@ -666,7 +696,7 @@ class Vits(BaseTTS): ) # add the speaker embedding layer if hasattr(self, "emb_g"): - gen_parameters = chain(gen_parameters, self.emb_g) + gen_parameters = chain(gen_parameters, self.emb_g.parameters()) optimizer0 = get_optimizer( self.config.optimizer, self.config.optimizer_params, self.config.lr_gen, parameters=gen_parameters ) diff --git a/TTS/tts/utils/monotonic_align/core.c b/TTS/tts/utils/monotonic_align/core.c deleted file mode 100644 index 5e3d619b..00000000 --- a/TTS/tts/utils/monotonic_align/core.c +++ /dev/null @@ -1,23086 +0,0 @@ -/* Generated by Cython 0.29.24 */ - -/* BEGIN: Cython Metadata -{ - "distutils": { - "depends": [], - "name": "TTS.tts.layers.glow_tts.monotonic_align.core", - "sources": [ - "TTS/tts/layers/glow_tts/monotonic_align/core.pyx" - ] - }, - "module_name": "TTS.tts.layers.glow_tts.monotonic_align.core" -} -END: Cython Metadata */ - -#ifndef PY_SSIZE_T_CLEAN -#define PY_SSIZE_T_CLEAN -#endif /* PY_SSIZE_T_CLEAN */ -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - 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- default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); 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- -/* HasAttr.proto */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); - -/* PyObject_GenericGetAttrNoDict.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr -#endif - -/* PyObject_GenericGetAttr.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr -#endif - -/* SetVTable.proto */ -static int __Pyx_SetVtable(PyObject *dict, void *vtable); - -/* PyObjectGetAttrStrNoError.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); - -/* SetupReduce.proto */ -static int __Pyx_setup_reduce(PyObject* type_obj); - -/* TypeImport.proto */ -#ifndef __PYX_HAVE_RT_ImportType_proto -#define __PYX_HAVE_RT_ImportType_proto -enum __Pyx_ImportType_CheckSize { - __Pyx_ImportType_CheckSize_Error = 0, - __Pyx_ImportType_CheckSize_Warn = 1, - __Pyx_ImportType_CheckSize_Ignore = 2 -}; -static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); -#endif - -/* CLineInTraceback.proto */ -#ifdef CYTHON_CLINE_IN_TRACEBACK -#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) -#else -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); -#endif - -/* CodeObjectCache.proto */ -typedef struct { - PyCodeObject* code_object; - int code_line; -} __Pyx_CodeObjectCacheEntry; -struct __Pyx_CodeObjectCache { - int count; - int max_count; - __Pyx_CodeObjectCacheEntry* entries; -}; -static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); 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- -/* Capsule.proto */ -static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); - -/* IsLittleEndian.proto */ -static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); - -/* BufferFormatCheck.proto */ -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type); - -/* TypeInfoCompare.proto */ -static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); - -/* MemviewSliceValidateAndInit.proto */ -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); - -/* GCCDiagnostics.proto */ -#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) -#define __Pyx_HAS_GCC_DIAGNOSTIC -#endif - -/* RealImag.proto */ -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - #define __Pyx_CREAL(z) ((z).real()) - #define __Pyx_CIMAG(z) ((z).imag()) - #else - #define __Pyx_CREAL(z) (__real__(z)) - #define __Pyx_CIMAG(z) (__imag__(z)) - #endif -#else - #define __Pyx_CREAL(z) ((z).real) - #define __Pyx_CIMAG(z) ((z).imag) -#endif -#if defined(__cplusplus) && CYTHON_CCOMPLEX\ - && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) - #define __Pyx_SET_CREAL(z,x) ((z).real(x)) - #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) -#else - #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) - #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) -#endif - -/* Arithmetic.proto */ -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq_float(a, b) ((a)==(b)) - #define __Pyx_c_sum_float(a, b) ((a)+(b)) - #define __Pyx_c_diff_float(a, b) ((a)-(b)) - #define __Pyx_c_prod_float(a, b) ((a)*(b)) - #define __Pyx_c_quot_float(a, b) ((a)/(b)) - #define __Pyx_c_neg_float(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero_float(z) ((z)==(float)0) - #define __Pyx_c_conj_float(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs_float(z) (::std::abs(z)) - #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero_float(z) ((z)==0) - #define __Pyx_c_conj_float(z) (conjf(z)) - #if 1 - #define __Pyx_c_abs_float(z) (cabsf(z)) - #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); - #endif -#endif - -/* Arithmetic.proto */ -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq_double(a, b) ((a)==(b)) - #define __Pyx_c_sum_double(a, b) ((a)+(b)) - #define __Pyx_c_diff_double(a, b) ((a)-(b)) - #define __Pyx_c_prod_double(a, b) ((a)*(b)) - #define __Pyx_c_quot_double(a, b) ((a)/(b)) - #define __Pyx_c_neg_double(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero_double(z) ((z)==(double)0) - #define __Pyx_c_conj_double(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs_double(z) (::std::abs(z)) - #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero_double(z) ((z)==0) - #define __Pyx_c_conj_double(z) (conj(z)) - #if 1 - #define __Pyx_c_abs_double(z) (cabs(z)) - #define __Pyx_c_pow_double(a, b) (cpow(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); - #endif -#endif - -/* MemviewSliceCopyTemplate.proto */ -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ -static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ -static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ - -/* Module declarations from 'cython.view' */ - -/* Module declarations from 'cython' */ - -/* Module declarations from 'cpython.buffer' */ - -/* Module declarations from 'libc.string' */ - -/* Module declarations from 'libc.stdio' */ - -/* Module declarations from '__builtin__' */ - -/* Module declarations from 'cpython.type' */ -static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; - -/* Module declarations from 'cpython' */ - -/* Module declarations from 'cpython.object' */ - -/* Module declarations from 'cpython.ref' */ - -/* Module declarations from 'cpython.mem' */ - -/* Module declarations from 'numpy' */ - -/* Module declarations from 'numpy' */ -static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; -static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; -static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; -static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; -static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; - -/* Module declarations from 'TTS.tts.layers.glow_tts.monotonic_align.core' */ -static PyTypeObject *__pyx_array_type = 0; -static PyTypeObject *__pyx_MemviewEnum_type = 0; -static PyTypeObject *__pyx_memoryview_type = 0; -static PyTypeObject *__pyx_memoryviewslice_type = 0; -static PyObject *generic = 0; -static PyObject *strided = 0; -static PyObject *indirect = 0; -static PyObject *contiguous = 0; -static PyObject *indirect_contiguous = 0; -static int __pyx_memoryview_thread_locks_used; -static PyThread_type_lock __pyx_memoryview_thread_locks[8]; -static void __pyx_f_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, float); /*proto*/ -static void __pyx_f_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c *__pyx_optional_args); /*proto*/ -static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ -static void *__pyx_align_pointer(void *, size_t); /*proto*/ -static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ -static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ -static PyObject *_unellipsify(PyObject *, int); /*proto*/ -static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ -static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ -static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ -static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ -static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ -static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ -static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ -static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ -static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ -static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ -static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ -static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ -static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; -#define __Pyx_MODULE_NAME "TTS.tts.layers.glow_tts.monotonic_align.core" -extern int __pyx_module_is_main_TTS__tts__layers__glow_tts__monotonic_align__core; -int __pyx_module_is_main_TTS__tts__layers__glow_tts__monotonic_align__core = 0; - -/* Implementation of 'TTS.tts.layers.glow_tts.monotonic_align.core' */ -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin_ImportError; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_MemoryError; -static PyObject *__pyx_builtin_enumerate; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_Ellipsis; -static PyObject *__pyx_builtin_id; -static PyObject *__pyx_builtin_IndexError; -static const char __pyx_k_O[] = "O"; -static const char __pyx_k_c[] = "c"; -static const char __pyx_k_id[] = "id"; -static const char __pyx_k_np[] = "np"; -static const char __pyx_k_new[] = "__new__"; -static const char __pyx_k_obj[] = "obj"; -static const char __pyx_k_base[] = "base"; -static const char __pyx_k_dict[] = "__dict__"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_mode[] = "mode"; -static const char __pyx_k_name[] = "name"; -static const char __pyx_k_ndim[] = "ndim"; -static const char __pyx_k_pack[] = "pack"; -static const char __pyx_k_size[] = "size"; -static const char __pyx_k_step[] = "step"; -static const char __pyx_k_stop[] = "stop"; -static const char __pyx_k_t_xs[] = "t_xs"; -static const char __pyx_k_t_ys[] = "t_ys"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_ASCII[] = "ASCII"; -static const char __pyx_k_class[] = "__class__"; -static const char __pyx_k_error[] = "error"; -static const char __pyx_k_flags[] = "flags"; -static const char __pyx_k_numpy[] = "numpy"; -static const char __pyx_k_paths[] = "paths"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_shape[] = "shape"; -static const char __pyx_k_start[] = "start"; -static const char __pyx_k_encode[] = "encode"; -static const char __pyx_k_format[] = "format"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_name_2[] = "__name__"; -static const char __pyx_k_pickle[] = "pickle"; -static const char __pyx_k_reduce[] = "__reduce__"; -static const char __pyx_k_struct[] = "struct"; -static const char __pyx_k_unpack[] = "unpack"; -static const char __pyx_k_update[] = "update"; -static const char __pyx_k_values[] = "values"; -static const char __pyx_k_fortran[] = "fortran"; -static const char __pyx_k_memview[] = "memview"; -static const char __pyx_k_Ellipsis[] = "Ellipsis"; -static const char __pyx_k_getstate[] = "__getstate__"; -static const char __pyx_k_itemsize[] = "itemsize"; -static const char __pyx_k_pyx_type[] = "__pyx_type"; -static const char __pyx_k_setstate[] = "__setstate__"; -static const char __pyx_k_TypeError[] = "TypeError"; -static const char __pyx_k_enumerate[] = "enumerate"; -static const char __pyx_k_pyx_state[] = "__pyx_state"; -static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; -static const char __pyx_k_IndexError[] = "IndexError"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_pyx_result[] = "__pyx_result"; -static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; -static const char __pyx_k_ImportError[] = "ImportError"; -static const char __pyx_k_MemoryError[] = "MemoryError"; -static const char __pyx_k_PickleError[] = "PickleError"; -static const char __pyx_k_max_neg_val[] = "max_neg_val"; -static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; -static const char __pyx_k_stringsource[] = "stringsource"; -static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; -static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; -static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; -static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; -static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; -static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; -static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; -static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; -static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; -static const char __pyx_k_strided_and_direct[] = ""; -static const char __pyx_k_strided_and_indirect[] = ""; -static const char __pyx_k_contiguous_and_direct[] = ""; -static const char __pyx_k_MemoryView_of_r_object[] = ""; -static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; -static const char __pyx_k_contiguous_and_indirect[] = ""; -static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; -static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; -static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; -static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; -static const char __pyx_k_strided_and_direct_or_indirect[] = ""; -static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; -static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; -static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; -static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; -static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; -static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; -static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; -static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; -static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; -static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; -static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; -static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; -static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; -static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; -static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; -static PyObject *__pyx_n_s_ASCII; -static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; -static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; -static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; -static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; -static PyObject *__pyx_kp_s_Cannot_index_with_type_s; -static PyObject *__pyx_n_s_Ellipsis; -static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; -static PyObject *__pyx_n_s_ImportError; -static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; -static PyObject *__pyx_n_s_IndexError; -static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; -static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; -static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; -static PyObject *__pyx_n_s_MemoryError; -static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; -static PyObject *__pyx_kp_s_MemoryView_of_r_object; -static PyObject *__pyx_n_b_O; -static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; -static PyObject *__pyx_n_s_PickleError; -static PyObject *__pyx_n_s_TypeError; -static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; -static PyObject *__pyx_n_s_ValueError; -static PyObject *__pyx_n_s_View_MemoryView; -static PyObject *__pyx_n_s_allocate_buffer; -static PyObject *__pyx_n_s_base; -static PyObject *__pyx_n_s_c; -static PyObject *__pyx_n_u_c; -static PyObject *__pyx_n_s_class; -static PyObject *__pyx_n_s_cline_in_traceback; -static PyObject *__pyx_kp_s_contiguous_and_direct; -static PyObject *__pyx_kp_s_contiguous_and_indirect; -static PyObject *__pyx_n_s_dict; -static PyObject *__pyx_n_s_dtype_is_object; -static PyObject *__pyx_n_s_encode; -static PyObject *__pyx_n_s_enumerate; -static PyObject *__pyx_n_s_error; -static PyObject *__pyx_n_s_flags; -static PyObject *__pyx_n_s_format; -static PyObject *__pyx_n_s_fortran; -static PyObject *__pyx_n_u_fortran; -static PyObject *__pyx_n_s_getstate; -static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; -static PyObject *__pyx_n_s_id; -static PyObject *__pyx_n_s_import; -static PyObject *__pyx_n_s_itemsize; -static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; -static PyObject *__pyx_n_s_main; -static PyObject *__pyx_n_s_max_neg_val; -static PyObject *__pyx_n_s_memview; -static PyObject *__pyx_n_s_mode; -static PyObject *__pyx_n_s_name; -static PyObject *__pyx_n_s_name_2; -static PyObject *__pyx_n_s_ndim; -static PyObject *__pyx_n_s_new; -static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; -static PyObject *__pyx_n_s_np; -static PyObject *__pyx_n_s_numpy; -static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; -static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; -static PyObject *__pyx_n_s_obj; -static PyObject *__pyx_n_s_pack; -static PyObject *__pyx_n_s_paths; -static PyObject *__pyx_n_s_pickle; -static PyObject *__pyx_n_s_pyx_PickleError; -static PyObject *__pyx_n_s_pyx_checksum; -static PyObject *__pyx_n_s_pyx_getbuffer; -static PyObject *__pyx_n_s_pyx_result; -static PyObject *__pyx_n_s_pyx_state; -static PyObject *__pyx_n_s_pyx_type; -static PyObject *__pyx_n_s_pyx_unpickle_Enum; -static PyObject *__pyx_n_s_pyx_vtable; -static PyObject *__pyx_n_s_range; -static PyObject *__pyx_n_s_reduce; -static PyObject *__pyx_n_s_reduce_cython; -static PyObject *__pyx_n_s_reduce_ex; -static PyObject *__pyx_n_s_setstate; -static PyObject *__pyx_n_s_setstate_cython; -static PyObject *__pyx_n_s_shape; -static PyObject *__pyx_n_s_size; -static PyObject *__pyx_n_s_start; -static PyObject *__pyx_n_s_step; -static PyObject *__pyx_n_s_stop; -static PyObject *__pyx_kp_s_strided_and_direct; -static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; -static PyObject *__pyx_kp_s_strided_and_indirect; -static PyObject *__pyx_kp_s_stringsource; -static PyObject *__pyx_n_s_struct; -static PyObject *__pyx_n_s_t_xs; -static PyObject *__pyx_n_s_t_ys; -static PyObject *__pyx_n_s_test; -static PyObject *__pyx_kp_s_unable_to_allocate_array_data; -static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; -static PyObject *__pyx_n_s_unpack; -static PyObject *__pyx_n_s_update; -static PyObject *__pyx_n_s_values; -static PyObject *__pyx_pf_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_xs, __Pyx_memviewslice __pyx_v_t_ys, float __pyx_v_max_neg_val); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ -static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ -static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ -static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject 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*__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject 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!= 0); - if (__pyx_t_8) { - - /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":26 - * v_cur = value[x, y-1] - * if x == 0: - * if y == 0: # <<<<<<<<<<<<<< - * v_prev = 0. - * else: - */ - __pyx_t_8 = ((__pyx_v_y == 0) != 0); - if (__pyx_t_8) { - - /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":27 - * if x == 0: - * if y == 0: - * v_prev = 0. # <<<<<<<<<<<<<< - * else: - * v_prev = max_neg_val - */ - __pyx_v_v_prev = 0.; - - /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":26 - * v_cur = value[x, y-1] - * if x == 0: - * if y == 0: # <<<<<<<<<<<<<< - * v_prev = 0. - * else: - */ - goto __pyx_L9; - } - - /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":29 - * v_prev = 0. - * else: - * v_prev = max_neg_val # <<<<<<<<<<<<<< - * else: - * v_prev = value[x-1, y-1] - */ - /*else*/ { - __pyx_v_v_prev = __pyx_v_max_neg_val; - } - __pyx_L9:; - - /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":25 - * else: - * v_cur = value[x, y-1] - * if x == 0: # <<<<<<<<<<<<<< - * if 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Py_ssize_t *dst_shape, - */ - -static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - CYTHON_UNUSED Py_ssize_t __pyx_v_i; - CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; - Py_ssize_t __pyx_v_dst_extent; - Py_ssize_t __pyx_v_src_stride; - Py_ssize_t __pyx_v_dst_stride; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - Py_ssize_t __pyx_t_4; - Py_ssize_t __pyx_t_5; - Py_ssize_t __pyx_t_6; - - /* "View.MemoryView":1147 - * - * cdef Py_ssize_t i - * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] - */ - __pyx_v_src_extent = (__pyx_v_src_shape[0]); - - /* "View.MemoryView":1148 - * cdef Py_ssize_t i - * cdef Py_ssize_t src_extent = src_shape[0] - * cdef Py_ssize_t dst_extent = 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(__pyx_t_1) { - - /* "View.MemoryView":1153 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); - if (__pyx_t_2) { - } else { - __pyx_t_1 = __pyx_t_2; - goto __pyx_L5_bool_binop_done; - } - __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); - if (__pyx_t_2) { - } else { - __pyx_t_1 = __pyx_t_2; - goto __pyx_L5_bool_binop_done; - } - - /* "View.MemoryView":1154 - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - */ - __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); - if (__pyx_t_2) { - __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); - } - __pyx_t_3 = (__pyx_t_2 != 0); - __pyx_t_1 = __pyx_t_3; - __pyx_L5_bool_binop_done:; - - /* "View.MemoryView":1153 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - if (__pyx_t_1) { - - /* "View.MemoryView":1155 - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); - - /* "View.MemoryView":1153 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - goto __pyx_L4; - } - - /* "View.MemoryView":1157 - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - */ - /*else*/ { - __pyx_t_4 = __pyx_v_dst_extent; - __pyx_t_5 = __pyx_t_4; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1158 - * else: - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< - * src_data += src_stride - * dst_data += dst_stride - */ - (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); - - /* "View.MemoryView":1159 - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * else: - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1160 - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L4:; - - /* "View.MemoryView":1152 - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - * if ndim == 1: # <<<<<<<<<<<<<< - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - */ - goto __pyx_L3; - } - - /* "View.MemoryView":1162 - * dst_data += dst_stride - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * _copy_strided_to_strided(src_data, src_strides + 1, - * dst_data, dst_strides + 1, - */ - /*else*/ { - __pyx_t_4 = __pyx_v_dst_extent; - __pyx_t_5 = __pyx_t_4; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1163 - * else: - * for i in range(dst_extent): - * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< - * dst_data, dst_strides + 1, - * src_shape + 1, dst_shape + 1, - */ - _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); - - /* "View.MemoryView":1167 - * src_shape + 1, dst_shape + 1, - * ndim - 1, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1168 - * ndim - 1, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L3:; - - /* "View.MemoryView":1140 - * - * @cython.cdivision(True) - * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< - * char *dst_data, Py_ssize_t *dst_strides, - * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, - */ - - /* function exit code */ -} - -/* "View.MemoryView":1170 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - */ - -static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - - /* "View.MemoryView":1173 - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< - * src.shape, dst.shape, ndim, itemsize) - * - */ - _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); - - /* "View.MemoryView":1170 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - */ - - /* function exit code */ -} - -/* "View.MemoryView":1177 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef Py_ssize_t shape, size = src.memview.view.itemsize - */ - -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { - Py_ssize_t __pyx_v_shape; - Py_ssize_t __pyx_v_size; - Py_ssize_t __pyx_r; - Py_ssize_t __pyx_t_1; - Py_ssize_t *__pyx_t_2; - Py_ssize_t *__pyx_t_3; - Py_ssize_t *__pyx_t_4; - - /* "View.MemoryView":1179 - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< - * - * for shape in src.shape[:ndim]: - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_size = __pyx_t_1; - - /* "View.MemoryView":1181 - * cdef Py_ssize_t shape, size = src.memview.view.itemsize - * - * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< - * size *= shape - * - */ - __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); - for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { - __pyx_t_2 = __pyx_t_4; - __pyx_v_shape = (__pyx_t_2[0]); - - /* "View.MemoryView":1182 - * - * for shape in src.shape[:ndim]: - * size *= shape # <<<<<<<<<<<<<< - * - * return size - */ - __pyx_v_size = (__pyx_v_size * __pyx_v_shape); - } - - /* "View.MemoryView":1184 - * size *= shape - * - * return size # <<<<<<<<<<<<<< - * - * @cname('__pyx_fill_contig_strides_array') - */ - __pyx_r = __pyx_v_size; - goto __pyx_L0; - - /* "View.MemoryView":1177 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef Py_ssize_t shape, size = src.memview.view.itemsize - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1187 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) nogil: - */ - -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { - int __pyx_v_idx; - Py_ssize_t __pyx_r; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - int __pyx_t_4; - - /* "View.MemoryView":1196 - * cdef int idx - * - * if order == 'F': # <<<<<<<<<<<<<< - * for idx in range(ndim): - * strides[idx] = stride - */ - __pyx_t_1 = ((__pyx_v_order == 'F') != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":1197 - * - * if order == 'F': - * for idx in range(ndim): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride *= shape[idx] - */ - __pyx_t_2 = __pyx_v_ndim; - __pyx_t_3 = __pyx_t_2; - for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { - __pyx_v_idx = __pyx_t_4; - - /* "View.MemoryView":1198 - * if order == 'F': - * for idx in range(ndim): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride *= shape[idx] - * else: - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1199 - * for idx in range(ndim): - * strides[idx] = stride - * stride *= shape[idx] # <<<<<<<<<<<<<< - * else: - * for idx in range(ndim - 1, -1, -1): - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - - /* "View.MemoryView":1196 - * cdef int idx - * - * if order == 'F': # <<<<<<<<<<<<<< - * for idx in range(ndim): - * strides[idx] = stride - */ - goto __pyx_L3; - } - - /* "View.MemoryView":1201 - * stride *= shape[idx] - * else: - * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride *= shape[idx] - */ - /*else*/ { - for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { - __pyx_v_idx = __pyx_t_2; - - /* "View.MemoryView":1202 - * else: - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride *= shape[idx] - * - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1203 - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride - * stride *= shape[idx] # <<<<<<<<<<<<<< - * - * return stride - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - } - __pyx_L3:; - - /* "View.MemoryView":1205 - * stride *= shape[idx] - * - * return stride # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_copy_data_to_temp') - */ - __pyx_r = __pyx_v_stride; - goto __pyx_L0; - - /* "View.MemoryView":1187 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) nogil: - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1208 - * - * @cname('__pyx_memoryview_copy_data_to_temp') - * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *tmpslice, - * char order, - */ - -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { - int __pyx_v_i; - void *__pyx_v_result; - size_t __pyx_v_itemsize; - size_t __pyx_v_size; - void *__pyx_r; - Py_ssize_t __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - struct __pyx_memoryview_obj *__pyx_t_4; - int __pyx_t_5; - int __pyx_t_6; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; - - /* "View.MemoryView":1219 - * cdef void *result - * - * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< - * cdef size_t size = slice_get_size(src, ndim) - * - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_itemsize = __pyx_t_1; - - /* "View.MemoryView":1220 - * - * cdef size_t itemsize = src.memview.view.itemsize - * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< - * - * result = malloc(size) - */ - __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); - - /* "View.MemoryView":1222 - * cdef size_t size = slice_get_size(src, ndim) - * - * result = malloc(size) # <<<<<<<<<<<<<< - * if not result: - * _err(MemoryError, NULL) - */ - __pyx_v_result = malloc(__pyx_v_size); - - /* "View.MemoryView":1223 - * - * result = malloc(size) - * if not result: # <<<<<<<<<<<<<< - * _err(MemoryError, NULL) - * - */ - __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1224 - * result = malloc(size) - * if not result: - * _err(MemoryError, NULL) # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error) - - /* "View.MemoryView":1223 - * - * result = malloc(size) - * if not result: # <<<<<<<<<<<<<< - * _err(MemoryError, NULL) - * - */ - } - - /* "View.MemoryView":1227 - * - * - * tmpslice.data = result # <<<<<<<<<<<<<< - * tmpslice.memview = src.memview - * for i in range(ndim): - */ - __pyx_v_tmpslice->data = ((char *)__pyx_v_result); - - /* "View.MemoryView":1228 - * - * tmpslice.data = result - * tmpslice.memview = src.memview # <<<<<<<<<<<<<< - * for i in range(ndim): - * tmpslice.shape[i] = src.shape[i] - */ - __pyx_t_4 = __pyx_v_src->memview; - __pyx_v_tmpslice->memview = __pyx_t_4; - - /* "View.MemoryView":1229 - * tmpslice.data = result - * tmpslice.memview = src.memview - * for i in range(ndim): # <<<<<<<<<<<<<< - * tmpslice.shape[i] = src.shape[i] - * tmpslice.suboffsets[i] = -1 - */ - __pyx_t_3 = __pyx_v_ndim; - __pyx_t_5 = __pyx_t_3; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1230 - * tmpslice.memview = src.memview - * for i in range(ndim): - * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< - * tmpslice.suboffsets[i] = -1 - * - */ - (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); - - /* 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dst_ndim, src_ndim) - * - */ - } - __pyx_L3:; - - /* "View.MemoryView":1289 - * broadcast_leading(&dst, dst_ndim, src_ndim) - * - * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< - * - * for i in range(ndim): - */ - __pyx_t_3 = __pyx_v_dst_ndim; - __pyx_t_4 = __pyx_v_src_ndim; - if (((__pyx_t_3 > __pyx_t_4) != 0)) { - __pyx_t_5 = __pyx_t_3; - } else { - __pyx_t_5 = __pyx_t_4; - } - __pyx_v_ndim = __pyx_t_5; - - /* "View.MemoryView":1291 - * cdef int ndim = max(src_ndim, dst_ndim) - * - * for i in range(ndim): # <<<<<<<<<<<<<< - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: - */ - __pyx_t_5 = __pyx_v_ndim; - __pyx_t_3 = __pyx_t_5; - for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { - __pyx_v_i = __pyx_t_4; - - /* "View.MemoryView":1292 - * - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< - * if src.shape[i] == 1: - * broadcasting = True - */ - __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1293 - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: # <<<<<<<<<<<<<< - * broadcasting = True - * src.strides[i] = 0 - */ - __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1294 - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: - * broadcasting = True # <<<<<<<<<<<<<< - * src.strides[i] = 0 - * else: - */ - __pyx_v_broadcasting = 1; - - /* "View.MemoryView":1295 - * if src.shape[i] == 1: - * broadcasting = True - * src.strides[i] = 0 # <<<<<<<<<<<<<< - * else: - * _err_extents(i, dst.shape[i], src.shape[i]) - */ - (__pyx_v_src.strides[__pyx_v_i]) = 0; - - /* "View.MemoryView":1293 - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: # <<<<<<<<<<<<<< - * broadcasting = True - * src.strides[i] = 0 - */ - goto __pyx_L7; - } - - /* "View.MemoryView":1297 - * src.strides[i] = 0 - * else: - * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< - * - * if src.suboffsets[i] >= 0: - */ - /*else*/ { - __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error) - } - __pyx_L7:; - - /* "View.MemoryView":1292 - * - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< - * if src.shape[i] == 1: - * broadcasting = True - */ - } - - /* "View.MemoryView":1299 - * _err_extents(i, dst.shape[i], src.shape[i]) - * - * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - */ - __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1300 - * - * if src.suboffsets[i] >= 0: - * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< - * - * if slices_overlap(&src, &dst, ndim, itemsize): - */ - __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error) - - /* "View.MemoryView":1299 - * _err_extents(i, dst.shape[i], src.shape[i]) - * - * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - */ - } - } - - /* "View.MemoryView":1302 - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< - * - * if not slice_is_contig(src, order, ndim): - */ - __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1304 - * if slices_overlap(&src, &dst, ndim, itemsize): - * - * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< - * order = get_best_order(&dst, ndim) - * - */ - __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1305 - * - * if not slice_is_contig(src, order, ndim): - * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) - */ - __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); - - /* "View.MemoryView":1304 - * if slices_overlap(&src, &dst, ndim, itemsize): - * - * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< - * order = get_best_order(&dst, ndim) - * - */ - } - - /* "View.MemoryView":1307 - * order = get_best_order(&dst, ndim) - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< - * src = tmp - * - */ - __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error) - __pyx_v_tmpdata = __pyx_t_7; - - /* "View.MemoryView":1308 - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) - * src = tmp # <<<<<<<<<<<<<< - * - * if not broadcasting: - */ - __pyx_v_src = __pyx_v_tmp; - - /* "View.MemoryView":1302 - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< - * - * if not slice_is_contig(src, order, ndim): - */ - } - - /* "View.MemoryView":1310 - * src = tmp - * - * if not broadcasting: # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1313 - * - * - * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(dst, 'C', ndim) - * elif slice_is_contig(src, 'F', ndim): - */ - __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1314 - * - * if slice_is_contig(src, 'C', ndim): - * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< - * elif slice_is_contig(src, 'F', ndim): - * direct_copy = slice_is_contig(dst, 'F', ndim) - */ - __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); - - /* "View.MemoryView":1313 - * - * - * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(dst, 'C', ndim) - * elif slice_is_contig(src, 'F', ndim): - */ - goto __pyx_L12; - } - - /* "View.MemoryView":1315 - * if slice_is_contig(src, 'C', ndim): - * direct_copy = slice_is_contig(dst, 'C', ndim) - * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(dst, 'F', ndim) - * - */ - __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1316 - * direct_copy = slice_is_contig(dst, 'C', ndim) - * elif slice_is_contig(src, 'F', ndim): - * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< - * - * if direct_copy: - */ - __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); - - /* "View.MemoryView":1315 - * if slice_is_contig(src, 'C', ndim): - * direct_copy = slice_is_contig(dst, 'C', ndim) - * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(dst, 'F', ndim) - * - */ - } - __pyx_L12:; - - /* "View.MemoryView":1318 - * direct_copy = slice_is_contig(dst, 'F', ndim) - * - * if direct_copy: # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - __pyx_t_2 = (__pyx_v_direct_copy != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1320 - * if direct_copy: - * - * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - - /* "View.MemoryView":1321 - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< - * refcount_copying(&dst, dtype_is_object, ndim, True) - * free(tmpdata) - */ - (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); - - /* "View.MemoryView":1322 - * refcount_copying(&dst, dtype_is_object, ndim, False) - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< - * free(tmpdata) - * return 0 - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - - /* "View.MemoryView":1323 - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) - * free(tmpdata) # <<<<<<<<<<<<<< - * return 0 - * - */ - free(__pyx_v_tmpdata); - - /* "View.MemoryView":1324 - * refcount_copying(&dst, dtype_is_object, ndim, True) - * free(tmpdata) - * return 0 # <<<<<<<<<<<<<< - * - * if order == 'F' == get_best_order(&dst, ndim): - */ - __pyx_r = 0; - goto __pyx_L0; - - /* "View.MemoryView":1318 - * direct_copy = slice_is_contig(dst, 'F', ndim) - * - * if direct_copy: # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - } - - /* "View.MemoryView":1310 - * src = tmp - * - * if not broadcasting: # <<<<<<<<<<<<<< - * - * - */ - } - - /* "View.MemoryView":1326 - * return 0 - * - * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_2 = (__pyx_v_order == 'F'); - if (__pyx_t_2) { - __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); - } - __pyx_t_8 = (__pyx_t_2 != 0); - if (__pyx_t_8) { - - /* "View.MemoryView":1329 - * - * - * transpose_memslice(&src) # <<<<<<<<<<<<<< - * transpose_memslice(&dst) - * - */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) - - /* "View.MemoryView":1330 - * - * transpose_memslice(&src) - * transpose_memslice(&dst) # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error) - - /* "View.MemoryView":1326 - * return 0 - * - * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< - * - * - */ - } - - /* "View.MemoryView":1332 - * transpose_memslice(&dst) - * - * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< - * copy_strided_to_strided(&src, &dst, ndim, itemsize) - * refcount_copying(&dst, dtype_is_object, ndim, True) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - - /* "View.MemoryView":1333 - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< - * refcount_copying(&dst, dtype_is_object, ndim, True) - * - */ - copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); - - /* "View.MemoryView":1334 - * refcount_copying(&dst, dtype_is_object, ndim, False) - * copy_strided_to_strided(&src, &dst, ndim, itemsize) - * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< - * - * free(tmpdata) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - - /* "View.MemoryView":1336 - * refcount_copying(&dst, dtype_is_object, ndim, True) - * - * free(tmpdata) # <<<<<<<<<<<<<< - * return 0 - * - */ - free(__pyx_v_tmpdata); - - /* "View.MemoryView":1337 - * - * free(tmpdata) - * return 0 # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_broadcast_leading') - */ - __pyx_r = 0; - goto __pyx_L0; - - /* "View.MemoryView":1268 - * - * @cname('__pyx_memoryview_copy_contents') - * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice dst, - * int src_ndim, int dst_ndim, - */ - - /* function exit code */ - __pyx_L1_error:; - { - #ifdef WITH_THREAD - PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); - #endif - __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); - #ifdef WITH_THREAD - __Pyx_PyGILState_Release(__pyx_gilstate_save); - #endif - } - __pyx_r = -1; - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1340 - * - * @cname('__pyx_memoryview_broadcast_leading') - * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< - * int ndim, - * int ndim_other) nogil: - */ - -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { - int __pyx_v_i; - int __pyx_v_offset; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - - /* "View.MemoryView":1344 - * int ndim_other) nogil: - * cdef int i - * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< - * - * for i in range(ndim - 1, -1, -1): - */ - __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); - - /* "View.MemoryView":1346 - * cdef int offset = ndim_other - ndim - * - * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * mslice.shape[i + offset] = mslice.shape[i] - * mslice.strides[i + offset] = mslice.strides[i] - */ - for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { - __pyx_v_i = __pyx_t_1; - - /* "View.MemoryView":1347 - * - * for i in range(ndim - 1, -1, -1): - * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< - * mslice.strides[i + offset] = mslice.strides[i] - * mslice.suboffsets[i + offset] = mslice.suboffsets[i] - */ - (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); - - /* "View.MemoryView":1348 - * for i in range(ndim - 1, -1, -1): - * mslice.shape[i + offset] = mslice.shape[i] - * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< - * mslice.suboffsets[i + offset] = mslice.suboffsets[i] - * - */ - (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); - - /* 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"" : "s", num_found); -} - -/* RaiseDoubleKeywords */ -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -/* ParseKeywords */ -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - while (PyDict_Next(kwds, &pos, &key, &value)) { - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = (**name == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* None */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { - PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); -} - -/* GetTopmostException */ -#if CYTHON_USE_EXC_INFO_STACK -static _PyErr_StackItem * -__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) -{ - _PyErr_StackItem *exc_info = tstate->exc_info; - while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && - exc_info->previous_item != NULL) - { - exc_info = exc_info->previous_item; - } - return exc_info; -} -#endif - -/* SaveResetException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - *type = exc_info->exc_type; - *value = exc_info->exc_value; - *tb = exc_info->exc_traceback; - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - #endif - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = type; - exc_info->exc_value = value; - exc_info->exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; icurexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - if (unlikely(PyTuple_Check(err))) - return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); - return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) -#endif -{ - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if CYTHON_USE_EXC_INFO_STACK - { - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = local_type; - exc_info->exc_value = local_value; - exc_info->exc_traceback = local_tb; - } - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* PyObjectCall */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = Py_TYPE(func)->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyErrFetchRestore */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* ArgTypeTest */ -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) -{ - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - else if (exact) { - #if PY_MAJOR_VERSION == 2 - if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(__Pyx_TypeCheck(obj, type))) return 1; - } - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); - return 0; -} - -/* PyCFunctionFastCall */ -#if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - int flags = PyCFunction_GET_FLAGS(func); - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { - return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); - } else { - return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); - } -} -#endif - -/* PyFunctionFastCall */ -#if CYTHON_FAST_PYCALL -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = __Pyx_PyFrame_GetLocalsplus(f); - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif -#endif - -/* PyObjectCall2Args */ -static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { - PyObject *args, *result = NULL; - #if CYTHON_FAST_PYCALL - if (PyFunction_Check(function)) { - PyObject *args[2] = {arg1, arg2}; - return __Pyx_PyFunction_FastCall(function, args, 2); - } - #endif - #if CYTHON_FAST_PYCCALL - if (__Pyx_PyFastCFunction_Check(function)) { - PyObject *args[2] = {arg1, arg2}; - return __Pyx_PyCFunction_FastCall(function, args, 2); - } - #endif - args = PyTuple_New(2); - if (unlikely(!args)) goto done; - Py_INCREF(arg1); - PyTuple_SET_ITEM(args, 0, arg1); - Py_INCREF(arg2); - PyTuple_SET_ITEM(args, 1, arg2); - Py_INCREF(function); - result = __Pyx_PyObject_Call(function, args, NULL); - Py_DECREF(args); - Py_DECREF(function); -done: - return result; -} - -/* PyObjectCallMethO */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ -#if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif - if (likely(PyCFunction_Check(func))) { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (__Pyx_PyFastCFunction_Check(func)) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* BytesEquals */ -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else - if (s1 == s2) { - return (equals == Py_EQ); - } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { - const char *ps1, *ps2; - Py_ssize_t length = PyBytes_GET_SIZE(s1); - if (length != PyBytes_GET_SIZE(s2)) - return (equals == Py_NE); - ps1 = PyBytes_AS_STRING(s1); - ps2 = PyBytes_AS_STRING(s2); - if (ps1[0] != ps2[0]) { - return (equals == Py_NE); - } else if (length == 1) { - return (equals == Py_EQ); - } else { - int result; -#if CYTHON_USE_UNICODE_INTERNALS - Py_hash_t hash1, hash2; - hash1 = ((PyBytesObject*)s1)->ob_shash; - hash2 = ((PyBytesObject*)s2)->ob_shash; - if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { - return (equals == Py_NE); - } -#endif - result = memcmp(ps1, ps2, (size_t)length); - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -#endif -} - -/* UnicodeEquals */ -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else -#if PY_MAJOR_VERSION < 3 - PyObject* owned_ref = NULL; -#endif - int s1_is_unicode, s2_is_unicode; - if (s1 == s2) { - goto return_eq; - } - s1_is_unicode = PyUnicode_CheckExact(s1); - s2_is_unicode = PyUnicode_CheckExact(s2); -#if PY_MAJOR_VERSION < 3 - if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { - owned_ref = PyUnicode_FromObject(s2); - if (unlikely(!owned_ref)) - return -1; - s2 = owned_ref; - s2_is_unicode = 1; - } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { - owned_ref = PyUnicode_FromObject(s1); - if (unlikely(!owned_ref)) - return -1; - s1 = owned_ref; - s1_is_unicode = 1; - } else if (((!s2_is_unicode) & (!s1_is_unicode))) { - return __Pyx_PyBytes_Equals(s1, s2, equals); - } -#endif - if (s1_is_unicode & s2_is_unicode) { - Py_ssize_t length; - int kind; - void *data1, *data2; - if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) - return -1; - length = __Pyx_PyUnicode_GET_LENGTH(s1); - if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { - goto return_ne; - } -#if CYTHON_USE_UNICODE_INTERNALS - { - Py_hash_t hash1, hash2; - #if CYTHON_PEP393_ENABLED - hash1 = ((PyASCIIObject*)s1)->hash; - hash2 = ((PyASCIIObject*)s2)->hash; - #else - hash1 = ((PyUnicodeObject*)s1)->hash; - hash2 = ((PyUnicodeObject*)s2)->hash; - #endif - if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { - goto return_ne; - } - } -#endif - kind = __Pyx_PyUnicode_KIND(s1); - if (kind != __Pyx_PyUnicode_KIND(s2)) { - goto return_ne; - } - data1 = __Pyx_PyUnicode_DATA(s1); - data2 = __Pyx_PyUnicode_DATA(s2); - if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { - goto return_ne; - } else if (length == 1) { - goto return_eq; - } else { - int result = memcmp(data1, data2, (size_t)(length * kind)); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & s2_is_unicode) { - goto return_ne; - } else if ((s2 == Py_None) & s1_is_unicode) { - goto return_ne; - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -return_eq: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ); -return_ne: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_NE); -#endif -} - -/* None */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* GetAttr */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_USE_TYPE_SLOTS -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -/* GetItemInt */ -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyList_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* ObjectGetItem */ -#if CYTHON_USE_TYPE_SLOTS -static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { - PyObject *runerr; - Py_ssize_t key_value; - PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; - if (unlikely(!(m && m->sq_item))) { - PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); - return NULL; - } - key_value = __Pyx_PyIndex_AsSsize_t(index); - if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { - return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); - } - if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { - PyErr_Clear(); - PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); - } - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { - PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; - if (likely(m && m->mp_subscript)) { - return m->mp_subscript(obj, key); - } - return __Pyx_PyObject_GetIndex(obj, key); -} -#endif - -/* decode_c_string */ -static CYTHON_INLINE PyObject* __Pyx_decode_c_string( - const char* cstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - Py_ssize_t length; - if (unlikely((start < 0) | (stop < 0))) { - size_t slen = strlen(cstring); - if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { - PyErr_SetString(PyExc_OverflowError, - "c-string too long to convert to Python"); - return NULL; - } - length = (Py_ssize_t) slen; - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - } - if (unlikely(stop <= start)) - return __Pyx_NewRef(__pyx_empty_unicode); - length = stop - start; - cstring += start; - if (decode_func) { - return decode_func(cstring, length, errors); - } else { - return PyUnicode_Decode(cstring, length, encoding, errors); - } -} - -/* GetAttr3 */ -static PyObject *__Pyx_GetAttr3Default(PyObject *d) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - return NULL; - __Pyx_PyErr_Clear(); - Py_INCREF(d); - return d; -} -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { - PyObject *r = __Pyx_GetAttr(o, n); - return (likely(r)) ? r : __Pyx_GetAttr3Default(d); -} - -/* PyDictVersioning */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { - PyObject **dictptr = NULL; - Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; - if (offset) { -#if CYTHON_COMPILING_IN_CPYTHON - dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); -#else - dictptr = _PyObject_GetDictPtr(obj); -#endif - } - return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; -} -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) - return 0; - return obj_dict_version == __Pyx_get_object_dict_version(obj); -} -#endif - -/* GetModuleGlobalName */ -#if CYTHON_USE_DICT_VERSIONS -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) -#else -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) -#endif -{ - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 - result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } else if (unlikely(PyErr_Occurred())) { - return NULL; - } -#else - result = PyDict_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } -#endif -#else - result = PyObject_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } - PyErr_Clear(); -#endif - return __Pyx_GetBuiltinName(name); -} - -/* RaiseTooManyValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* ExtTypeTest */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -/* SwapException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = *type; - exc_info->exc_value = *value; - exc_info->exc_traceback = *tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - #endif - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); - PyErr_SetExcInfo(*type, *value, *tb); - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#endif - -/* Import */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* FastTypeChecks */ -#if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = a->tp_base; - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; - if (!res) { - res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } - return res; -} -#endif -static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - assert(PyExceptionClass_Check(exc_type)); - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; i= 0 || (x^b) >= 0)) - return PyInt_FromLong(x); - return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a, x; -#ifdef HAVE_LONG_LONG - const PY_LONG_LONG llb = intval; - PY_LONG_LONG lla, llx; -#endif - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - default: return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - } - x = a + b; - return PyLong_FromLong(x); -#ifdef HAVE_LONG_LONG - long_long: - llx = lla + llb; - return PyLong_FromLongLong(llx); -#endif - - - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - double result; - PyFPE_START_PROTECT("add", return NULL) - result = ((double)a) + (double)b; - PyFPE_END_PROTECT(result) - return PyFloat_FromDouble(result); - } - return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); -} -#endif - -/* None */ -static CYTHON_INLINE long __Pyx_div_long(long a, long b) { - long q = a / b; - long r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* ImportFrom */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -/* HasAttr */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { - PyObject *r; - if (unlikely(!__Pyx_PyBaseString_Check(n))) { - PyErr_SetString(PyExc_TypeError, - "hasattr(): attribute name must be string"); - return -1; - } - r = __Pyx_GetAttr(o, n); - if (unlikely(!r)) { - PyErr_Clear(); - return 0; - } else { - Py_DECREF(r); - return 1; - } -} - -/* PyObject_GenericGetAttrNoDict */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'%.50s' object has no attribute '%U'", - tp->tp_name, attr_name); -#else - "'%.50s' object has no attribute '%.400s'", - tp->tp_name, PyString_AS_STRING(attr_name)); -#endif - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { - PyObject *descr; - PyTypeObject *tp = Py_TYPE(obj); - if (unlikely(!PyString_Check(attr_name))) { - return PyObject_GenericGetAttr(obj, attr_name); - } - assert(!tp->tp_dictoffset); - descr = _PyType_Lookup(tp, attr_name); - if (unlikely(!descr)) { - return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); - } - Py_INCREF(descr); - #if PY_MAJOR_VERSION < 3 - if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) - #endif - { - descrgetfunc f = Py_TYPE(descr)->tp_descr_get; - if (unlikely(f)) { - PyObject *res = f(descr, obj, (PyObject *)tp); - Py_DECREF(descr); - return res; - } - } - return descr; -} -#endif - -/* PyObject_GenericGetAttr */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { - if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { - return PyObject_GenericGetAttr(obj, attr_name); - } - return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); -} -#endif - -/* SetVTable */ -static int __Pyx_SetVtable(PyObject *dict, void *vtable) { -#if PY_VERSION_HEX >= 0x02070000 - PyObject *ob = PyCapsule_New(vtable, 0, 0); -#else - PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); -#endif - if (!ob) - goto bad; - if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) - goto bad; - Py_DECREF(ob); - return 0; -bad: - Py_XDECREF(ob); - return -1; -} - -/* PyObjectGetAttrStrNoError */ -static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - __Pyx_PyErr_Clear(); -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { - PyObject *result; -#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 - PyTypeObject* tp = Py_TYPE(obj); - if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { - return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); - } -#endif - result = __Pyx_PyObject_GetAttrStr(obj, attr_name); - if (unlikely(!result)) { - __Pyx_PyObject_GetAttrStr_ClearAttributeError(); - } - return result; -} - -/* SetupReduce */ -static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; -#else - if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; -#endif -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); - if (likely(reduce_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (reduce == object_reduce || PyErr_Occurred()) { - goto __PYX_BAD; - } - setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); - if (likely(setstate_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (!setstate || PyErr_Occurred()) { - goto __PYX_BAD; - } - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto __PYX_GOOD; -__PYX_BAD: - if (!PyErr_Occurred()) - PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); - ret = -1; -__PYX_GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} - -/* TypeImport */ -#ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, - size_t size, enum __Pyx_ImportType_CheckSize check_size) -{ - PyObject *result = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - result = PyObject_GetAttrString(module, class_name); - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if ((size_t)basicsize < size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s size changed, may indicate binary incompatibility. " - "Expected %zd from C header, got %zd from PyObject", - module_name, class_name, size, basicsize); - goto bad; - } - if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s size changed, may indicate binary incompatibility. " - "Expected %zd from C header, got %zd from PyObject", - module_name, class_name, size, basicsize); - goto bad; - } - else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. " - "Expected %zd from C header, got %zd from PyObject", - module_name, class_name, size, basicsize); - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(result); - return NULL; -} -#endif - -/* CLineInTraceback */ -#ifndef CYTHON_CLINE_IN_TRACEBACK -static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { - PyObject *use_cline; - PyObject *ptype, *pvalue, *ptraceback; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject **cython_runtime_dict; -#endif - if (unlikely(!__pyx_cython_runtime)) { - return c_line; - } - __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); -#if CYTHON_COMPILING_IN_CPYTHON - cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); - if (likely(cython_runtime_dict)) { - __PYX_PY_DICT_LOOKUP_IF_MODIFIED( - use_cline, *cython_runtime_dict, - __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) - } else -#endif - { - PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); - if (use_cline_obj) { - use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; - Py_DECREF(use_cline_obj); - } else { - PyErr_Clear(); - use_cline = NULL; - } - } - if (!use_cline) { - c_line = 0; - PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); - } - else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { - c_line = 0; - } - __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); - return c_line; -} -#endif - -/* CodeObjectCache */ -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - if (c_line) { - c_line = __Pyx_CLineForTraceback(tstate, c_line); - } - py_code = __pyx_find_code_object(c_line ? -c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); - } - py_frame = PyFrame_New( - tstate, /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if ((0)) {} - view->obj = NULL; - Py_DECREF(obj); -} -#endif - - -/* MemviewSliceIsContig */ -static int -__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) -{ - int i, index, step, start; - Py_ssize_t itemsize = mvs.memview->view.itemsize; - if (order == 'F') { - step = 1; - start = 0; - } else { - step = -1; - start = ndim - 1; - } - for (i = 0; i < ndim; i++) { - index = start + step * i; - if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) - return 0; - itemsize *= mvs.shape[index]; - } - return 1; -} - -/* OverlappingSlices */ -static void -__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, - void **out_start, void **out_end, - int ndim, size_t itemsize) -{ - char *start, *end; - int i; - start = end = slice->data; - for (i = 0; i < ndim; i++) { - Py_ssize_t stride = slice->strides[i]; - Py_ssize_t extent = slice->shape[i]; - if (extent == 0) { - *out_start = *out_end = start; - return; - } else { - if (stride > 0) - end += stride * (extent - 1); - else - start += stride * (extent - 1); - } - } - *out_start = start; - *out_end = end + itemsize; -} -static int -__pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize) -{ - void *start1, *end1, *start2, *end2; - __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); - __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); - return (start1 < end2) && (start2 < end1); -} - -/* Capsule */ -static CYTHON_INLINE PyObject * -__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) -{ - PyObject *cobj; -#if PY_VERSION_HEX >= 0x02070000 - cobj = PyCapsule_New(p, sig, NULL); -#else - cobj = PyCObject_FromVoidPtr(p, NULL); -#endif - return cobj; -} - -/* IsLittleEndian */ -static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) -{ - union { - uint32_t u32; - uint8_t u8[4]; - } S; - S.u32 = 0x01020304; - return S.u8[0] == 4; -} - -/* BufferFormatCheck */ -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t <= '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case '?': return "'bool'"; - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? "'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number, ndim; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ndim = ctx->head->field->type->ndim; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - CYTHON_FALLTHROUGH; - case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && - (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - CYTHON_FALLTHROUGH; - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} - -/* TypeInfoCompare */ - static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -/* MemviewSliceValidateAndInit */ - static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (unlikely(buf->strides[dim] != sizeof(void *))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (unlikely(buf->strides[dim] != buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (unlikely(stride < buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - } else { - if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (unlikely(buf->suboffsets)) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) -{ - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessible " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; - } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (unlikely(buf->ndim != ndim)) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; - } - if (unlikely((unsigned) buf->itemsize != dtype->size)) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->len > 0) { - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) - goto fail; - if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) - goto fail; - } - if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) - goto fail; - } - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, - &__Pyx_TypeInfo_float, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = (float)(1.0) / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = (float)(1.0) / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - return __Pyx_c_prod_float(a, a); - case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); - case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = powf(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2f(0.0, -1.0); - } - } else { - r = __Pyx_c_abs_float(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = (double)(1.0) / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = (double)(1.0) / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0.0, -1.0); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* MemviewSliceCopyTemplate */ - static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object) -{ - __Pyx_RefNannyDeclarations - int i; - __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; - struct __pyx_memoryview_obj *from_memview = from_mvs->memview; - Py_buffer *buf = &from_memview->view; - PyObject *shape_tuple = NULL; - PyObject *temp_int = NULL; - struct __pyx_array_obj *array_obj = NULL; - struct __pyx_memoryview_obj *memview_obj = NULL; - __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); - for (i = 0; i < ndim; i++) { - if (unlikely(from_mvs->suboffsets[i] >= 0)) { - PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " - "indirect dimensions (axis %d)", i); - goto fail; - } - } - shape_tuple = PyTuple_New(ndim); - if (unlikely(!shape_tuple)) { - goto fail; - } - __Pyx_GOTREF(shape_tuple); - for(i = 0; i < ndim; i++) { - temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); - if(unlikely(!temp_int)) { - goto fail; - } else { - PyTuple_SET_ITEM(shape_tuple, i, temp_int); - temp_int = NULL; - } - } - array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); - if (unlikely(!array_obj)) { - goto fail; - } - __Pyx_GOTREF(array_obj); - memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - (PyObject *) array_obj, contig_flag, - dtype_is_object, - from_mvs->memview->typeinfo); - if (unlikely(!memview_obj)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) - goto fail; - if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, - dtype_is_object) < 0)) - goto fail; - goto no_fail; -fail: - __Pyx_XDECREF(new_mvs.memview); - new_mvs.memview = NULL; - new_mvs.data = NULL; -no_fail: - __Pyx_XDECREF(shape_tuple); - __Pyx_XDECREF(temp_int); - __Pyx_XDECREF(array_obj); - __Pyx_RefNannyFinishContext(); - return new_mvs; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const char neg_one = (char) -1, const_zero = (char) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(char) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (char) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (char) 0; - case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) - case 2: - if (8 * sizeof(char) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { - return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(char) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { - return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(char) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { - return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (char) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(char) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (char) 0; - case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) - case -2: - if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(char) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { - return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(char) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); 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-#else - char val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (char) -1; - } - } else { - char val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (char) -1; - val = __Pyx_PyInt_As_char(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to char"); - return (char) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; -} - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - if (PyObject_Hash(*t->p) == -1) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -#if !CYTHON_PEP393_ENABLED -static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -} -#else -static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (likely(PyUnicode_IS_ASCII(o))) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -} -#endif -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { - return __Pyx_PyUnicode_AsStringAndSize(o, length); - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { - int retval; - if (unlikely(!x)) return -1; - retval = __Pyx_PyObject_IsTrue(x); - Py_DECREF(x); - return retval; -} -static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { -#if PY_MAJOR_VERSION >= 3 - if (PyLong_Check(result)) { - if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, - "__int__ returned non-int (type %.200s). " - "The ability to return an instance of a strict subclass of int " - "is deprecated, and may be removed in a future version of Python.", - Py_TYPE(result)->tp_name)) { - Py_DECREF(result); - return NULL; - } - return result; - } -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - type_name, type_name, Py_TYPE(result)->tp_name); - Py_DECREF(result); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x) || PyLong_Check(x))) -#else - if (likely(PyLong_Check(x))) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = m->nb_int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = m->nb_long(x); - } - #else - if (likely(m && m->nb_int)) { - name = "int"; - res = m->nb_int(x); - } - #endif -#else - if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { - res = PyNumber_Int(x); - } -#endif - if (likely(res)) { -#if PY_MAJOR_VERSION < 3 - if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { -#else - if (unlikely(!PyLong_CheckExact(res))) { -#endif - return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(b); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { - return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/TTS/tts/utils/speakers.py b/TTS/tts/utils/speakers.py index 1b9ab96f..13696a20 100644 --- a/TTS/tts/utils/speakers.py +++ b/TTS/tts/utils/speakers.py @@ -63,7 +63,6 @@ class SpeakerManager: use_cuda: bool = False, ): - self.data_items = [] self.d_vectors = {} self.speaker_ids = {} self.clip_ids = [] @@ -72,7 +71,7 @@ class SpeakerManager: self.use_cuda = use_cuda if data_items: - self.speaker_ids, self.speaker_names, _ = self.parse_speakers_from_data(self.data_items) + self.speaker_ids, _ = self.parse_speakers_from_data(data_items) if d_vectors_file_path: self.set_d_vectors_from_file(d_vectors_file_path) @@ -110,10 +109,10 @@ class SpeakerManager: @staticmethod def parse_speakers_from_data(items: list) -> Tuple[Dict, int]: - """Parse speaker IDs from data samples retured by `load_meta_data()`. + """Parse speaker IDs from data samples retured by `load_tts_samples()`. Args: - items (list): Data sampled returned by `load_meta_data()`. + items (list): Data sampled returned by `load_tts_samples()`. Returns: Tuple[Dict, int]: speaker IDs and number of speakers. @@ -127,7 +126,7 @@ class SpeakerManager: """Set speaker IDs from data samples. Args: - items (List): Data sampled returned by `load_meta_data()`. + items (List): Data sampled returned by `load_tts_samples()`. """ self.speaker_ids, _ = self.parse_speakers_from_data(items) diff --git a/TTS/tts/utils/ssim.py b/TTS/tts/utils/ssim.py index caed575f..883efdb8 100644 --- a/TTS/tts/utils/ssim.py +++ b/TTS/tts/utils/ssim.py @@ -23,8 +23,10 @@ def _ssim(img1, img2, window, window_size, channel, size_average=True): mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) + # TODO: check if you need AMP disabled + # with torch.cuda.amp.autocast(enabled=False): + mu1_sq = mu1.float().pow(2) + mu2_sq = mu2.float().pow(2) mu1_mu2 = mu1 * mu2 sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq diff --git a/TTS/tts/utils/synthesis.py b/TTS/tts/utils/synthesis.py index ca15f4cc..5185139e 100644 --- a/TTS/tts/utils/synthesis.py +++ b/TTS/tts/utils/synthesis.py @@ -172,7 +172,7 @@ def speaker_id_to_torch(speaker_id, cuda=False): def embedding_to_torch(d_vector, cuda=False): if d_vector is not None: d_vector = np.asarray(d_vector) - d_vector = torch.from_numpy(d_vector).unsqueeze(0).type(torch.FloatTensor) + d_vector = torch.from_numpy(d_vector).type(torch.FloatTensor) if cuda: return d_vector.cuda() return d_vector @@ -210,20 +210,42 @@ def synthesis( d_vector=None, backend="torch", ): - """Synthesize voice for the given text. + """Synthesize voice for the given text using Griffin-Lim vocoder or just compute output features to be passed to + the vocoder model. Args: - model (TTS.tts.models): model to synthesize. - text (str): target text - CONFIG (dict): config dictionary to be loaded from config.json. - use_cuda (bool): enable cuda. - ap (TTS.tts.utils.audio.AudioProcessor): audio processor to process - model outputs. - speaker_id (int): id of speaker - style_wav (str | Dict[str, float]): Uses for style embedding of GST. - enable_eos_bos_chars (bool): enable special chars for end of sentence and start of sentence. - do_trim_silence (bool): trim silence after synthesis. - backend (str): tf or torch + model (TTS.tts.models): + The TTS model to synthesize audio with. + + text (str): + The input text to convert to speech. + + CONFIG (Coqpit): + Model configuration. + + use_cuda (bool): + Enable/disable CUDA. + + ap (TTS.tts.utils.audio.AudioProcessor): + The audio processor for extracting features and pre/post-processing audio. + + speaker_id (int): + Speaker ID passed to the speaker embedding layer in multi-speaker model. Defaults to None. + + style_wav (str | Dict[str, float]): + Path or tensor to/of a waveform used for computing the style embedding. Defaults to None. + + enable_eos_bos_chars (bool): + enable special chars for end of sentence and start of sentence. Defaults to False. + + do_trim_silence (bool): + trim silence after synthesis. Defaults to False. + + d_vector (torch.Tensor): + d-vector for multi-speaker models in share :math:`[1, D]`. Defaults to None. + + backend (str): + tf or torch. Defaults to "torch". """ # GST processing style_mel = None diff --git a/TTS/utils/audio.py b/TTS/utils/audio.py index f5fb1d7f..dd9c5701 100644 --- a/TTS/utils/audio.py +++ b/TTS/utils/audio.py @@ -108,6 +108,8 @@ class TorchSTFT(nn.Module): # pylint: disable=abstract-method class AudioProcessor(object): """Audio Processor for TTS used by all the data pipelines. + TODO: Make this a dataclass to replace `BaseAudioConfig`. + Note: All the class arguments are set to default values to enable a flexible initialization of the class with the model config. They are not meaningful for all the arguments. @@ -643,6 +645,10 @@ class AudioProcessor(object): >>> wav = ap.load_wav(WAV_FILE, sr=22050)[:5 * 22050] >>> pitch = ap.compute_f0(wav) """ + # align F0 length to the spectrogram length + if len(x) % self.hop_length == 0: + x = np.pad(x, (0, self.hop_length // 2), mode="reflect") + f0, t = pw.dio( x.astype(np.double), fs=self.sample_rate, @@ -745,6 +751,14 @@ class AudioProcessor(object): wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav)))) scipy.io.wavfile.write(path, sr if sr else self.sample_rate, wav_norm.astype(np.int16)) + def get_duration(self, filename: str) -> float: + """Get the duration of a wav file using Librosa. + + Args: + filename (str): Path to the wav file. + """ + return librosa.get_duration(filename) + @staticmethod def mulaw_encode(wav: np.ndarray, qc: int) -> np.ndarray: mu = 2 ** qc - 1 diff --git a/TTS/utils/download.py b/TTS/utils/download.py new file mode 100644 index 00000000..5cfb69cd --- /dev/null +++ b/TTS/utils/download.py @@ -0,0 +1,185 @@ +# Adapted from https://github.com/pytorch/audio/ + +import hashlib +import logging +import os +import tarfile +import urllib +import urllib.request +import zipfile +from typing import Any, Iterable, List, Optional + +from torch.utils.model_zoo import tqdm + + +def stream_url( + url: str, start_byte: Optional[int] = None, block_size: int = 32 * 1024, progress_bar: bool = True +) -> Iterable: + """Stream url by chunk + + Args: + url (str): Url. + start_byte (int or None, optional): Start streaming at that point (Default: ``None``). + block_size (int, optional): Size of chunks to stream (Default: ``32 * 1024``). + progress_bar (bool, optional): Display a progress bar (Default: ``True``). + """ + + # If we already have the whole file, there is no need to download it again + req = urllib.request.Request(url, method="HEAD") + with urllib.request.urlopen(req) as response: + url_size = int(response.info().get("Content-Length", -1)) + if url_size == start_byte: + return + + req = urllib.request.Request(url) + if start_byte: + req.headers["Range"] = "bytes={}-".format(start_byte) + + with urllib.request.urlopen(req) as upointer, tqdm( + unit="B", + unit_scale=True, + unit_divisor=1024, + total=url_size, + disable=not progress_bar, + ) as pbar: + + num_bytes = 0 + while True: + chunk = upointer.read(block_size) + if not chunk: + break + yield chunk + num_bytes += len(chunk) + pbar.update(len(chunk)) + + +def download_url( + url: str, + download_folder: str, + filename: Optional[str] = None, + hash_value: Optional[str] = None, + hash_type: str = "sha256", + progress_bar: bool = True, + resume: bool = False, +) -> None: + """Download file to disk. + + Args: + url (str): Url. + download_folder (str): Folder to download file. + filename (str or None, optional): Name of downloaded file. If None, it is inferred from the url + (Default: ``None``). + hash_value (str or None, optional): Hash for url (Default: ``None``). + hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). + progress_bar (bool, optional): Display a progress bar (Default: ``True``). + resume (bool, optional): Enable resuming download (Default: ``False``). + """ + + req = urllib.request.Request(url, method="HEAD") + req_info = urllib.request.urlopen(req).info() # pylint: disable=consider-using-with + + # Detect filename + filename = filename or req_info.get_filename() or os.path.basename(url) + filepath = os.path.join(download_folder, filename) + if resume and os.path.exists(filepath): + mode = "ab" + local_size: Optional[int] = os.path.getsize(filepath) + + elif not resume and os.path.exists(filepath): + raise RuntimeError("{} already exists. Delete the file manually and retry.".format(filepath)) + else: + mode = "wb" + local_size = None + + if hash_value and local_size == int(req_info.get("Content-Length", -1)): + with open(filepath, "rb") as file_obj: + if validate_file(file_obj, hash_value, hash_type): + return + raise RuntimeError("The hash of {} does not match. Delete the file manually and retry.".format(filepath)) + + with open(filepath, mode) as fpointer: + for chunk in stream_url(url, start_byte=local_size, progress_bar=progress_bar): + fpointer.write(chunk) + + with open(filepath, "rb") as file_obj: + if hash_value and not validate_file(file_obj, hash_value, hash_type): + raise RuntimeError("The hash of {} does not match. Delete the file manually and retry.".format(filepath)) + + +def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") -> bool: + """Validate a given file object with its hash. + + Args: + file_obj: File object to read from. + hash_value (str): Hash for url. + hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). + + Returns: + bool: return True if its a valid file, else False. + """ + + if hash_type == "sha256": + hash_func = hashlib.sha256() + elif hash_type == "md5": + hash_func = hashlib.md5() + else: + raise ValueError + + while True: + # Read by chunk to avoid filling memory + chunk = file_obj.read(1024 ** 2) + if not chunk: + break + hash_func.update(chunk) + + return hash_func.hexdigest() == hash_value + + +def extract_archive(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]: + """Extract archive. + Args: + from_path (str): the path of the archive. + to_path (str or None, optional): the root path of the extraced files (directory of from_path) + (Default: ``None``) + overwrite (bool, optional): overwrite existing files (Default: ``False``) + + Returns: + list: List of paths to extracted files even if not overwritten. + """ + + if to_path is None: + to_path = os.path.dirname(from_path) + + try: + with tarfile.open(from_path, "r") as tar: + logging.info("Opened tar file %s.", from_path) + files = [] + for file_ in tar: # type: Any + file_path = os.path.join(to_path, file_.name) + if file_.isfile(): + files.append(file_path) + if os.path.exists(file_path): + logging.info("%s already extracted.", file_path) + if not overwrite: + continue + tar.extract(file_, to_path) + return files + except tarfile.ReadError: + pass + + try: + with zipfile.ZipFile(from_path, "r") as zfile: + logging.info("Opened zip file %s.", from_path) + files = zfile.namelist() + for file_ in files: + file_path = os.path.join(to_path, file_) + if os.path.exists(file_path): + logging.info("%s already extracted.", file_path) + if not overwrite: + continue + zfile.extract(file_, to_path) + return files + except zipfile.BadZipFile: + pass + + raise NotImplementedError(" > [!] only supports tar.gz, tgz, and zip achives.") diff --git a/TTS/utils/downloaders.py b/TTS/utils/downloaders.py new file mode 100644 index 00000000..89f2148f --- /dev/null +++ b/TTS/utils/downloaders.py @@ -0,0 +1,33 @@ +import os + +from TTS.utils.download import download_url, extract_archive + + +def download_ljspeech(path: str): + """Download and extract LJSpeech dataset + + Args: + path (str): path to the directory where the dataset will be stored. + """ + os.makedirs(path, exist_ok=True) + url = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2" + download_url(url, path) + basename = os.path.basename(url) + archive = os.path.join(path, basename) + print(" > Extracting archive file...") + extract_archive(archive) + + +def download_vctk(path: str): + """Download and extract VCTK dataset + + Args: + path (str): path to the directory where the dataset will be stored. + """ + os.makedirs(path, exist_ok=True) + url = "https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip" + download_url(url, path) + basename = os.path.basename(url) + archive = os.path.join(path, basename) + print(" > Extracting archive file...") + extract_archive(archive) diff --git a/TTS/utils/io.py b/TTS/utils/io.py index dd4ffd60..a93f6118 100644 --- a/TTS/utils/io.py +++ b/TTS/utils/io.py @@ -38,7 +38,8 @@ def copy_model_files(config: Coqpit, out_path, new_fields): """ copy_config_path = os.path.join(out_path, "config.json") # add extra information fields - config.update(new_fields, allow_new=True) + if new_fields: + config.update(new_fields, allow_new=True) # TODO: Revert to config.save_json() once Coqpit supports arbitrary paths. with fsspec.open(copy_config_path, "w", encoding="utf8") as f: json.dump(config.to_dict(), f, indent=4) diff --git a/TTS/utils/logging/console_logger.py b/TTS/utils/logging/console_logger.py index 0c1aa862..74371342 100644 --- a/TTS/utils/logging/console_logger.py +++ b/TTS/utils/logging/console_logger.py @@ -47,11 +47,19 @@ class ConsoleLogger: tcolors.BOLD, step, batch_steps, global_step, tcolors.ENDC ) for key, value in loss_dict.items(): - # print the avg value if given if f"avg_{key}" in avg_loss_dict.keys(): - log_text += "{}{}: {:.5f} ({:.5f})\n".format(indent, key, value, avg_loss_dict[f"avg_{key}"]) + # print the avg value if given + if isinstance(value, float) and round(value, 5) == 0: + # do not round the number if it is zero when rounded + log_text += "{}{}: {} ({})\n".format(indent, key, value, avg_loss_dict[f"avg_{key}"]) + else: + # print the rounded value + log_text += "{}{}: {:.5f} ({:.5f})\n".format(indent, key, value, avg_loss_dict[f"avg_{key}"]) else: - log_text += "{}{}: {:.5f} \n".format(indent, key, value) + if isinstance(value, float) and round(value, 5) == 0: + log_text += "{}{}: {} \n".format(indent, key, value) + else: + log_text += "{}{}: {:.5f} \n".format(indent, key, value) print(log_text, flush=True) # pylint: disable=unused-argument diff --git a/TTS/utils/synthesizer.py b/TTS/utils/synthesizer.py index 236e78a9..af07419f 100644 --- a/TTS/utils/synthesizer.py +++ b/TTS/utils/synthesizer.py @@ -87,52 +87,15 @@ class Synthesizer(object): """ return pysbd.Segmenter(language=lang, clean=True) - def _load_speakers(self, speaker_file: str) -> None: - """Load the SpeakerManager to organize multi-speaker TTS. It loads the speakers meta-data and the speaker - encoder if it is defined. - - Args: - speaker_file (str): path to the speakers meta-data file. - """ - print("Loading speakers ...") - self.speaker_manager = SpeakerManager( - encoder_model_path=self.encoder_checkpoint, encoder_config_path=self.encoder_config - ) - self.speaker_manager.load_d_vectors_file(self.tts_config.get("d_vector_file", speaker_file)) - self.num_speakers = self.speaker_manager.num_speakers - self.d_vector_dim = self.speaker_manager.d_vector_dim - - def _set_tts_speaker_file(self): - """Set the TTS speaker file used by a multi-speaker model.""" - # setup if multi-speaker settings are in the global model config - if hasattr(self.tts_config, "use_speaker_embedding") and self.tts_config.use_speaker_embedding is True: - if self.tts_config.use_d_vector_file: - self.tts_speakers_file = ( - self.tts_speakers_file if self.tts_speakers_file else self.tts_config["d_vector_file"] - ) - self.tts_config["d_vector_file"] = self.tts_speakers_file - else: - self.tts_speakers_file = ( - self.tts_speakers_file if self.tts_speakers_file else self.tts_config["speakers_file"] - ) - - # setup if multi-speaker settings are in the model args config - if ( - self.tts_speakers_file is None - and hasattr(self.tts_config, "model_args") - and hasattr(self.tts_config.model_args, "use_speaker_embedding") - and self.tts_config.model_args.use_speaker_embedding - ): - _args = self.tts_config.model_args - if _args.use_d_vector_file: - self.tts_speakers_file = self.tts_speakers_file if self.tts_speakers_file else _args["d_vector_file"] - _args["d_vector_file"] = self.tts_speakers_file - else: - self.tts_speakers_file = self.tts_speakers_file if self.tts_speakers_file else _args["speakers_file"] - def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None: """Load the TTS model. + 1. Load the model config. + 2. Init the AudioProcessor. + 3. Init the model from the config. + 4. Move the model to the GPU if CUDA is enabled. + 5. Init the speaker manager for the model. + Args: tts_checkpoint (str): path to the model checkpoint. tts_config_path (str): path to the model config file. @@ -144,15 +107,38 @@ class Synthesizer(object): self.use_phonemes = self.tts_config.use_phonemes self.ap = AudioProcessor(verbose=False, **self.tts_config.audio) - self.tts_model = setup_tts_model(config=self.tts_config) + speaker_manager = self._init_speaker_manager() + + self.tts_model = setup_tts_model(config=self.tts_config, speaker_manager=speaker_manager) self.tts_model.load_checkpoint(self.tts_config, tts_checkpoint, eval=True) if use_cuda: self.tts_model.cuda() - self._set_tts_speaker_file() + + def _init_speaker_manager(self): + """Initialize the SpeakerManager""" + # setup if multi-speaker settings are in the global model config + speaker_manager = None + if hasattr(self.tts_config, "use_speaker_embedding") and self.tts_config.use_speaker_embedding is True: + if self.tts_speakers_file: + speaker_manager = SpeakerManager(speaker_id_file_path=self.tts_speakers_file) + if self.tts_config.get("speakers_file", None): + speaker_manager = SpeakerManager(speaker_id_file_path=self.tts_config.speakers_file) + + if hasattr(self.tts_config, "use_d_vector_file") and self.tts_config.use_speaker_embedding is True: + if self.tts_speakers_file: + speaker_manager = SpeakerManager(d_vectors_file_path=self.tts_speakers_file) + if self.tts_config.get("d_vector_file", None): + speaker_manager = SpeakerManager(d_vectors_file_path=self.tts_config.d_vector_file) + return speaker_manager def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None: """Load the vocoder model. + 1. Load the vocoder config. + 2. Init the AudioProcessor for the vocoder. + 3. Init the vocoder model from the config. + 4. Move the model to the GPU if CUDA is enabled. + Args: model_file (str): path to the model checkpoint. model_config (str): path to the model config file. @@ -207,11 +193,12 @@ class Synthesizer(object): # handle multi-speaker speaker_embedding = None speaker_id = None - if self.tts_speakers_file: + if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "speaker_ids"): if speaker_idx and isinstance(speaker_idx, str): if self.tts_config.use_d_vector_file: # get the speaker embedding from the saved d_vectors. speaker_embedding = self.tts_model.speaker_manager.get_d_vectors_by_speaker(speaker_idx)[0] + speaker_embedding = np.array(speaker_embedding)[None, :] # [1 x embedding_dim] else: # get speaker idx from the speaker name speaker_id = self.tts_model.speaker_manager.speaker_ids[speaker_idx] @@ -226,7 +213,7 @@ class Synthesizer(object): else: if speaker_idx: raise ValueError( - f" [!] Missing speaker.json file path for selecting speaker {speaker_idx}." + f" [!] Missing speakers.json file path for selecting speaker {speaker_idx}." "Define path for speaker.json if it is a multi-speaker model or remove defined speaker idx. " ) diff --git a/TTS/utils/trainer_utils.py b/TTS/utils/trainer_utils.py index 005114d1..dabb33cd 100644 --- a/TTS/utils/trainer_utils.py +++ b/TTS/utils/trainer_utils.py @@ -1,8 +1,13 @@ import importlib +import os +import re from typing import Dict, List, Tuple +from urllib.parse import urlparse +import fsspec import torch +from TTS.utils.io import load_fsspec from TTS.utils.training import NoamLR @@ -80,3 +85,66 @@ def get_optimizer( if model is not None: parameters = model.parameters() return optimizer(parameters, lr=lr, **optimizer_params) + + +def get_last_checkpoint(path: str) -> Tuple[str, str]: + """Get latest checkpoint or/and best model in path. + + It is based on globbing for `*.pth.tar` and the RegEx + `(checkpoint|best_model)_([0-9]+)`. + + Args: + path: Path to files to be compared. + + Raises: + ValueError: If no checkpoint or best_model files are found. + + Returns: + Path to the last checkpoint + Path to best checkpoint + """ + fs = fsspec.get_mapper(path).fs + file_names = fs.glob(os.path.join(path, "*.pth.tar")) + scheme = urlparse(path).scheme + if scheme: # scheme is not preserved in fs.glob, add it back + file_names = [scheme + "://" + file_name for file_name in file_names] + last_models = {} + last_model_nums = {} + for key in ["checkpoint", "best_model"]: + last_model_num = None + last_model = None + # pass all the checkpoint files and find + # the one with the largest model number suffix. + for file_name in file_names: + match = re.search(f"{key}_([0-9]+)", file_name) + if match is not None: + model_num = int(match.groups()[0]) + if last_model_num is None or model_num > last_model_num: + last_model_num = model_num + last_model = file_name + + # if there is no checkpoint found above + # find the checkpoint with the latest + # modification date. + key_file_names = [fn for fn in file_names if key in fn] + if last_model is None and len(key_file_names) > 0: + last_model = max(key_file_names, key=os.path.getctime) + last_model_num = load_fsspec(last_model)["step"] + + if last_model is not None: + last_models[key] = last_model + last_model_nums[key] = last_model_num + + # check what models were found + if not last_models: + raise ValueError(f"No models found in continue path {path}!") + if "checkpoint" not in last_models: # no checkpoint just best model + last_models["checkpoint"] = last_models["best_model"] + elif "best_model" not in last_models: # no best model + # this shouldn't happen, but let's handle it just in case + last_models["best_model"] = last_models["checkpoint"] + # finally check if last best model is more recent than checkpoint + elif last_model_nums["best_model"] > last_model_nums["checkpoint"]: + last_models["checkpoint"] = last_models["best_model"] + + return last_models["checkpoint"], last_models["best_model"] diff --git a/TTS/vocoder/configs/shared_configs.py b/TTS/vocoder/configs/shared_configs.py index 6891ce6c..c5d6a8b4 100644 --- a/TTS/vocoder/configs/shared_configs.py +++ b/TTS/vocoder/configs/shared_configs.py @@ -1,7 +1,5 @@ from dataclasses import dataclass, field -from coqpit import MISSING - from TTS.config import BaseAudioConfig, BaseTrainingConfig @@ -17,11 +15,11 @@ class BaseVocoderConfig(BaseTrainingConfig): Number of instances used for evaluation. Defaults to 10. data_path (str): Root path of the training data. All the audio files found recursively from this root path are used for - training. Defaults to MISSING. + training. Defaults to `""`. feature_path (str): Root path to the precomputed feature files. Defaults to None. seq_len (int): - Length of the waveform segments used for training. Defaults to MISSING. + Length of the waveform segments used for training. Defaults to 1000. pad_short (int): Extra padding for the waveforms shorter than `seq_len`. Defaults to 0. conv_path (int): @@ -45,9 +43,9 @@ class BaseVocoderConfig(BaseTrainingConfig): use_noise_augment: bool = False # enable/disable random noise augmentation in spectrograms. eval_split_size: int = 10 # number of samples used for evaluation. # dataset - data_path: str = MISSING # root data path. It finds all wav files recursively from there. + data_path: str = "" # root data path. It finds all wav files recursively from there. feature_path: str = None # if you use precomputed features - seq_len: int = MISSING # signal length used in training. + seq_len: int = 1000 # signal length used in training. pad_short: int = 0 # additional padding for short wavs conv_pad: int = 0 # additional padding against convolutions applied to spectrograms use_cache: bool = False # use in memory cache to keep the computed features. This might cause OOM. diff --git a/TTS/vocoder/configs/wavernn_config.py b/TTS/vocoder/configs/wavernn_config.py index 0afa1f43..f39400e5 100644 --- a/TTS/vocoder/configs/wavernn_config.py +++ b/TTS/vocoder/configs/wavernn_config.py @@ -75,7 +75,7 @@ class WavernnConfig(BaseVocoderConfig): model: str = "wavernn" # Model specific params - model_params: WavernnArgs = field(default_factory=WavernnArgs) + model_args: WavernnArgs = field(default_factory=WavernnArgs) target_loss: str = "loss" # Inference diff --git a/TTS/vocoder/datasets/__init__.py b/TTS/vocoder/datasets/__init__.py index 86b059c3..871eb0d2 100644 --- a/TTS/vocoder/datasets/__init__.py +++ b/TTS/vocoder/datasets/__init__.py @@ -5,6 +5,7 @@ from torch.utils.data import Dataset from TTS.utils.audio import AudioProcessor from TTS.vocoder.datasets.gan_dataset import GANDataset +from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset diff --git a/TTS/vocoder/datasets/preprocess.py b/TTS/vocoder/datasets/preprocess.py index 62bd4ba5..d8cc350a 100644 --- a/TTS/vocoder/datasets/preprocess.py +++ b/TTS/vocoder/datasets/preprocess.py @@ -29,9 +29,7 @@ def preprocess_wav_files(out_path: str, config: Coqpit, ap: AudioProcessor): mel = ap.melspectrogram(y) np.save(mel_path, mel) if isinstance(config.mode, int): - quant = ( - ap.mulaw_encode(y, qc=config.mode) if config.model_params.mulaw else ap.quantize(y, bits=config.mode) - ) + quant = ap.mulaw_encode(y, qc=config.mode) if config.model_args.mulaw else ap.quantize(y, bits=config.mode) np.save(quant_path, quant) diff --git a/TTS/vocoder/models/base_vocoder.py b/TTS/vocoder/models/base_vocoder.py index f879cd42..9d6ef26f 100644 --- a/TTS/vocoder/models/base_vocoder.py +++ b/TTS/vocoder/models/base_vocoder.py @@ -1,3 +1,5 @@ +from coqpit import Coqpit + from TTS.model import BaseModel # pylint: skip-file @@ -16,5 +18,35 @@ class BaseVocoder(BaseModel): - 1D tensors `batch x 1` """ - def __init__(self): - super().__init__() + def __init__(self, config): + super().__init__(config) + + def _set_model_args(self, config: Coqpit): + """Setup model args based on the config type. + + If the config is for training with a name like "*Config", then the model args are embeded in the + config.model_args + + If the config is for the model with a name like "*Args", then we assign the directly. + """ + # don't use isintance not to import recursively + if "Config" in config.__class__.__name__: + if "characters" in config: + _, self.config, num_chars = self.get_characters(config) + self.config.num_chars = num_chars + if hasattr(self.config, "model_args"): + config.model_args.num_chars = num_chars + if "model_args" in config: + self.args = self.config.model_args + # This is for backward compatibility + if "model_params" in config: + self.args = self.config.model_params + else: + self.config = config + if "model_args" in config: + self.args = self.config.model_args + # This is for backward compatibility + if "model_params" in config: + self.args = self.config.model_params + else: + raise ValueError("config must be either a *Config or *Args") diff --git a/TTS/vocoder/models/gan.py b/TTS/vocoder/models/gan.py index f203c533..81ba87c4 100644 --- a/TTS/vocoder/models/gan.py +++ b/TTS/vocoder/models/gan.py @@ -35,7 +35,7 @@ class GAN(BaseVocoder): >>> config = HifiganConfig() >>> model = GAN(config) """ - super().__init__() + super().__init__(config) self.config = config self.model_g = setup_generator(config) self.model_d = setup_discriminator(config) @@ -197,18 +197,24 @@ class GAN(BaseVocoder): audios = {f"{name}/audio": sample_voice} return figures, audios - def train_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]: + def train_log( + self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int # pylint: disable=unused-argument + ) -> Tuple[Dict, np.ndarray]: """Call `_log()` for training.""" - return self._log("train", ap, batch, outputs) + ap = assets["audio_processor"] + self._log("train", ap, batch, outputs) @torch.no_grad() def eval_step(self, batch: Dict, criterion: nn.Module, optimizer_idx: int) -> Tuple[Dict, Dict]: """Call `train_step()` with `no_grad()`""" return self.train_step(batch, criterion, optimizer_idx) - def eval_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]: + def eval_log( + self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int # pylint: disable=unused-argument + ) -> Tuple[Dict, np.ndarray]: """Call `_log()` for evaluation.""" - return self._log("eval", ap, batch, outputs) + ap = assets["audio_processor"] + self._log("eval", ap, batch, outputs) def load_checkpoint( self, @@ -299,7 +305,7 @@ class GAN(BaseVocoder): def get_data_loader( # pylint: disable=no-self-use self, config: Coqpit, - ap: AudioProcessor, + assets: Dict, is_eval: True, data_items: List, verbose: bool, @@ -318,6 +324,7 @@ class GAN(BaseVocoder): Returns: DataLoader: Torch dataloader. """ + ap = assets["audio_processor"] dataset = GANDataset( ap=ap, items=data_items, diff --git a/TTS/vocoder/models/wavegrad.py b/TTS/vocoder/models/wavegrad.py index 8d95a063..ed4f4b37 100644 --- a/TTS/vocoder/models/wavegrad.py +++ b/TTS/vocoder/models/wavegrad.py @@ -9,7 +9,6 @@ from torch.nn.utils import weight_norm from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from TTS.utils.audio import AudioProcessor from TTS.utils.io import load_fsspec from TTS.utils.trainer_utils import get_optimizer, get_scheduler from TTS.vocoder.datasets import WaveGradDataset @@ -58,7 +57,7 @@ class Wavegrad(BaseVocoder): # pylint: disable=dangerous-default-value def __init__(self, config: Coqpit): - super().__init__() + super().__init__(config) self.config = config self.use_weight_norm = config.model_params.use_weight_norm self.hop_len = np.prod(config.model_params.upsample_factors) @@ -258,21 +257,22 @@ class Wavegrad(BaseVocoder): return {"model_output": noise_hat}, {"loss": loss} def train_log( # pylint: disable=no-self-use - self, ap: AudioProcessor, batch: Dict, outputs: Dict # pylint: disable=unused-argument + self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int # pylint: disable=unused-argument ) -> Tuple[Dict, np.ndarray]: - return None, None + pass @torch.no_grad() def eval_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]: return self.train_step(batch, criterion) def eval_log( # pylint: disable=no-self-use - self, ap: AudioProcessor, batch: Dict, outputs: Dict # pylint: disable=unused-argument - ) -> Tuple[Dict, np.ndarray]: - return None, None + self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int # pylint: disable=unused-argument + ) -> None: + pass - def test_run(self, ap: AudioProcessor, samples: List[Dict], ouputs: Dict): # pylint: disable=unused-argument + def test_run(self, assets: Dict, samples: List[Dict], outputs: Dict): # pylint: disable=unused-argument # setup noise schedule and inference + ap = assets["audio_processor"] noise_schedule = self.config["test_noise_schedule"] betas = np.linspace(noise_schedule["min_val"], noise_schedule["max_val"], noise_schedule["num_steps"]) self.compute_noise_level(betas) @@ -307,8 +307,9 @@ class Wavegrad(BaseVocoder): return {"input": m, "waveform": y} def get_data_loader( - self, config: Coqpit, ap: AudioProcessor, is_eval: True, data_items: List, verbose: bool, num_gpus: int + self, config: Coqpit, assets: Dict, is_eval: True, data_items: List, verbose: bool, num_gpus: int ): + ap = assets["audio_processor"] dataset = WaveGradDataset( ap=ap, items=data_items, diff --git a/TTS/vocoder/models/wavernn.py b/TTS/vocoder/models/wavernn.py index 9b0d6837..1977efb6 100644 --- a/TTS/vocoder/models/wavernn.py +++ b/TTS/vocoder/models/wavernn.py @@ -222,10 +222,7 @@ class Wavernn(BaseVocoder): samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency. """ - super().__init__() - - self.args = config.model_params - self.config = config + super().__init__(config) if isinstance(self.args.mode, int): self.n_classes = 2 ** self.args.mode @@ -572,8 +569,9 @@ class Wavernn(BaseVocoder): @torch.no_grad() def test_run( - self, ap: AudioProcessor, samples: List[Dict], output: Dict # pylint: disable=unused-argument + self, assets: Dict, samples: List[Dict], output: Dict # pylint: disable=unused-argument ) -> Tuple[Dict, Dict]: + ap = assets["audio_processor"] figures = {} audios = {} for idx, sample in enumerate(samples): @@ -600,20 +598,21 @@ class Wavernn(BaseVocoder): def get_data_loader( # pylint: disable=no-self-use self, config: Coqpit, - ap: AudioProcessor, + assets: Dict, is_eval: True, data_items: List, verbose: bool, num_gpus: int, ): + ap = assets["audio_processor"] dataset = WaveRNNDataset( ap=ap, items=data_items, seq_len=config.seq_len, hop_len=ap.hop_length, - pad=config.model_params.pad, - mode=config.model_params.mode, - mulaw=config.model_params.mulaw, + pad=config.model_args.pad, + mode=config.model_args.mode, + mulaw=config.model_args.mulaw, is_training=not is_eval, verbose=verbose, ) diff --git a/docs/source/conf.py b/docs/source/conf.py index 09c44b8f..a9fa6133 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -39,20 +39,6 @@ master_doc = "index" # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = [ -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'TODO/*'] - -source_suffix = [".rst", ".md"] - -# extensions extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', @@ -68,6 +54,17 @@ extensions = [ "sphinx_inline_tabs", ] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'TODO/*'] + +source_suffix = [".rst", ".md"] + myst_enable_extensions = ['linkify',] # 'sphinxcontrib.katex', diff --git a/docs/source/finetuning.md b/docs/source/finetuning.md index 35cd6f19..fd9a295c 100644 --- a/docs/source/finetuning.md +++ b/docs/source/finetuning.md @@ -38,7 +38,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways: You can list the availabe models on terminal as ```bash - tts --list-models + tts --list_models ``` The command above lists the the models in a naming format as ```///```. diff --git a/docs/source/implementing_a_new_model.md b/docs/source/implementing_a_new_model.md index c0043bf1..176c4865 100644 --- a/docs/source/implementing_a_new_model.md +++ b/docs/source/implementing_a_new_model.md @@ -10,7 +10,7 @@ We keep tests under `tests` folder. You can add `tts` layers tests under `tts_tests` folder. Basic tests are checking input-output tensor shapes and output values for a given input. Consider testing extreme cases that are more likely to cause problems like `zero` tensors. -3. Implement loss function. +3. Implement a loss function. We keep loss functions under `TTS/tts/layers/losses.py`. You can also mix-and-match implemented loss functions as you like. @@ -29,19 +29,20 @@ A model interacts with the `Trainer API` for training, `Synthesizer API` for inference and testing. - A 🐸TTS model must return a dictionary by the `forward()` and `inference()` functions. This dictionary must also include the `model_outputs` key that is considered as the main model output by the `Trainer` and `Synthesizer`. + A 🐸TTS model must return a dictionary by the `forward()` and `inference()` functions. This dictionary must `model_outputs` key that is considered as the main model output by the `Trainer` and `Synthesizer`. You can place your `tts` model implementation under `TTS/tts/models/new_model.py` then inherit and implement the `BaseTTS`. There is also the `callback` interface by which you can manipulate both the model and the `Trainer` states. Callbacks give you - the infinite flexibility to add custom behaviours for your model and training routines. + an infinite flexibility to add custom behaviours for your model and training routines. For more details, see {ref}`BaseTTS ` and :obj:`TTS.utils.callbacks`. 6. Optionally, define `MyModelArgs`. - `MyModelArgs` is a 👨‍✈️Coqpit class that sets all the class arguments of the `MyModel`. It should be enough to pass - an `MyModelArgs` instance to initiate the `MyModel`. + `MyModelArgs` is a 👨‍✈️Coqpit class that sets all the class arguments of the `MyModel`. `MyModelArgs` must have + all the fields neccessary to instantiate the `MyModel`. However, for training, you need to pass `MyModelConfig` to + the model. 7. Test `MyModel`. @@ -59,3 +60,149 @@ 9. Write Docstrings. We love you more when you document your code. ❤️ + + +# Template 🐸TTS Model implementation + +You can start implementing your model by copying the following base class. + +```python +from TTS.tts.models.base_tts import BaseTTS + + +class MyModel(BaseTTS): + """ + Notes on input/output tensor shapes: + Any input or output tensor of the model must be shaped as + + - 3D tensors `batch x time x channels` + - 2D tensors `batch x channels` + - 1D tensors `batch x 1` + """ + + def __init__(self, config: Coqpit): + super().__init__() + self._set_model_args(config) + + def _set_model_args(self, config: Coqpit): + """Set model arguments from the config. Override this.""" + pass + + def forward(self, input: torch.Tensor, *args, aux_input={}, **kwargs) -> Dict: + """Forward pass for the model mainly used in training. + + You can be flexible here and use different number of arguments and argument names since it is intended to be + used by `train_step()` without exposing it out of the model. + + Args: + input (torch.Tensor): Input tensor. + aux_input (Dict): Auxiliary model inputs like embeddings, durations or any other sorts of inputs. + + Returns: + Dict: Model outputs. Main model output must be named as "model_outputs". + """ + outputs_dict = {"model_outputs": None} + ... + return outputs_dict + + def inference(self, input: torch.Tensor, aux_input={}) -> Dict: + """Forward pass for inference. + + We don't use `*kwargs` since it is problematic with the TorchScript API. + + Args: + input (torch.Tensor): [description] + aux_input (Dict): Auxiliary inputs like speaker embeddings, durations etc. + + Returns: + Dict: [description] + """ + outputs_dict = {"model_outputs": None} + ... + return outputs_dict + + def train_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]: + """Perform a single training step. Run the model forward pass and compute losses. + + Args: + batch (Dict): Input tensors. + criterion (nn.Module): Loss layer designed for the model. + + Returns: + Tuple[Dict, Dict]: Model ouputs and computed losses. + """ + outputs_dict = {} + loss_dict = {} # this returns from the criterion + ... + return outputs_dict, loss_dict + + def train_log(self, batch: Dict, outputs: Dict, logger: "Logger", assets:Dict, steps:int) -> None: + """Create visualizations and waveform examples for training. + + For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to + be projected onto Tensorboard. + + Args: + ap (AudioProcessor): audio processor used at training. + batch (Dict): Model inputs used at the previous training step. + outputs (Dict): Model outputs generated at the previoud training step. + + Returns: + Tuple[Dict, np.ndarray]: training plots and output waveform. + """ + pass + + def eval_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]: + """Perform a single evaluation step. Run the model forward pass and compute losses. In most cases, you can + call `train_step()` with no changes. + + Args: + batch (Dict): Input tensors. + criterion (nn.Module): Loss layer designed for the model. + + Returns: + Tuple[Dict, Dict]: Model ouputs and computed losses. + """ + outputs_dict = {} + loss_dict = {} # this returns from the criterion + ... + return outputs_dict, loss_dict + + def eval_log(self, batch: Dict, outputs: Dict, logger: "Logger", assets:Dict, steps:int) -> None: + """The same as `train_log()`""" + pass + + def load_checkpoint(self, config: Coqpit, checkpoint_path: str, eval: bool = False) -> None: + """Load a checkpoint and get ready for training or inference. + + Args: + config (Coqpit): Model configuration. + checkpoint_path (str): Path to the model checkpoint file. + eval (bool, optional): If true, init model for inference else for training. Defaults to False. + """ + ... + + def get_optimizer(self) -> Union["Optimizer", List["Optimizer"]]: + """Setup an return optimizer or optimizers.""" + pass + + def get_lr(self) -> Union[float, List[float]]: + """Return learning rate(s). + + Returns: + Union[float, List[float]]: Model's initial learning rates. + """ + pass + + def get_scheduler(self, optimizer: torch.optim.Optimizer): + pass + + def get_criterion(self): + pass + + def format_batch(self): + pass + +``` + + diff --git a/docs/source/index.md b/docs/source/index.md index d5f77ad4..756cea8e 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -47,6 +47,7 @@ models/glow_tts.md models/vits.md models/forward_tts.md + models/tacotron1-2.md .. toctree:: :maxdepth: 2 diff --git a/docs/source/models/tacotron1-2.md b/docs/source/models/tacotron1-2.md new file mode 100644 index 00000000..90833ecb --- /dev/null +++ b/docs/source/models/tacotron1-2.md @@ -0,0 +1,63 @@ +# 🌮 Tacotron 1 and 2 + +Tacotron is one of the first successful DL-based text-to-mel models and opened up the whole TTS field for more DL research. + +Tacotron mainly is an encoder-decoder model with attention. + +The encoder takes input tokens (characters or phonemes) and the decoder outputs mel-spectrogram* frames. Attention module in-between learns to align the input tokens with the output mel-spectrgorams. + +Tacotron1 and 2 are both built on the same encoder-decoder architecture but they use different layers. Additionally, Tacotron1 uses a Postnet module to convert mel-spectrograms to linear spectrograms with a higher resolution before the vocoder. + +Vanilla Tacotron models are slow at inference due to the auto-regressive* nature that prevents the model to process all the inputs in parallel. One trick is to use a higher “reduction rate” that helps the model to predict multiple frames at once. That is, reduction rate 2 reduces the number of decoder iterations by half. + +Tacotron also uses a Prenet module with Dropout that projects the model’s previous output before feeding it to the decoder again. The paper and most of the implementations use the Dropout layer even in inference and they report the attention fails or the voice quality degrades otherwise. But the issue with that, you get a slightly different output speech every time you run the model. + +Tsraining the attention is notoriously problematic in Tacoron models. Especially, in inference, for some input sequences, the alignment fails and causes the model to produce unexpected results. There are many different methods proposed to improve the attention. + +After hundreds of experiments, @ 🐸TTS we suggest Double Decoder Consistency that leads to the most robust model performance. + +If you have a limited VRAM, then you can try using the Guided Attention Loss or the Dynamic Convolutional Attention. You can also combine the two. + + +## Important resources & papers +- Tacotron: https://arxiv.org/abs/2006.06873 +- Tacotron2: https://arxiv.org/abs/2008.03802 +- Double Decoder Consistency: https://coqui.ai/blog/tts/solving-attention-problems-of-tts-models-with-double-decoder-consistency +- Guided Attention Loss: https://arxiv.org/abs/1710.08969 +- Forward & Backward Decoder: https://arxiv.org/abs/1907.09006 +- Forward Attention: https://arxiv.org/abs/1807.06736 +- Gaussian Attention: https://arxiv.org/abs/1910.10288 +- Dynamic Convolutional Attention: https://arxiv.org/pdf/1910.10288.pdf + + +## BaseTacotron +```{eval-rst} +.. autoclass:: TTS.tts.models.base_tacotron.BaseTacotron + :members: +``` + +## Tacotron +```{eval-rst} +.. autoclass:: TTS.tts.models.tacotron.Tacotron + :members: +``` + +## Tacotron2 +```{eval-rst} +.. autoclass:: TTS.tts.models.tacotron2.Tacotron2 + :members: +``` + +## TacotronConfig +```{eval-rst} +.. autoclass:: TTS.tts.configs.tacotron_config.TacotronConfig + :members: +``` + +## Tacotron2Config +```{eval-rst} +.. autoclass:: TTS.tts.configs.tacotron2_config.Tacotron2Config + :members: +``` + + diff --git a/docs/source/training_a_model.md b/docs/source/training_a_model.md index aadd741e..a28710d0 100644 --- a/docs/source/training_a_model.md +++ b/docs/source/training_a_model.md @@ -1,66 +1,33 @@ # Training a Model -1. Decide what model you want to use. +1. Decide the model you want to use. Each model has a different set of pros and cons that define the run-time efficiency and the voice quality. It is up to you to decide what model servers your needs. Other than referring to the papers, one easy way is to test the 🐸TTS community models and see how fast and good each of the models. Or you can start a discussion on our communication channels. -2. Understand the configuration class, its fields and values of your model. +2. Understand the configuration, its fields and values. For instance, if you want to train a `Tacotron` model then see the `TacotronConfig` class and make sure you understand it. -3. Go to the recipes and check the recipe of your target model. +3. Check the recipes. - Recipes do not promise perfect models but they provide a good start point for `Nervous Beginners`. A recipe script training - a `GlowTTS` model on `LJSpeech` dataset looks like below. Let's be creative and call this script `train_glowtts.py`. + Recipes are located under `TTS/recipes/`. They do not promise perfect models but they provide a good start point for + `Nervous Beginners`. + A recipe for `GlowTTS` using `LJSpeech` dataset looks like below. Let's be creative and call this `train_glowtts.py`. - ```python - # train_glowtts.py - - import os - - from TTS.tts.configs import GlowTTSConfig - from TTS.tts.configs import BaseDatasetConfig - from TTS.trainer import init_training, Trainer, TrainingArgs - - - output_path = os.path.dirname(os.path.abspath(__file__)) - dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")) - config = GlowTTSConfig( - batch_size=32, - eval_batch_size=16, - num_loader_workers=4, - num_eval_loader_workers=4, - run_eval=True, - test_delay_epochs=-1, - epochs=1000, - text_cleaner="english_cleaners", - use_phonemes=False, - phoneme_language="en-us", - phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), - print_step=25, - print_eval=True, - mixed_precision=False, - output_path=output_path, - datasets=[dataset_config] - ) - args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) - trainer = Trainer(args, config, output_path, c_logger, tb_logger) - trainer.fit() + ```{literalinclude} ../../recipes/ljspeech/glow_tts/train_glowtts.py ``` - You need to change fields of the `BaseDatasetConfig` to match your own dataset and then update `GlowTTSConfig` + You need to change fields of the `BaseDatasetConfig` to match your dataset and then update `GlowTTSConfig` fields as you need. 4. Run the training. - You need to run the training script. - ```bash $ CUDA_VISIBLE_DEVICES="0" python train_glowtts.py ``` - Notice that you set the GPU you want to use on your system by setting `CUDA_VISIBLE_DEVICES` environment variable. + Notice that we set the GPU for the training by `CUDA_VISIBLE_DEVICES` environment variable. To see available GPUs on your system, you can use `nvidia-smi` command on the terminal. If you like to run a multi-gpu training using DDP back-end, @@ -71,7 +38,7 @@ The example above runs a multi-gpu training using GPUs `0, 1, 2`. - The beginning of a training run looks like below. + Beginning of a training log looks like this: ```console > Experiment folder: /your/output_path/-Juni-23-2021_02+52-78899209 @@ -140,11 +107,11 @@ $ tensorboard --logdir= ``` -6. Check the logs and the Tensorboard and monitor the training. +6. Monitor the training progress. - On the terminal and Tensorboard, you can monitor the losses and their changes over time. Also Tensorboard provides certain figures and sample outputs. + On the terminal and Tensorboard, you can monitor the progress of your model. Also Tensorboard provides certain figures and sample outputs. - Note that different models have different metrics, visuals and outputs to be displayed. + Note that different models have different metrics, visuals and outputs. You should also check the [FAQ page](https://github.com/coqui-ai/TTS/wiki/FAQ) for common problems and solutions that occur in a training. @@ -163,3 +130,17 @@ 8. Return to the step 1 and reiterate for training a `vocoder` model. In the example above, we trained a `GlowTTS` model, but the same workflow applies to all the other 🐸TTS models. + + +# Multi-speaker Training + +Training a multi-speaker model is mostly the same as training a single-speaker model. +You need to specify a couple of configuration parameters, initiate a `SpeakerManager` instance and pass it to the model. + +The configuration parameters define whether you want to train the model with a speaker-embedding layer or pre-computed +d-vectors. For using d-vectors, you first need to compute the d-vectors using the `SpeakerEncoder`. + +The same Glow-TTS model above can be trained on a multi-speaker VCTK dataset with the script below. + +```{literalinclude} ../../recipes/vctk/glow_tts/train_glow_tts.py +``` \ No newline at end of file diff --git a/docs/source/tutorial_for_nervous_beginners.md b/docs/source/tutorial_for_nervous_beginners.md index a81e8fa7..fa09cb7d 100644 --- a/docs/source/tutorial_for_nervous_beginners.md +++ b/docs/source/tutorial_for_nervous_beginners.md @@ -18,68 +18,50 @@ $ pip install -e . ## Training a `tts` Model -A breakdown of a simple script training a GlowTTS model on LJspeech dataset. See the comments for the explanation of -each line. +A breakdown of a simple script that trains a GlowTTS model on the LJspeech dataset. See the comments for more details. ### Pure Python Way -```python -import os +0. Download your dataset. -# GlowTTSConfig: all model related values for training, validating and testing. -from TTS.tts.configs import GlowTTSConfig + In this example, we download and use the LJSpeech dataset. Set the download directory based on your preferences. -# BaseDatasetConfig: defines name, formatter and path of the dataset. -from TTS.tts.configs import BaseDatasetConfig + ```bash + $ python -c 'from TTS.utils.downloaders import download_ljspeech; download_ljspeech("../recipes/ljspeech/");' + ``` -# init_training: Initialize and setup the training environment. -# Trainer: Where the ✨️ happens. -# TrainingArgs: Defines the set of arguments of the Trainer. -from TTS.trainer import init_training, Trainer, TrainingArgs +1. Define `train.py`. -# we use the same path as this script as our training folder. -output_path = os.path.dirname(os.path.abspath(__file__)) + ```{literalinclude} ../../recipes/ljspeech/glow_tts/train_glowtts.py + ``` -# set LJSpeech as our target dataset and define its path so that the Trainer knows what data formatter it needs. -dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")) +2. Run the script. -# Configure the model. Every config class inherits the BaseTTSConfig to have all the fields defined for the Trainer. -config = GlowTTSConfig( - batch_size=32, - eval_batch_size=16, - num_loader_workers=4, - num_eval_loader_workers=4, - run_eval=True, - test_delay_epochs=-1, - epochs=1000, - text_cleaner="english_cleaners", - use_phonemes=False, - phoneme_language="en-us", - phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), - print_step=25, - print_eval=True, - mixed_precision=False, - output_path=output_path, - datasets=[dataset_config] -) + ```bash + CUDA_VISIBLE_DEVICES=0 python train.py + ``` -# Take the config and the default Trainer arguments, setup the training environment and override the existing -# config values from the terminal. So you can do the following. -# >>> python train.py --coqpit.batch_size 128 -args, config, output_path, _, _, _= init_training(TrainingArgs(), config) + - Continue a previous run. -# Initiate the Trainer. -# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training, -# distributed training etc. -trainer = Trainer(args, config, output_path) + ```bash + CUDA_VISIBLE_DEVICES=0 python train.py --continue_path path/to/previous/run/folder/ + ``` -# And kick it 🚀 -trainer.fit() -``` + - Fine-tune a model. + + ```bash + CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth.tar + ``` + + - Run multi-gpu training. + + ```bash + CUDA_VISIBLE_DEVICES=0,1,2 python TTS/bin/distribute.py --script train.py + ``` ### CLI Way -We still support running training from CLI like in the old days. The same training can be started as follows. +We still support running training from CLI like in the old days. The same training run can also be started as follows. 1. Define your `config.json` @@ -111,45 +93,12 @@ We still support running training from CLI like in the old days. The same traini $ CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path config.json ``` - - ## Training a `vocoder` Model -```python -import os - -from TTS.vocoder.configs import HifiganConfig -from TTS.trainer import init_training, Trainer, TrainingArgs - - -output_path = os.path.dirname(os.path.abspath(__file__)) -config = HifiganConfig( - batch_size=32, - eval_batch_size=16, - num_loader_workers=4, - num_eval_loader_workers=4, - run_eval=True, - test_delay_epochs=-1, - epochs=1000, - seq_len=8192, - pad_short=2000, - use_noise_augment=True, - eval_split_size=10, - print_step=25, - print_eval=True, - mixed_precision=False, - lr_gen=1e-4, - lr_disc=1e-4, - # `vocoder` only needs a data path and they read recursively all the `.wav` files underneath. - data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"), - output_path=output_path, -) -args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, tb_logger) -trainer.fit() +```{literalinclude} ../../recipes/ljspeech/hifigan/train_hifigan.py ``` -❗️ Note that you can also start the training run from CLI as the `tts` model above. +❗️ Note that you can also use ```train_vocoder.py``` as the ```tts``` models above. ## Synthesizing Speech diff --git a/notebooks/Chinese_Mandarin_DDC_GST_Tacotron2_TTS_and_MultiBand_MelGAN_Example_Synthetizer.ipynb b/notebooks/Chinese_Mandarin_DDC_GST_Tacotron2_TTS_and_MultiBand_MelGAN_Example_Synthetizer.ipynb deleted file mode 100644 index 1be93a82..00000000 --- a/notebooks/Chinese_Mandarin_DDC_GST_Tacotron2_TTS_and_MultiBand_MelGAN_Example_Synthetizer.ipynb +++ /dev/null @@ -1,606 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6LWsNd3_M3MP" - }, - "source": [ - "# Mozilla TTS on CPU Real-Time Speech Synthesis " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "FAqrSIWgLyP0" - }, - "source": [ - "We use Tacotron2 and MultiBand-Melgan models and Baker dataset (chinese mandarin).\n", - "\n", - "Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 126K steps (3 days) with a single GPU.\n", - "\n", - "MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n", - "\n", - "Note that both model performances can be improved with more training." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ku-dA4DKoeXk" - }, - "source": [ - "### Download Models" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Zlgi8fPdpRF0" - }, - "source": [ - "### Define TTS function" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ZksegYQepkFg" - }, - "source": [ - "### Load Models" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "oVa0kOamprgj" - }, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import IPython\n", - "\n", - "from TTS.utils.synthesizer import Synthesizer\n", - "from TTS.utils.manage import ModelManager\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EY-sHVO8IFSH" - }, - "outputs": [], - "source": [ - "# runtime settings\n", - "use_cuda = False" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# tts and vocoder name\n", - "TTS_NAME = \"tts_models/zh-CN/baker/tacotron2-DDC-GST\"\n", - "VOCODER_NAME = \"vocoder_models/en/ljspeech/multiband-melgan\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "manager = ModelManager(\"../TTS/.models.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > tts_models/zh-CN/baker/tacotron2-DDC-GST is already downloaded.\n", - " > vocoder_models/en/ljspeech/multiband-melgan is already downloaded.\n" - ] - } - ], - "source": [ - "tts_checkpoint_file, tts_config_file, tts_json_dict = manager.download_model(TTS_NAME)\n", - "vocoder_checkpoint_file, vocoder_config_file, vocoder_json_dict = manager.download_model(VOCODER_NAME)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Using model: tacotron2\n", - " > Generator Model: multiband_melgan_generator\n" - ] - } - ], - "source": [ - "synthesizer = Synthesizer(tts_checkpoint_file, tts_config_file, vocoder_checkpoint_file, vocoder_config_file, use_cuda)\n", - "sample_rate = synthesizer.tts_config.audio[\"sample_rate\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ws_YkPKsLgo-" - }, - "source": [ - "## Run Inference" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Here some test sentences for you to play with :\n", - "sentences= [\"我从来不会说很标准的中文。\",\n", - "\"我喜欢听人工智能的博客。\",\n", - "\"我来自一个法国郊区的地方。\",\n", - "\"不比不知道，一比吓一跳！\",\n", - "\"台湾是一个真的很好玩的地方！\",\n", - "\"干一行，行一行，行行都行。\",\n", - "\"我要盖被子，好尴尬！\",]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我从来不会说很标准的中文。']\n", - " > Processing time: 1.6665124893188477\n", - " > Real-time factor: 0.5583910829911347\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 1.4052538871765137\n", - " > Real-time factor: 0.5193391025114328\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我来自一个法国郊区的地方。']\n", - " > Processing time: 1.605910062789917\n", - " > Real-time factor: 0.5785999490934259\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['不比不知道，一比吓一跳！']\n", - " > Processing time: 1.9105627536773682\n", - " > Real-time factor: 0.6607262973429417\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['台湾是一个真的很好玩的地方！']\n", - " > Processing time: 1.3081049919128418\n", - " > Real-time factor: 0.4218891158389621\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['干一行，行一行，行行都行。']\n", - " > Processing time: 2.0958540439605713\n", - " > Real-time factor: 0.6709288860239634\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我要盖被子，好尴尬！']\n", - " > Processing time: 1.5188167095184326\n", - " > Real-time factor: 0.6257456734843319\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for sentence in sentences:\n", - " wav = synthesizer.tts(sentence)\n", - " IPython.display.display(IPython.display.Audio(wav, rate=sample_rate)) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 2.114016056060791\n", - " > Real-time factor: 0.643271887228699\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# you can also play with Global Style Token (GST) by feeding a \n", - "# ... wav_style parameter to the tts method\n", - "\n", - "style_wav = {\"2\": 0.2}\n", - "\n", - "wav = synthesizer.tts(sentences[1], style_wav=style_wav)\n", - "IPython.display.display(IPython.display.Audio(wav, rate=sample_rate)) " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 1.5687272548675537\n", - " > Real-time factor: 0.6401842606201799\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 2.070594072341919\n", - " > Real-time factor: 0.8067677285683367\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 1.3769311904907227\n", - " > Real-time factor: 0.5088718951180015\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 2.024374485015869\n", - " > Real-time factor: 0.6782983435843654\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " > Text splitted to sentences.\n", - "['我喜欢听人工智能的博客。']\n", - " > Processing time: 2.4434399604797363\n", - " > Real-time factor: 0.7435119663360867\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# On this model specifically, we can observe that the GSToken \"2\" is responsible for speech speed\n", - "# You can listen to these 5 different samples, the flow is slower and slower as the value is higher\n", - "for value in [-0.2, -0.1, 0, 0.1, 0.2]:\n", - " style_wav = {\"2\": value}\n", - " wav = synthesizer.tts(sentences[1], style_wav=style_wav)\n", - " IPython.display.display(IPython.display.Audio(wav, rate=sample_rate)) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "name": "DDC-TTS_and_MultiBand-MelGAN_Example.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/DDC_TTS_and_MultiBand_MelGAN_Example.ipynb b/notebooks/DDC_TTS_and_MultiBand_MelGAN_Example.ipynb deleted file mode 100644 index 67171b0e..00000000 --- a/notebooks/DDC_TTS_and_MultiBand_MelGAN_Example.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6LWsNd3_M3MP" - }, - "source": [ - "# Mozilla TTS on CPU Real-Time Speech Synthesis " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "FAqrSIWgLyP0" - }, - "source": [ - "We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n", - "\n", - "Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n", - "\n", - "MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n", - "\n", - "Note that both model performances can be improved with more training." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ku-dA4DKoeXk" - }, - "source": [ - "### Download Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162 - }, - "colab_type": "code", - "id": "jGIgnWhGsxU1", - "outputId": "88725e41-a8dc-4885-b3bf-cac939f38abe", - "tags": [] - }, - "outputs": [], - "source": [ - "!gdown --id 1dntzjWFg7ufWaTaFy80nRz-Tu02xWZos -O data/tts_model.pth.tar\n", - "!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "colab_type": "code", - "id": "4dnpE0-kvTsu", - "outputId": "76377c6d-789c-4995-ba00-a21a6e1c401e", - "tags": [] - }, - "outputs": [], - "source": [ - "!gdown --id 1Ty5DZdOc0F7OTGj9oJThYbL5iVu_2G0K -O data/vocoder_model.pth.tar\n", - "!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n", - "!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Zlgi8fPdpRF0" - }, - "source": [ - "### Define TTS function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "f-Yc42nQZG5A" - }, - "outputs": [], - "source": [ - "def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n", - " t_1 = time.time()\n", - " waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n", - " truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n", - " # mel_postnet_spec = ap.denormalize(mel_postnet_spec.T)\n", - " if not use_gl:\n", - " waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n", - " waveform = waveform.flatten()\n", - " if use_cuda:\n", - " waveform = waveform.cpu()\n", - " waveform = waveform.numpy()\n", - " rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n", - " tps = (time.time() - t_1) / len(waveform)\n", - " print(waveform.shape)\n", - " print(\" > Run-time: {}\".format(time.time() - t_1))\n", - " print(\" > Real-time factor: {}\".format(rtf))\n", - " print(\" > Time per step: {}\".format(tps))\n", - " IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n", - " return alignment, mel_postnet_spec, stop_tokens, waveform" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ZksegYQepkFg" - }, - "source": [ - "### Load Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "oVa0kOamprgj" - }, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import time\n", - "import IPython\n", - "\n", - "from TTS.tts.utils.generic_utils import setup_model\n", - "from TTS.utils.io import load_config\n", - "from TTS.tts.utils.text.symbols import symbols, phonemes\n", - "from TTS.utils.audio import AudioProcessor\n", - "from TTS.tts.utils.synthesis import synthesis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EY-sHVO8IFSH" - }, - "outputs": [], - "source": [ - "# runtime settings\n", - "use_cuda = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_1aIUp2FpxOQ" - }, - "outputs": [], - "source": [ - "# model paths\n", - "TTS_MODEL = \"data/tts_model.pth.tar\"\n", - "TTS_CONFIG = \"data/config.json\"\n", - "VOCODER_MODEL = \"data/vocoder_model.pth.tar\"\n", - "VOCODER_CONFIG = \"data/config_vocoder.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "CpgmdBVQplbv" - }, - "outputs": [], - "source": [ - "# load configs\n", - "TTS_CONFIG = load_config(TTS_CONFIG)\n", - "VOCODER_CONFIG = load_config(VOCODER_CONFIG)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 471 - }, - "colab_type": "code", - "id": "zmrQxiozIUVE", - "outputId": "60c4daa0-4c5b-4a2e-fe0d-be437d003a49", - "tags": [] - }, - "outputs": [], - "source": [ - "# load the audio processor\n", - "TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n", - "ap = AudioProcessor(**TTS_CONFIG.audio) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "colab_type": "code", - "id": "8fLoI4ipqMeS", - "outputId": "b789066e-e305-42ad-b3ca-eba8d9267382", - "tags": [] - }, - "outputs": [], - "source": [ - "# LOAD TTS MODEL\n", - "# multi speaker \n", - "speaker_id = None\n", - "speakers = []\n", - "\n", - "# load the model\n", - "num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n", - "model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n", - "\n", - "# load model state\n", - "cp = torch.load(TTS_MODEL, map_location=torch.device('cpu'))\n", - "\n", - "# load the model\n", - "model.load_state_dict(cp['model'])\n", - "if use_cuda:\n", - " model.cuda()\n", - "model.eval()\n", - "\n", - "# set model stepsize\n", - "if 'r' in cp:\n", - " model.decoder.set_r(cp['r'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "colab_type": "code", - "id": "zKoq0GgzqzhQ", - "outputId": "234efc61-f37a-40bc-95a3-b51896018ccb", - "tags": [] - }, - "outputs": [], - "source": [ - "from TTS.vocoder.utils.generic_utils import setup_generator\n", - "\n", - "# LOAD VOCODER MODEL\n", - "vocoder_model = setup_generator(VOCODER_CONFIG)\n", - "vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location=\"cpu\")[\"model\"])\n", - "vocoder_model.remove_weight_norm()\n", - "vocoder_model.inference_padding = 0\n", - "\n", - "ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) \n", - "if use_cuda:\n", - " vocoder_model.cuda()\n", - "vocoder_model.eval()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ws_YkPKsLgo-" - }, - "source": [ - "## Run Inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 134 - }, - "colab_type": "code", - "id": "FuWxZ9Ey5Puj", - "outputId": "9c06adad-5451-4393-89a1-a2e7dc39ab91", - "tags": [] - }, - "outputs": [], - "source": [ - "sentence = \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n", - "align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, use_cuda, ap, use_gl=False, figures=True)" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "name": "DDC-TTS_and_MultiBand-MelGAN_Example.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/DDC_TTS_and_MultiBand_MelGAN_TF_Example.ipynb b/notebooks/DDC_TTS_and_MultiBand_MelGAN_TF_Example.ipynb deleted file mode 100644 index 4b009ce9..00000000 --- a/notebooks/DDC_TTS_and_MultiBand_MelGAN_TF_Example.ipynb +++ /dev/null @@ -1,346 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false", - "colab_type": "text", - "id": "6LWsNd3_M3MP" - }, - "source": [ - "# Mozilla TTS on CPU Real-Time Speech Synthesis with Tensorflow" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false", - "colab_type": "text", - "id": "FAqrSIWgLyP0" - }, - "source": [ - "**These models are converted from released [PyTorch models](https://colab.research.google.com/drive/1u_16ZzHjKYFn1HNVuA4Qf_i2MMFB9olY?usp=sharing) using our TF utilities provided in Mozilla TTS.**\n", - "\n", - "These TF models support TF 2.2 and for different versions you might need to\n", - "regenerate them. \n", - "\n", - "We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n", - "\n", - "Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n", - "\n", - "MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n", - "\n", - "Note that both model performances can be improved with more training.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false", - "colab_type": "text", - "id": "Ku-dA4DKoeXk" - }, - "source": [ - "### Download Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162 - }, - "colab_type": "code", - "id": "jGIgnWhGsxU1", - "outputId": "08b0dddd-4edf-48c9-e8e5-a419b36a5c3d", - "tags": [] - }, - "outputs": [], - "source": [ - "!gdown --id 1p7OSEEW_Z7ORxNgfZwhMy7IiLE1s0aH7 -O data/tts_model.pkl\n", - "!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "colab_type": "code", - "id": "4dnpE0-kvTsu", - "outputId": "2fe836eb-c7e7-4f1e-9352-0142126bb19f", - "tags": [] - }, - "outputs": [], - "source": [ - "!gdown --id 1rHmj7CqD3Sfa716Y3ub_vpIBrQg_b1yF -O data/vocoder_model.pkl\n", - "!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n", - "!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false", - "colab_type": "text", - "id": "Zlgi8fPdpRF0" - }, - "source": [ - "### Define TTS function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": {}, - "colab_type": "code", - "id": "f-Yc42nQZG5A" - }, - "outputs": [], - "source": [ - "def tts(model, text, CONFIG, p):\n", - " t_1 = time.time()\n", - " waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n", - " truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars,\n", - " backend='tf')\n", - " waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n", - " waveform = waveform.numpy()[0, 0]\n", - " rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n", - " tps = (time.time() - t_1) / len(waveform)\n", - " print(waveform.shape)\n", - " print(\" > Run-time: {}\".format(time.time() - t_1))\n", - " print(\" > Real-time factor: {}\".format(rtf))\n", - " print(\" > Time per step: {}\".format(tps))\n", - " IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n", - " return alignment, mel_postnet_spec, stop_tokens, waveform" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false", - "colab_type": "text", - "id": "ZksegYQepkFg" - }, - "source": [ - "### Load Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": {}, - "colab_type": "code", - "id": "oVa0kOamprgj" - }, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import time\n", - "import IPython\n", - "\n", - "from TTS.tts.tf.utils.generic_utils import setup_model\n", - "from TTS.tts.tf.utils.io import load_checkpoint\n", - "from TTS.utils.io import load_config\n", - "from TTS.tts.utils.text.symbols import symbols, phonemes\n", - "from TTS.utils.audio import AudioProcessor\n", - "from TTS.tts.utils.synthesis import synthesis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": {}, - "colab_type": "code", - "id": "EY-sHVO8IFSH" - }, - "outputs": [], - "source": [ - "# runtime settings\n", - "use_cuda = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": {}, - "colab_type": "code", - "id": "_1aIUp2FpxOQ" - }, - "outputs": [], - "source": [ - "# model paths\n", - "TTS_MODEL = \"data/tts_model.pkl\"\n", - "TTS_CONFIG = \"data/config.json\"\n", - "VOCODER_MODEL = \"data/vocoder_model.pkl\"\n", - "VOCODER_CONFIG = \"data/config_vocoder.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": {}, - "colab_type": "code", - "id": "CpgmdBVQplbv" - }, - "outputs": [], - "source": [ - "# load configs\n", - "TTS_CONFIG = load_config(TTS_CONFIG)\n", - "VOCODER_CONFIG = load_config(VOCODER_CONFIG)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 471 - }, - "colab_type": "code", - "id": "zmrQxiozIUVE", - "outputId": "fa71bd05-401f-4e5b-a6f7-60ae765966db", - "tags": [] - }, - "outputs": [], - "source": [ - "# load the audio processor\n", - "TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n", - "ap = AudioProcessor(**TTS_CONFIG.audio) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 72 - }, - "colab_type": "code", - "id": "8fLoI4ipqMeS", - "outputId": "595d990f-930d-4698-ee14-77796b5eed7d", - "tags": [] - }, - "outputs": [], - "source": [ - "# LOAD TTS MODEL\n", - "# multi speaker \n", - "speaker_id = None\n", - "speakers = []\n", - "\n", - "# load the model\n", - "num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n", - "model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n", - "model.build_inference()\n", - "model = load_checkpoint(model, TTS_MODEL)\n", - "model.decoder.set_max_decoder_steps(1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "colab_type": "code", - "id": "zKoq0GgzqzhQ", - "outputId": "2cc3deae-144f-4465-da3b-98628d948506" - }, - "outputs": [], - "source": [ - "from TTS.vocoder.tf.utils.generic_utils import setup_generator\n", - "from TTS.vocoder.tf.utils.io import load_checkpoint\n", - "\n", - "# LOAD VOCODER MODEL\n", - "vocoder_model = setup_generator(VOCODER_CONFIG)\n", - "vocoder_model.build_inference()\n", - "vocoder_model = load_checkpoint(vocoder_model, VOCODER_MODEL)\n", - "vocoder_model.inference_padding = 0\n", - "\n", - "ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false", - "colab_type": "text", - "id": "Ws_YkPKsLgo-" - }, - "source": [ - "## Run Inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 134 - }, - "colab_type": "code", - "id": "FuWxZ9Ey5Puj", - "outputId": "07ede6e5-06e6-4612-f687-7984d20e5254" - }, - "outputs": [], - "source": [ - "sentence = \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n", - "align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, ap)" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "DDC-TTS_and_MultiBand-MelGAN_TF_Example.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/DDC_TTS_and_ParallelWaveGAN_Example.ipynb b/notebooks/DDC_TTS_and_ParallelWaveGAN_Example.ipynb deleted file mode 100644 index 4c1008e0..00000000 --- a/notebooks/DDC_TTS_and_ParallelWaveGAN_Example.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6LWsNd3_M3MP" - }, - "source": [ - "# Mozilla TTS on CPU Real-Time Speech Synthesis " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "FAqrSIWgLyP0" - }, - "source": [ - "We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n", - "\n", - "Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n", - "\n", - "MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n", - "\n", - "Note that both model performances can be improved with more training." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ku-dA4DKoeXk" - }, - "source": [ - "### Download Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162 - }, - "colab_type": "code", - "id": "jGIgnWhGsxU1", - "outputId": "88725e41-a8dc-4885-b3bf-cac939f38abe", - "tags": [] - }, - "outputs": [], - "source": [ - "!gdown --id 1dntzjWFg7ufWaTaFy80nRz-Tu02xWZos -O data/tts_model.pth.tar\n", - "!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "colab_type": "code", - "id": "4dnpE0-kvTsu", - "outputId": "76377c6d-789c-4995-ba00-a21a6e1c401e", - "tags": [] - }, - "outputs": [], - "source": [ - "!gdown --id 1X09hHAyAJOnrplCUMAdW_t341Kor4YR4 -O data/vocoder_model.pth.tar\n", - "!gdown --id \"1qN7vQRIYkzvOX_DtiZtTajzoZ1eW1-Eg\" -O data/config_vocoder.json\n", - "!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Zlgi8fPdpRF0" - }, - "source": [ - "### Define TTS function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "f-Yc42nQZG5A" - }, - "outputs": [], - "source": [ - "def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n", - " t_1 = time.time()\n", - " waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n", - " truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n", - " # mel_postnet_spec = ap.denormalize(mel_postnet_spec.T)\n", - " if not use_gl:\n", - " waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n", - " waveform = waveform.flatten()\n", - " if use_cuda:\n", - " waveform = waveform.cpu()\n", - " waveform = waveform.numpy()\n", - " rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n", - " tps = (time.time() - t_1) / len(waveform)\n", - " print(waveform.shape)\n", - " print(\" > Run-time: {}\".format(time.time() - t_1))\n", - " print(\" > Real-time factor: {}\".format(rtf))\n", - " print(\" > Time per step: {}\".format(tps))\n", - " IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n", - " return alignment, mel_postnet_spec, stop_tokens, waveform" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ZksegYQepkFg" - }, - "source": [ - "### Load Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "oVa0kOamprgj" - }, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import time\n", - "import IPython\n", - "\n", - "from TTS.tts.utils.generic_utils import setup_model\n", - "from TTS.utils.io import load_config\n", - "from TTS.tts.utils.text.symbols import symbols, phonemes\n", - "from TTS.utils.audio import AudioProcessor\n", - "from TTS.tts.utils.synthesis import synthesis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EY-sHVO8IFSH" - }, - "outputs": [], - "source": [ - "# runtime settings\n", - "use_cuda = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_1aIUp2FpxOQ" - }, - "outputs": [], - "source": [ - "# model paths\n", - "TTS_MODEL = \"data/tts_model.pth.tar\"\n", - "TTS_CONFIG = \"data/config.json\"\n", - "VOCODER_MODEL = \"data/vocoder_model.pth.tar\"\n", - "VOCODER_CONFIG = \"data/config_vocoder.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "CpgmdBVQplbv" - }, - "outputs": [], - "source": [ - "# load configs\n", - "TTS_CONFIG = load_config(TTS_CONFIG)\n", - "VOCODER_CONFIG = load_config(VOCODER_CONFIG)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 471 - }, - "colab_type": "code", - "id": "zmrQxiozIUVE", - "outputId": "60c4daa0-4c5b-4a2e-fe0d-be437d003a49", - "tags": [] - }, - "outputs": [], - "source": [ - "# load the audio processor\n", - "TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n", - "ap = AudioProcessor(**TTS_CONFIG.audio) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "colab_type": "code", - "id": "8fLoI4ipqMeS", - "outputId": "b789066e-e305-42ad-b3ca-eba8d9267382", - "tags": [] - }, - "outputs": [], - "source": [ - "# LOAD TTS MODEL\n", - "# multi speaker \n", - "speaker_id = None\n", - "speakers = []\n", - "\n", - "# load the model\n", - "num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n", - "model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n", - "\n", - "# load model state\n", - "cp = torch.load(TTS_MODEL, map_location=torch.device('cpu'))\n", - "\n", - "# load the model\n", - "model.load_state_dict(cp['model'])\n", - "if use_cuda:\n", - " model.cuda()\n", - "model.eval()\n", - "\n", - "# set model stepsize\n", - "if 'r' in cp:\n", - " model.decoder.set_r(cp['r'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "colab_type": "code", - "id": "zKoq0GgzqzhQ", - "outputId": "234efc61-f37a-40bc-95a3-b51896018ccb", - "tags": [] - }, - "outputs": [], - "source": [ - "from TTS.vocoder.utils.generic_utils import setup_generator\n", - "\n", - "# LOAD VOCODER MODEL\n", - "vocoder_model = setup_generator(VOCODER_CONFIG)\n", - "vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location=\"cpu\")[\"model\"])\n", - "vocoder_model.remove_weight_norm()\n", - "vocoder_model.inference_padding = 0\n", - "\n", - "ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) \n", - "if use_cuda:\n", - " vocoder_model.cuda()\n", - "vocoder_model.eval()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ws_YkPKsLgo-" - }, - "source": [ - "## Run Inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 134 - }, - "colab_type": "code", - "id": "FuWxZ9Ey5Puj", - "outputId": "9c06adad-5451-4393-89a1-a2e7dc39ab91", - "tags": [] - }, - "outputs": [], - "source": [ - "sentence = \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n", - "align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, use_cuda, ap, use_gl=False, figures=True)" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "name": "DDC-TTS_and_MultiBand-MelGAN_Example.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/ExtractTTSpectrogram.ipynb b/notebooks/ExtractTTSpectrogram.ipynb index 3597ebe3..50b60ff0 100644 --- a/notebooks/ExtractTTSpectrogram.ipynb +++ b/notebooks/ExtractTTSpectrogram.ipynb @@ -2,14 +2,16 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "This is a notebook to generate mel-spectrograms from a TTS model to be used in a Vocoder training." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -20,7 +22,7 @@ "import numpy as np\n", "from tqdm import tqdm as tqdm\n", "from torch.utils.data import DataLoader\n", - "from TTS.tts.datasets.TTSDataset import TTSDataset\n", + "from TTS.tts.datasets.dataset import TTSDataset\n", "from TTS.tts.layers.losses import L1LossMasked\n", "from TTS.utils.audio import AudioProcessor\n", "from TTS.config import load_config\n", @@ -33,13 +35,13 @@ "\n", "import os\n", "os.environ['CUDA_VISIBLE_DEVICES']='2'" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def set_filename(wav_path, out_path):\n", " wav_file = os.path.basename(wav_path)\n", @@ -51,13 +53,13 @@ " mel_path = os.path.join(out_path, \"mel\", file_name)\n", " wav_path = os.path.join(out_path, \"wav_gl\", file_name)\n", " return file_name, wavq_path, mel_path, wav_path" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "OUT_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/specs2/\"\n", "DATA_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/\"\n", @@ -77,13 +79,13 @@ "C = load_config(CONFIG_PATH)\n", "C.audio['do_trim_silence'] = False # IMPORTANT!!!!!!!!!!!!!!! disable to align mel specs with the wav files\n", "ap = AudioProcessor(bits=QUANTIZE_BIT, **C.audio)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(C['r'])\n", "# if the vocabulary was passed, replace the default\n", @@ -95,13 +97,13 @@ "# TODO: multiple speaker\n", "model = setup_model(C)\n", "model.load_checkpoint(C, MODEL_FILE, eval=True)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "preprocessor = importlib.import_module(\"TTS.tts.datasets.formatters\")\n", "preprocessor = getattr(preprocessor, DATASET.lower())\n", @@ -120,20 +122,20 @@ "loader = DataLoader(\n", " dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False\n", ")\n" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Generate model outputs " - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import pickle\n", "\n", @@ -212,42 +214,42 @@ "\n", " print(np.mean(losses))\n", " print(np.mean(postnet_losses))" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# for pwgan\n", "with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n", " for data in metadata:\n", " f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Sanity Check" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "idx = 1\n", "ap.melspectrogram(ap.load_wav(item_idx[idx])).shape" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import soundfile as sf\n", "wav, sr = sf.read(item_idx[idx])\n", @@ -255,46 +257,46 @@ "mel_decoder = mel_outputs[idx][:mel_lengths[idx], :].detach().cpu().numpy()\n", "mel_truth = ap.melspectrogram(wav)\n", "print(mel_truth.shape)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# plot posnet output\n", "print(mel_postnet[:mel_lengths[idx], :].shape)\n", "plot_spectrogram(mel_postnet, ap)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# plot decoder output\n", "print(mel_decoder.shape)\n", "plot_spectrogram(mel_decoder, ap)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# plot GT specgrogram\n", "print(mel_truth.shape)\n", "plot_spectrogram(mel_truth.T, ap)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# postnet, decoder diff\n", "from matplotlib import pylab as plt\n", @@ -303,13 +305,13 @@ "plt.imshow(abs(mel_diff[:mel_lengths[idx],:]).T,aspect=\"auto\", origin=\"lower\");\n", "plt.colorbar()\n", "plt.tight_layout()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# PLOT GT SPECTROGRAM diff\n", "from matplotlib import pylab as plt\n", @@ -318,13 +320,13 @@ "plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n", "plt.colorbar()\n", "plt.tight_layout()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# PLOT GT SPECTROGRAM diff\n", "from matplotlib import pylab as plt\n", @@ -334,22 +336,23 @@ "plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n", "plt.colorbar()\n", "plt.tight_layout()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { + "interpreter": { + "hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56" + }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.7 64-bit ('base': conda)" + "display_name": "Python 3.9.7 64-bit ('base': conda)", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -362,11 +365,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" - }, - "interpreter": { - "hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/notebooks/PlotUmapLibriTTS.ipynb b/notebooks/PlotUmapLibriTTS.ipynb index f61ce40c..c809a5c4 100644 --- a/notebooks/PlotUmapLibriTTS.ipynb +++ b/notebooks/PlotUmapLibriTTS.ipynb @@ -19,19 +19,16 @@ "source": [ "import os\n", "import glob\n", - "import random\n", "import numpy as np\n", - "import torch\n", "import umap\n", "\n", - "from TTS.speaker_encoder.model import SpeakerEncoder\n", "from TTS.utils.audio import AudioProcessor\n", - "from TTS.tts.utils.generic_utils import load_config\n", + "from TTS.config import load_config\n", "\n", "from bokeh.io import output_notebook, show\n", "from bokeh.plotting import figure\n", "from bokeh.models import HoverTool, ColumnDataSource, BoxZoomTool, ResetTool, OpenURL, TapTool\n", - "from bokeh.transform import factor_cmap, factor_mark\n", + "from bokeh.transform import factor_cmap\n", "from bokeh.palettes import Category10" ] }, diff --git a/notebooks/dataset_analysis/AnalyzeDataset.ipynb b/notebooks/dataset_analysis/AnalyzeDataset.ipynb index 6ff2d2ca..c2aabbf9 100644 --- a/notebooks/dataset_analysis/AnalyzeDataset.ipynb +++ b/notebooks/dataset_analysis/AnalyzeDataset.ipynb @@ -22,7 +22,6 @@ "import os\n", "import sys\n", "sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n", - "import glob\n", "import librosa\n", "import numpy as np\n", "import pandas as pd\n", diff --git a/notebooks/dataset_analysis/CheckDatasetSNR.ipynb b/notebooks/dataset_analysis/CheckDatasetSNR.ipynb index 91ecc954..18c48d0b 100644 --- a/notebooks/dataset_analysis/CheckDatasetSNR.ipynb +++ b/notebooks/dataset_analysis/CheckDatasetSNR.ipynb @@ -21,10 +21,9 @@ "metadata": {}, "outputs": [], "source": [ - "import os, sys\n", + "import os\n", "import glob\n", "import subprocess\n", - "import tempfile\n", "import IPython\n", "import soundfile as sf\n", "import numpy as np\n", @@ -208,4 +207,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/notebooks/dataset_analysis/CheckPitch.ipynb b/notebooks/dataset_analysis/CheckPitch.ipynb new file mode 100644 index 00000000..72afbc64 --- /dev/null +++ b/notebooks/dataset_analysis/CheckPitch.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "import numpy as np\n", + "import glob\n", + "from TTS.utils.audio import AudioProcessor\n", + "from TTS.config.shared_configs import BaseAudioConfig\n", + "from TTS.tts.utils.visual import plot_pitch" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "source": [ + "pitch_path = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/f0_cache\"\n", + "wav_path = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/wavs\"\n", + "wav_files = glob.glob(\"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/wavs/*.wav\")\n", + "print(len(wav_files))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "13100\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "source": [ + "ap = AudioProcessor(**BaseAudioConfig( sample_rate=22050,\n", + " do_trim_silence=True,\n", + " trim_db=60.0,\n", + " signal_norm=False,\n", + " mel_fmin=0.0,\n", + " mel_fmax=8000,\n", + " spec_gain=1.0,\n", + " log_func=\"np.log\",\n", + " ref_level_db=20,\n", + " preemphasis=0.0,))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " > Setting up Audio Processor...\n", + " | > sample_rate:22050\n", + " | > resample:False\n", + " | > num_mels:80\n", + " | > log_func:np.log\n", + " | > min_level_db:-100\n", + " | > frame_shift_ms:None\n", + " | > frame_length_ms:None\n", + " | > ref_level_db:20\n", + " | > fft_size:1024\n", + " | > power:1.5\n", + " | > preemphasis:0.0\n", + " | > griffin_lim_iters:60\n", + " | > signal_norm:False\n", + " | > symmetric_norm:True\n", + " | > mel_fmin:0\n", + " | > mel_fmax:8000\n", + " | > spec_gain:1.0\n", + " | > stft_pad_mode:reflect\n", + " | > max_norm:4.0\n", + " | > clip_norm:True\n", + " | > do_trim_silence:True\n", + " | > trim_db:60.0\n", + " | > do_sound_norm:False\n", + " | > do_amp_to_db_linear:True\n", + " | > do_amp_to_db_mel:True\n", + " | > stats_path:None\n", + " | > base:2.718281828459045\n", + " | > hop_length:256\n", + " | > win_length:1024\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "source": [ + "pitch_files = [wf.replace(\".wav\", \"_pitch.npy\").replace(wav_path, pitch_path) for wf in wav_files]" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 30, + "source": [ + "idx = 100\n", + "# wav_file = wav_files[idx]\n", + "# pitch_file = pitch_files[idx]\n", + "wav_file = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/../LJSpeech-1.1/wavs/LJ011-0097.wav\"\n", + "pitch_file = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/f0_cache/LJ011-0097_pitch.npy\"\n", + "pitch = np.load(pitch_file)\n", + "wav = ap.load_wav(wav_file)\n", + "spec = ap.melspectrogram(wav)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 31, + "source": [ + "plot_pitch(pitch, spec.T)" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "execution_count": 31 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.9.7", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.9.1 64-bit ('miniconda3': virtualenv)" + }, + "interpreter": { + "hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/dataset_analysis/PhonemeCoverage.ipynb b/notebooks/dataset_analysis/PhonemeCoverage.ipynb index e659511a..2b7f5d67 100644 --- a/notebooks/dataset_analysis/PhonemeCoverage.ipynb +++ b/notebooks/dataset_analysis/PhonemeCoverage.ipynb @@ -50,7 +50,7 @@ "source": [ "# import stuff\n", "from TTS.utils.io import load_config\n", - "from TTS.tts.datasets.formatters import load_meta_data\n", + "from TTS.tts.datasets.formatters import load_tts_samples\n", "from TTS.tts.utils.text import phoneme_to_sequence, sequence_to_phoneme\n", "from tqdm import tqdm\n", "from matplotlib import pylab as plt\n", @@ -75,7 +75,7 @@ "CONFIG = load_config(CONFIG_FILE)\n", "\n", "# Load some properties from config.json\n", - "CONFIG_METADATA = sorted(load_meta_data(CONFIG.datasets)[0])\n", + "CONFIG_METADATA = sorted(load_tts_samples(CONFIG.datasets)[0])\n", "CONFIG_METADATA = CONFIG_METADATA\n", "CONFIG_DATASET = CONFIG.datasets[0]\n", "CONFIG_PHONEME_LANGUAGE = CONFIG.phoneme_language\n", diff --git a/recipes/README.md b/recipes/README.md index 041693a2..cf3f3de9 100644 --- a/recipes/README.md +++ b/recipes/README.md @@ -1,13 +1,16 @@ # 🐸💬 TTS Training Recipes -TTS recipes intended to host bash scripts running all the necessary steps to train a TTS model with a particular dataset. +TTS recipes intended to host scripts running all the necessary steps to train a TTS model on a particular dataset. -Run each script from the root TTS folder as follows +For each dataset, you need to download the dataset once. Then you run the training for the model you want. + +Run each script from the root TTS folder as follows. ```console -$ bash ./recipes///run.sh +$ sh ./recipes//download_.sh +$ python recipes///train.py ``` -All the outputs are held under the recipe directory unless you change the paths in the bash script. +If you train a new model using TTS, feel free to share your training to expand the list of recipes. -If you train a new model using TTS, feel free to share your training to expand the list of recipes. \ No newline at end of file +You can also open a new discussion and share your progress with the 🐸 community. \ No newline at end of file diff --git a/recipes/ljspeech/align_tts/train_aligntts.py b/recipes/ljspeech/align_tts/train_aligntts.py index 4e214f92..68b67d66 100644 --- a/recipes/ljspeech/align_tts/train_aligntts.py +++ b/recipes/ljspeech/align_tts/train_aligntts.py @@ -1,9 +1,14 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training -from TTS.tts.configs import AlignTTSConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.align_tts_config import AlignTTSConfig, BaseDatasetConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.align_tts import AlignTTS +from TTS.utils.audio import AudioProcessor output_path = os.path.dirname(os.path.abspath(__file__)) + +# init configs dataset_config = BaseDatasetConfig( name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") ) @@ -25,6 +30,24 @@ config = AlignTTSConfig( output_path=output_path, datasets=[dataset_config], ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init model +model = AlignTTS(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/fast_pitch/train_fast_pitch.py b/recipes/ljspeech/fast_pitch/train_fast_pitch.py index 614e42e0..0a4a965b 100644 --- a/recipes/ljspeech/fast_pitch/train_fast_pitch.py +++ b/recipes/ljspeech/fast_pitch/train_fast_pitch.py @@ -1,8 +1,11 @@ import os -from TTS.config import BaseAudioConfig, BaseDatasetConfig -from TTS.trainer import Trainer, TrainingArgs, init_training -from TTS.tts.configs import FastPitchConfig +from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.fast_pitch_config import FastPitchConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.forward_tts import ForwardTTS +from TTS.utils.audio import AudioProcessor from TTS.utils.manage import ModelManager output_path = os.path.dirname(os.path.abspath(__file__)) @@ -64,7 +67,23 @@ if not config.model_args.use_aligner: f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true" ) -# train the model -args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, tb_logger) +# init audio processor +ap = AudioProcessor(**config.audio) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init the model +model = ForwardTTS(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/fast_speech/train_fast_speech.py b/recipes/ljspeech/fast_speech/train_fast_speech.py new file mode 100644 index 00000000..a71da94b --- /dev/null +++ b/recipes/ljspeech/fast_speech/train_fast_speech.py @@ -0,0 +1,88 @@ +import os + +from TTS.config import BaseAudioConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.fast_speech_config import FastSpeechConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.forward_tts import ForwardTTS +from TTS.utils.audio import AudioProcessor +from TTS.utils.manage import ModelManager + +output_path = os.path.dirname(os.path.abspath(__file__)) + +# init configs +dataset_config = BaseDatasetConfig( + name="ljspeech", + meta_file_train="metadata.csv", + # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"), + path=os.path.join(output_path, "../LJSpeech-1.1/"), +) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=60.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = FastSpeechConfig( + run_name="fast_speech_ljspeech", + audio=audio_config, + batch_size=32, + eval_batch_size=16, + num_loader_workers=8, + num_eval_loader_workers=4, + compute_input_seq_cache=True, + compute_f0=False, + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="english_cleaners", + use_phonemes=True, + use_espeak_phonemes=False, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=50, + print_eval=False, + mixed_precision=False, + sort_by_audio_len=True, + max_seq_len=500000, + output_path=output_path, + datasets=[dataset_config], +) + +# compute alignments +if not config.model_args.use_aligner: + manager = ModelManager() + model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA") + # TODO: make compute_attention python callable + os.system( + f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true" + ) + +# init audio processor +ap = AudioProcessor(**config.audio) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init the model +model = ForwardTTS(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/ljspeech/glow_tts/train_glowtts.py b/recipes/ljspeech/glow_tts/train_glowtts.py index 5d71f4ed..7bd9ea19 100644 --- a/recipes/ljspeech/glow_tts/train_glowtts.py +++ b/recipes/ljspeech/glow_tts/train_glowtts.py @@ -1,12 +1,30 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training -from TTS.tts.configs import BaseDatasetConfig, GlowTTSConfig +# Trainer: Where the ✨️ happens. +# TrainingArgs: Defines the set of arguments of the Trainer. +from TTS.trainer import Trainer, TrainingArgs +# GlowTTSConfig: all model related values for training, validating and testing. +from TTS.tts.configs.glow_tts_config import GlowTTSConfig + +# BaseDatasetConfig: defines name, formatter and path of the dataset. +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.glow_tts import GlowTTS +from TTS.utils.audio import AudioProcessor + +# we use the same path as this script as our training folder. output_path = os.path.dirname(os.path.abspath(__file__)) + +# DEFINE DATASET CONFIG +# Set LJSpeech as our target dataset and define its path. +# You can also use a simple Dict to define the dataset and pass it to your custom formatter. dataset_config = BaseDatasetConfig( name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") ) + +# INITIALIZE THE TRAINING CONFIGURATION +# Configure the model. Every config class inherits the BaseTTSConfig. config = GlowTTSConfig( batch_size=32, eval_batch_size=16, @@ -25,6 +43,37 @@ config = GlowTTSConfig( output_path=output_path, datasets=[dataset_config], ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + +# INITIALIZE THE AUDIO PROCESSOR +# Audio processor is used for feature extraction and audio I/O. +# It mainly serves to the dataloader and the training loggers. +ap = AudioProcessor(**config.audio.to_dict()) + +# LOAD DATA SAMPLES +# Each sample is a list of ```[text, audio_file_path, speaker_name]``` +# You can define your custom sample loader returning the list of samples. +# Or define your custom formatter and pass it to the `load_tts_samples`. +# Check `TTS.tts.datasets.load_tts_samples` for more details. +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# INITIALIZE THE MODEL +# Models take a config object and a speaker manager as input +# Config defines the details of the model like the number of layers, the size of the embedding, etc. +# Speaker manager is used by multi-speaker models. +model = GlowTTS(config, speaker_manager=None) + +# INITIALIZE THE TRAINER +# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training, +# distributed training, etc. +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, # assets are objetcs used by the models but not class members. +) + +# AND... 3,2,1... 🚀 trainer.fit() diff --git a/recipes/ljspeech/hifigan/train_hifigan.py b/recipes/ljspeech/hifigan/train_hifigan.py index f50ef476..8d1c272a 100644 --- a/recipes/ljspeech/hifigan/train_hifigan.py +++ b/recipes/ljspeech/hifigan/train_hifigan.py @@ -1,29 +1,51 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training +from TTS.trainer import Trainer, TrainingArgs +from TTS.utils.audio import AudioProcessor from TTS.vocoder.configs import HifiganConfig +from TTS.vocoder.datasets.preprocess import load_wav_data +from TTS.vocoder.models.gan import GAN output_path = os.path.dirname(os.path.abspath(__file__)) + config = HifiganConfig( batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, - test_delay_epochs=-1, + test_delay_epochs=5, epochs=1000, seq_len=8192, pad_short=2000, use_noise_augment=True, eval_split_size=10, print_step=25, - print_eval=True, + print_eval=False, mixed_precision=False, lr_gen=1e-4, lr_disc=1e-4, data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"), output_path=output_path, ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) + +# init model +model = GAN(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py b/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py index 1473ec3c..90c52997 100644 --- a/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py +++ b/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py @@ -1,29 +1,51 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training +from TTS.trainer import Trainer, TrainingArgs +from TTS.utils.audio import AudioProcessor from TTS.vocoder.configs import MultibandMelganConfig +from TTS.vocoder.datasets.preprocess import load_wav_data +from TTS.vocoder.models.gan import GAN output_path = os.path.dirname(os.path.abspath(__file__)) + config = MultibandMelganConfig( batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, - test_delay_epochs=-1, + test_delay_epochs=5, epochs=1000, seq_len=8192, pad_short=2000, use_noise_augment=True, eval_split_size=10, print_step=25, - print_eval=True, + print_eval=False, mixed_precision=False, lr_gen=1e-4, lr_disc=1e-4, data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"), output_path=output_path, ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) + +# init model +model = GAN(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/speedy_speech/train_speedy_speech.py b/recipes/ljspeech/speedy_speech/train_speedy_speech.py index 2882468f..6b9683af 100644 --- a/recipes/ljspeech/speedy_speech/train_speedy_speech.py +++ b/recipes/ljspeech/speedy_speech/train_speedy_speech.py @@ -1,18 +1,15 @@ import os from TTS.config import BaseAudioConfig, BaseDatasetConfig -from TTS.trainer import Trainer, TrainingArgs, init_training -from TTS.tts.configs import SpeedySpeechConfig -from TTS.utils.manage import ModelManager +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.forward_tts import ForwardTTS +from TTS.utils.audio import AudioProcessor output_path = os.path.dirname(os.path.abspath(__file__)) - -# init configs dataset_config = BaseDatasetConfig( - name="ljspeech", - meta_file_train="metadata.csv", - # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"), - path=os.path.join(output_path, "../LJSpeech-1.1/"), + name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") ) audio_config = BaseAudioConfig( @@ -53,16 +50,32 @@ config = SpeedySpeechConfig( datasets=[dataset_config], ) -# compute alignments -if not config.model_args.use_aligner: - manager = ModelManager() - model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA") - # TODO: make compute_attention python callable - os.system( - f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true" - ) +# # compute alignments +# if not config.model_args.use_aligner: +# manager = ModelManager() +# model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA") +# # TODO: make compute_attention python callable +# os.system( +# f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true" +# ) -# train the model -args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, tb_logger) +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init model +model = ForwardTTS(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/tacotron2-DCA/run.sh b/recipes/ljspeech/tacotron2-DCA/run.sh deleted file mode 100644 index 8bcd9e3d..00000000 --- a/recipes/ljspeech/tacotron2-DCA/run.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash -# take the scripts's parent's directory to prefix all the output paths. -RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" -echo $RUN_DIR -# # download LJSpeech dataset -# wget http://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 -# # extract -# tar -xjf LJSpeech-1.1.tar.bz2 -# # create train-val splits -# shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv -# head -n 12000 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv -# tail -n 1100 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv -# mv LJSpeech-1.1 $RUN_DIR/ -# rm LJSpeech-1.1.tar.bz2 -# # compute dataset mean and variance for normalization -# python TTS/bin/compute_statistics.py $RUN_DIR/tacotron2-DDC.json $RUN_DIR/scale_stats.npy --data_path $RUN_DIR/LJSpeech-1.1/wavs/ -# training .... -# change the GPU id if needed -CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path $RUN_DIR/tacotron2-DCA.json \ - --coqpit.output_path $RUN_DIR \ - --coqpit.datasets.0.path /media/erogol/nvme_linux/gdrive/Projects/TTS/recipes/ljspeech/tacotron2-DDC/LJSpeech-1.1/ \ - --coqpit.audio.stats_path $RUN_DIR/scale_stats.npy \ \ No newline at end of file diff --git a/recipes/ljspeech/tacotron2-DCA/scale_stats.npy b/recipes/ljspeech/tacotron2-DCA/scale_stats.npy deleted file mode 100644 index 1dc577a6..00000000 Binary files a/recipes/ljspeech/tacotron2-DCA/scale_stats.npy and /dev/null differ diff --git a/recipes/ljspeech/tacotron2-DCA/tacotron2-DCA.json b/recipes/ljspeech/tacotron2-DCA/tacotron2-DCA.json deleted file mode 100644 index 73bb8ae3..00000000 --- a/recipes/ljspeech/tacotron2-DCA/tacotron2-DCA.json +++ /dev/null @@ -1,85 +0,0 @@ -{ - "datasets": [ - { - "name": "ljspeech", - "path": "DEFINE THIS", - "meta_file_train": "metadata.csv", - "meta_file_val": null - } - ], - "audio": { - "fft_size": 1024, - "win_length": 1024, - "hop_length": 256, - "frame_length_ms": null, - "frame_shift_ms": null, - "sample_rate": 22050, - "preemphasis": 0.0, - "ref_level_db": 20, - "do_trim_silence": true, - "trim_db": 60, - "power": 1.5, - "griffin_lim_iters": 60, - "num_mels": 80, - "mel_fmin": 50.0, - "mel_fmax": 7600.0, - "spec_gain": 1, - "signal_norm": true, - "min_level_db": -100, - "symmetric_norm": true, - "max_norm": 4.0, - "clip_norm": true, - "stats_path": "scale_stats.npy" - }, - "distributed_backend": "nlcc", - "distributed_url": "tcp:\/\/localhost:54321", - "model": "Tacotron2", - "run_name": "ljspeech-dca", - "run_description": "tacotron2 with dynamic conv attention.", - "batch_size": 64, - "eval_batch_size": 16, - "mixed_precision": true, - "loss_masking": true, - "decoder_loss_alpha": 0.25, - "postnet_loss_alpha": 0.25, - "postnet_diff_spec_alpha": 0.25, - "decoder_diff_spec_alpha": 0.25, - "decoder_ssim_alpha": 0.25, - "postnet_ssim_alpha": 0.25, - "ga_alpha": 5.0, - "stopnet_pos_weight": 15.0, - "run_eval": true, - "test_delay_epochs": 10, - "max_decoder_steps": 1000, - "noam_schedule": true, - "grad_clip": 0.05, - "epochs": 1000, - "lr": 0.001, - "wd": 1e-06, - "warmup_steps": 4000, - "memory_size": -1, - "prenet_type": "original", - "prenet_dropout": true, - "attention_type": "dynamic_convolution", - "location_attn": true, - "attention_norm": "sigmoid", - "r": 2, - "stopnet": true, - "separate_stopnet": true, - "print_step": 25, - "tb_plot_step": 100, - "print_eval": false, - "save_step": 10000, - "checkpoint": true, - "text_cleaner": "phoneme_cleaners", - "num_loader_workers": 4, - "num_val_loader_workers": 4, - "batch_group_size": 4, - "min_seq_len": 6, - "max_seq_len": 180, - "compute_input_seq_cache": true, - "output_path": "DEFINE THIS", - "phoneme_cache_path": "DEFINE THIS", - "use_phonemes": false, - "phoneme_language": "en-us" -} diff --git a/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py b/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py new file mode 100644 index 00000000..cf00ccc2 --- /dev/null +++ b/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py @@ -0,0 +1,76 @@ +import os + +from TTS.config.shared_configs import BaseAudioConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.configs.tacotron2_config import Tacotron2Config +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.tacotron2 import Tacotron2 +from TTS.utils.audio import AudioProcessor + +# from TTS.tts.datasets.tokenizer import Tokenizer + +output_path = os.path.dirname(os.path.abspath(__file__)) + +# init configs +dataset_config = BaseDatasetConfig( + name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") +) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=60.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = Tacotron2Config( # This is the config that is saved for the future use + audio=audio_config, + batch_size=64, + eval_batch_size=16, + num_loader_workers=4, + num_eval_loader_workers=4, + run_eval=True, + test_delay_epochs=-1, + ga_alpha=5.0, + r=2, + attention_type="dynamic_convolution", + double_decoder_consistency=True, + epochs=1000, + text_cleaner="phoneme_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=25, + print_eval=True, + mixed_precision=False, + output_path=output_path, + datasets=[dataset_config], +) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init model +model = Tacotron2(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/ljspeech/tacotron2-DDC/run.sh b/recipes/ljspeech/tacotron2-DDC/run.sh deleted file mode 100644 index dd36454f..00000000 --- a/recipes/ljspeech/tacotron2-DDC/run.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash -# take the scripts's parent's directory to prefix all the output paths. -RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" -echo $RUN_DIR -# download LJSpeech dataset -wget http://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 -# extract -tar -xjf LJSpeech-1.1.tar.bz2 -# create train-val splits -shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv -head -n 12000 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv -tail -n 1100 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv -mv LJSpeech-1.1 $RUN_DIR/ -rm LJSpeech-1.1.tar.bz2 -# compute dataset mean and variance for normalization -python TTS/bin/compute_statistics.py $RUN_DIR/tacotron2-DDC.json $RUN_DIR/scale_stats.npy --data_path $RUN_DIR/LJSpeech-1.1/wavs/ -# training .... -# change the GPU id if needed -CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path $RUN_DIR/tacotron2-DDC.json \ - --coqpit.output_path $RUN_DIR \ - --coqpit.datasets.0.path $RUN_DIR/LJSpeech-1.1/ \ - --coqpit.audio.stats_path $RUN_DIR/scale_stats.npy \ \ No newline at end of file diff --git a/recipes/ljspeech/tacotron2-DDC/scale_stats.npy b/recipes/ljspeech/tacotron2-DDC/scale_stats.npy deleted file mode 100644 index 1dc577a6..00000000 Binary files a/recipes/ljspeech/tacotron2-DDC/scale_stats.npy and /dev/null differ diff --git a/recipes/ljspeech/tacotron2-DDC/tacotron2-DDC.json b/recipes/ljspeech/tacotron2-DDC/tacotron2-DDC.json deleted file mode 100644 index 339e65b8..00000000 --- a/recipes/ljspeech/tacotron2-DDC/tacotron2-DDC.json +++ /dev/null @@ -1,94 +0,0 @@ -{ - "model": "Tacotron2", - "datasets": [ - { - "name": "ljspeech", - "path": "DEFINE THIS", - "meta_file_train": "metadata.csv", - "meta_file_val": null - } - ], - "audio": { - "fft_size": 1024, - "win_length": 1024, - "hop_length": 256, - "frame_length_ms": null, - "frame_shift_ms": null, - "sample_rate": 22050, - "preemphasis": 0.0, - "ref_level_db": 20, - "do_trim_silence": true, - "trim_db": 60, - "power": 1.5, - "griffin_lim_iters": 60, - "num_mels": 80, - "mel_fmin": 50.0, - "mel_fmax": 7600.0, - "spec_gain": 1, - "signal_norm": true, - "min_level_db": -100, - "symmetric_norm": true, - "max_norm": 4.0, - "clip_norm": true, - "stats_path": "scale_stats.npy" - }, - "gst":{ - "gst_embedding_dim": 256, - "gst_num_heads": 4, - "gst_num_style_tokens": 10 - }, - "distributed_backend": "gloo", - "distributed_url": "tcp:\/\/localhost:54321", - "run_name": "ljspeech-ddc", - "run_description": "tacotron2 with double decoder consistency.", - "batch_size": 64, - "eval_batch_size": 16, - "mixed_precision": false, - "loss_masking": true, - "decoder_loss_alpha": 0.25, - "postnet_loss_alpha": 0.25, - "postnet_diff_spec_alpha": 0.25, - "decoder_diff_spec_alpha": 0.25, - "decoder_ssim_alpha": 0.25, - "postnet_ssim_alpha": 0.25, - "ga_alpha": 5.0, - "stopnet_pos_weight": 15.0, - "run_eval": true, - "test_delay_epochs": 10, - "test_sentences_file": null, - "max_decoder_steps": 1000, - "noam_schedule": true, - "grad_clip": 0.05, - "epochs": 1000, - "lr": 0.001, - "wd": 1e-06, - "warmup_steps": 4000, - "memory_size": -1, - "prenet_type": "original", - "prenet_dropout": true, - "attention_type": "original", - "location_attn": true, - "double_decoder_consistency": true, - "ddc_r": 6, - "attention_norm": "sigmoid", - "r": 6, - "gradual_training": [[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]], - "stopnet": true, - "separate_stopnet": true, - "print_step": 25, - "tb_plot_step": 100, - "print_eval": false, - "save_step": 10000, - "checkpoint": true, - "text_cleaner": "phoneme_cleaners", - "num_loader_workers": 4, - "num_val_loader_workers": 4, - "batch_group_size": 4, - "min_seq_len": 6, - "max_seq_len": 180, - "compute_input_seq_cache": true, - "output_path": "DEFINE THIS", - "phoneme_cache_path": "DEFINE THIS", - "use_phonemes": false, - "phoneme_language": "en-us" -} diff --git a/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py b/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py new file mode 100644 index 00000000..b452094a --- /dev/null +++ b/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py @@ -0,0 +1,75 @@ +import os + +from TTS.config.shared_configs import BaseAudioConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.configs.tacotron2_config import Tacotron2Config +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.tacotron2 import Tacotron2 +from TTS.utils.audio import AudioProcessor + +# from TTS.tts.datasets.tokenizer import Tokenizer + +output_path = os.path.dirname(os.path.abspath(__file__)) + +# init configs +dataset_config = BaseDatasetConfig( + name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") +) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=60.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = Tacotron2Config( # This is the config that is saved for the future use + audio=audio_config, + batch_size=64, + eval_batch_size=16, + num_loader_workers=4, + num_eval_loader_workers=4, + run_eval=True, + test_delay_epochs=-1, + r=6, + gradual_training=[[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]], + double_decoder_consistency=True, + epochs=1000, + text_cleaner="phoneme_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=25, + print_eval=True, + mixed_precision=False, + output_path=output_path, + datasets=[dataset_config], +) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init model +model = Tacotron2(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/ljspeech/univnet/train.py b/recipes/ljspeech/univnet/train.py index e8979c92..589fd027 100644 --- a/recipes/ljspeech/univnet/train.py +++ b/recipes/ljspeech/univnet/train.py @@ -1,7 +1,10 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training +from TTS.trainer import Trainer, TrainingArgs +from TTS.utils.audio import AudioProcessor from TTS.vocoder.configs import UnivnetConfig +from TTS.vocoder.datasets.preprocess import load_wav_data +from TTS.vocoder.models.gan import GAN output_path = os.path.dirname(os.path.abspath(__file__)) config = UnivnetConfig( @@ -24,6 +27,24 @@ config = UnivnetConfig( data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"), output_path=output_path, ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) + +# init model +model = GAN(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/vits_tts/train_vits.py b/recipes/ljspeech/vits_tts/train_vits.py index 7cf52f89..e86cc861 100644 --- a/recipes/ljspeech/vits_tts/train_vits.py +++ b/recipes/ljspeech/vits_tts/train_vits.py @@ -1,8 +1,12 @@ import os from TTS.config.shared_configs import BaseAudioConfig -from TTS.trainer import Trainer, TrainingArgs, init_training -from TTS.tts.configs import BaseDatasetConfig, VitsConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.configs.vits_config import VitsConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.vits import Vits +from TTS.utils.audio import AudioProcessor output_path = os.path.dirname(os.path.abspath(__file__)) dataset_config = BaseDatasetConfig( @@ -24,6 +28,7 @@ audio_config = BaseAudioConfig( signal_norm=False, do_amp_to_db_linear=False, ) + config = VitsConfig( audio=audio_config, run_name="vits_ljspeech", @@ -47,6 +52,24 @@ config = VitsConfig( output_path=output_path, datasets=[dataset_config], ) -args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, tb_logger, cudnn_benchmark=True) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init model +model = Vits(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/wavegrad/train_wavegrad.py b/recipes/ljspeech/wavegrad/train_wavegrad.py index fe038915..6786c052 100644 --- a/recipes/ljspeech/wavegrad/train_wavegrad.py +++ b/recipes/ljspeech/wavegrad/train_wavegrad.py @@ -1,7 +1,10 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training +from TTS.trainer import Trainer, TrainingArgs +from TTS.utils.audio import AudioProcessor from TTS.vocoder.configs import WavegradConfig +from TTS.vocoder.datasets.preprocess import load_wav_data +from TTS.vocoder.models.wavegrad import Wavegrad output_path = os.path.dirname(os.path.abspath(__file__)) config = WavegradConfig( @@ -22,6 +25,24 @@ config = WavegradConfig( data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"), output_path=output_path, ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) + +# init model +model = Wavegrad(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/ljspeech/wavernn/train_wavernn.py b/recipes/ljspeech/wavernn/train_wavernn.py index 8f138298..f64f5752 100644 --- a/recipes/ljspeech/wavernn/train_wavernn.py +++ b/recipes/ljspeech/wavernn/train_wavernn.py @@ -1,7 +1,10 @@ import os -from TTS.trainer import Trainer, TrainingArgs, init_training +from TTS.trainer import Trainer, TrainingArgs +from TTS.utils.audio import AudioProcessor from TTS.vocoder.configs import WavernnConfig +from TTS.vocoder.datasets.preprocess import load_wav_data +from TTS.vocoder.models.wavernn import Wavernn output_path = os.path.dirname(os.path.abspath(__file__)) config = WavernnConfig( @@ -24,6 +27,24 @@ config = WavernnConfig( data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"), output_path=output_path, ) -args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) -trainer = Trainer(args, config, output_path, c_logger, dashboard_logger, cudnn_benchmark=True) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) + +# init model +model = Wavernn(config) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) trainer.fit() diff --git a/recipes/vctk/download_vctk.sh b/recipes/vctk/download_vctk.sh new file mode 100644 index 00000000..c0cea743 --- /dev/null +++ b/recipes/vctk/download_vctk.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# take the scripts's parent's directory to prefix all the output paths. +RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +echo $RUN_DIR +# download LJSpeech dataset +wget https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip -O VCTK-Corpus-0.92.zip +# extract +mkdir VCTK +unzip VCTK-Corpus-0.92 -d VCTK +# create train-val splits +mv VCTK $RUN_DIR/recipes/vctk/ +rm VCTK-Corpus-0.92.zip diff --git a/recipes/vctk/fast_pitch/train_fast_pitch.py b/recipes/vctk/fast_pitch/train_fast_pitch.py new file mode 100644 index 00000000..f40587e0 --- /dev/null +++ b/recipes/vctk/fast_pitch/train_fast_pitch.py @@ -0,0 +1,80 @@ +import os + +from TTS.config import BaseAudioConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.fast_pitch_config import FastPitchConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.forward_tts import ForwardTTS +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=23.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = FastPitchConfig( + run_name="fast_pitch_ljspeech", + audio=audio_config, + batch_size=32, + eval_batch_size=16, + num_loader_workers=8, + num_eval_loader_workers=4, + compute_input_seq_cache=True, + compute_f0=True, + f0_cache_path=os.path.join(output_path, "f0_cache"), + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="english_cleaners", + use_phonemes=True, + use_espeak_phonemes=False, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=50, + print_eval=False, + mixed_precision=False, + sort_by_audio_len=True, + max_seq_len=500000, + output_path=output_path, + datasets=[dataset_config], + use_speaker_embedding=True, +) + +# init audio processor +ap = AudioProcessor(**config.audio) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it maps speaker-id to speaker-name in the model and data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) +config.model_args.num_speakers = speaker_manager.num_speakers + +# init model +model = ForwardTTS(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/vctk/fast_speech/train_fast_speech.py b/recipes/vctk/fast_speech/train_fast_speech.py new file mode 100644 index 00000000..b2988809 --- /dev/null +++ b/recipes/vctk/fast_speech/train_fast_speech.py @@ -0,0 +1,80 @@ +import os + +from TTS.config import BaseAudioConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.fast_speech_config import FastSpeechConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.forward_tts import ForwardTTS +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=23.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = FastSpeechConfig( + run_name="fast_pitch_ljspeech", + audio=audio_config, + batch_size=32, + eval_batch_size=16, + num_loader_workers=8, + num_eval_loader_workers=4, + compute_input_seq_cache=True, + compute_f0=True, + f0_cache_path=os.path.join(output_path, "f0_cache"), + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="english_cleaners", + use_phonemes=True, + use_espeak_phonemes=False, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=50, + print_eval=False, + mixed_precision=False, + sort_by_audio_len=True, + max_seq_len=500000, + output_path=output_path, + datasets=[dataset_config], + use_speaker_embedding=True, +) + +# init audio processor +ap = AudioProcessor(**config.audio) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it maps speaker-id to speaker-name in the model and data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) +config.model_args.num_speakers = speaker_manager.num_speakers + +# init model +model = ForwardTTS(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/vctk/glow_tts/train_glow_tts.py b/recipes/vctk/glow_tts/train_glow_tts.py new file mode 100644 index 00000000..8c9f5388 --- /dev/null +++ b/recipes/vctk/glow_tts/train_glow_tts.py @@ -0,0 +1,75 @@ +import os + +from TTS.config.shared_configs import BaseAudioConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.glow_tts_config import GlowTTSConfig +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.glow_tts import GlowTTS +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +# set experiment paths +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_path = os.path.join(output_path, "../VCTK/") + +# download the dataset if not downloaded +if not os.path.exists(dataset_path): + from TTS.utils.downloaders import download_vctk + + download_vctk(dataset_path) + +# define dataset config +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=dataset_path) + +# define audio config +# ❗ resample the dataset externally using `TTS/bin/resample.py` and set `resample=False` for faster training +audio_config = BaseAudioConfig(sample_rate=22050, resample=True, do_trim_silence=True, trim_db=23.0) + +# define model config +config = GlowTTSConfig( + batch_size=64, + eval_batch_size=16, + num_loader_workers=4, + num_eval_loader_workers=4, + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="phoneme_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=25, + print_eval=False, + mixed_precision=True, + output_path=output_path, + datasets=[dataset_config], + use_speaker_embedding=True, +) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it maps speaker-id to speaker-name in the model and data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) +config.num_speakers = speaker_manager.num_speakers + +# init model +model = GlowTTS(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/vctk/speedy_speech/train_speedy_speech.py b/recipes/vctk/speedy_speech/train_speedy_speech.py new file mode 100644 index 00000000..81f78d26 --- /dev/null +++ b/recipes/vctk/speedy_speech/train_speedy_speech.py @@ -0,0 +1,80 @@ +import os + +from TTS.config import BaseAudioConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.forward_tts import ForwardTTS +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=23.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = SpeedySpeechConfig( + run_name="fast_pitch_ljspeech", + audio=audio_config, + batch_size=32, + eval_batch_size=16, + num_loader_workers=8, + num_eval_loader_workers=4, + compute_input_seq_cache=True, + compute_f0=True, + f0_cache_path=os.path.join(output_path, "f0_cache"), + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="english_cleaners", + use_phonemes=True, + use_espeak_phonemes=False, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=50, + print_eval=False, + mixed_precision=False, + sort_by_audio_len=True, + max_seq_len=500000, + output_path=output_path, + datasets=[dataset_config], + use_speaker_embedding=True, +) + +# init audio processor +ap = AudioProcessor(**config.audio) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it maps speaker-id to speaker-name in the model and data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) +config.model_args.num_speakers = speaker_manager.num_speakers + +# init model +model = ForwardTTS(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py b/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py new file mode 100644 index 00000000..b0030f17 --- /dev/null +++ b/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py @@ -0,0 +1,80 @@ +import os + +from TTS.config.shared_configs import BaseAudioConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.configs.tacotron_config import TacotronConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.tacotron import Tacotron +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) + +audio_config = BaseAudioConfig( + sample_rate=22050, + resample=True, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training. + do_trim_silence=True, + trim_db=23.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = TacotronConfig( # This is the config that is saved for the future use + audio=audio_config, + batch_size=48, + eval_batch_size=16, + num_loader_workers=4, + num_eval_loader_workers=4, + run_eval=True, + test_delay_epochs=-1, + r=6, + gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]], + double_decoder_consistency=True, + epochs=1000, + text_cleaner="phoneme_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=25, + print_eval=False, + mixed_precision=True, + sort_by_audio_len=True, + min_seq_len=0, + max_seq_len=44000 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio + output_path=output_path, + datasets=[dataset_config], + use_speaker_embedding=True, # set this to enable multi-sepeaker training +) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it mainly handles speaker-id to speaker-name for the model and the data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) + +# init model +model = Tacotron(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py b/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py new file mode 100644 index 00000000..346d650b --- /dev/null +++ b/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py @@ -0,0 +1,87 @@ +import os + +from TTS.config.shared_configs import BaseAudioConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.configs.tacotron2_config import Tacotron2Config +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.tacotron2 import Tacotron2 +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) + +audio_config = BaseAudioConfig( + sample_rate=22050, + resample=False, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training. + do_trim_silence=True, + trim_db=23.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + preemphasis=0.0, +) + +config = Tacotron2Config( # This is the config that is saved for the future use + audio=audio_config, + batch_size=32, + eval_batch_size=16, + num_loader_workers=4, + num_eval_loader_workers=4, + run_eval=True, + test_delay_epochs=-1, + r=2, + # gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]], + double_decoder_consistency=False, + epochs=1000, + text_cleaner="phoneme_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=150, + print_eval=False, + mixed_precision=True, + sort_by_audio_len=True, + min_seq_len=14800, + max_seq_len=22050 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio + output_path=output_path, + datasets=[dataset_config], + use_speaker_embedding=True, # set this to enable multi-sepeaker training + decoder_ssim_alpha=0.0, # disable ssim losses that causes NaN for some runs. + postnet_ssim_alpha=0.0, + postnet_diff_spec_alpha=0.0, + decoder_diff_spec_alpha=0.0, + attention_norm="softmax", + optimizer="Adam", + lr_scheduler=None, + lr=3e-5, +) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it mainly handles speaker-id to speaker-name for the model and the data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) + +# init model +model = Tacotron2(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/recipes/vctk/vits/train_vits.py b/recipes/vctk/vits/train_vits.py new file mode 100644 index 00000000..19074ce3 --- /dev/null +++ b/recipes/vctk/vits/train_vits.py @@ -0,0 +1,86 @@ +import os + +from TTS.config.shared_configs import BaseAudioConfig +from TTS.trainer import Trainer, TrainingArgs +from TTS.tts.configs.shared_configs import BaseDatasetConfig +from TTS.tts.configs.vits_config import VitsConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.models.vits import Vits +from TTS.tts.utils.speakers import SpeakerManager +from TTS.utils.audio import AudioProcessor + +output_path = os.path.dirname(os.path.abspath(__file__)) +dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) + + +audio_config = BaseAudioConfig( + sample_rate=22050, + win_length=1024, + hop_length=256, + num_mels=80, + preemphasis=0.0, + ref_level_db=20, + log_func="np.log", + do_trim_silence=True, + trim_db=23.0, + mel_fmin=0, + mel_fmax=None, + spec_gain=1.0, + signal_norm=False, + do_amp_to_db_linear=False, + resample=True, +) + +config = VitsConfig( + audio=audio_config, + run_name="vits_vctk", + use_speaker_embedding=True, + batch_size=32, + eval_batch_size=16, + batch_group_size=5, + num_loader_workers=4, + num_eval_loader_workers=4, + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="english_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + compute_input_seq_cache=True, + print_step=25, + print_eval=False, + mixed_precision=True, + sort_by_audio_len=True, + min_seq_len=32 * 256 * 4, + max_seq_len=1500000, + output_path=output_path, + datasets=[dataset_config], +) + +# init audio processor +ap = AudioProcessor(**config.audio.to_dict()) + +# load training samples +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) + +# init speaker manager for multi-speaker training +# it maps speaker-id to speaker-name in the model and data-loader +speaker_manager = SpeakerManager() +speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) +config.model_args.num_speakers = speaker_manager.num_speakers + +# init model +model = Vits(config, speaker_manager) + +# init the trainer and 🚀 +trainer = Trainer( + TrainingArgs(), + config, + output_path, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + training_assets={"audio_processor": ap}, +) +trainer.fit() diff --git a/setup.py b/setup.py index 53abe468..95f0841b 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,24 @@ #!/usr/bin/env python +# ,*++++++*, ,*++++++*, +# *++. .+++ *++. .++* +# *+* ,++++* *+* *+* ,++++, *+* +# ,+, .++++++++++* ,++,,,,*+, ,++++++++++. *+, +# *+. .++++++++++++..++ *+.,++++++++++++. .+* +# .+* ++++++++++++.*+, .+*.++++++++++++ *+, +# .++ *++++++++* ++, .++.*++++++++* ++, +# ,+++*. . .*++, ,++*. .*+++* +# *+, .,*++**. .**++**. ,+* +# .+* *+, +# *+. .+* +# *+* +++ +++ *+* +# .+++*. . . *+++. +# ,+* *+++*... ...*+++* *+, +# .++. .""""+++++++****+++++++"""". ++. +# ,++. .++, +# .++* *++. +# *+++, ,+++* +# .,*++++::::::++++*,. +# `````` import os import subprocess diff --git a/tests/test_stft_torch.py b/tests/aux_tests/__init__.py similarity index 100% rename from tests/test_stft_torch.py rename to tests/aux_tests/__init__.py diff --git a/tests/model_manager.py b/tests/aux_tests/model_manager.py similarity index 100% rename from tests/model_manager.py rename to tests/aux_tests/model_manager.py diff --git a/tests/test_audio_processor.py b/tests/aux_tests/test_audio_processor.py similarity index 100% rename from tests/test_audio_processor.py rename to tests/aux_tests/test_audio_processor.py diff --git a/tests/test_extract_tts_spectrograms.py b/tests/aux_tests/test_extract_tts_spectrograms.py similarity index 100% rename from tests/test_extract_tts_spectrograms.py rename to tests/aux_tests/test_extract_tts_spectrograms.py diff --git a/tests/test_speaker_encoder.py b/tests/aux_tests/test_speaker_encoder.py similarity index 100% rename from tests/test_speaker_encoder.py rename to tests/aux_tests/test_speaker_encoder.py diff --git a/tests/test_speaker_encoder_train.py b/tests/aux_tests/test_speaker_encoder_train.py similarity index 100% rename from tests/test_speaker_encoder_train.py rename to tests/aux_tests/test_speaker_encoder_train.py diff --git a/tests/test_speaker_manager.py b/tests/aux_tests/test_speaker_manager.py similarity index 100% rename from tests/test_speaker_manager.py rename to tests/aux_tests/test_speaker_manager.py diff --git a/tests/aux_tests/test_stft_torch.py b/tests/aux_tests/test_stft_torch.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_text_processing.py b/tests/aux_tests/test_text_processing.py similarity index 100% rename from tests/test_text_processing.py rename to tests/aux_tests/test_text_processing.py diff --git a/tests/data_tests/test_loader.py b/tests/data_tests/test_loader.py index 18066ef3..8a20c261 100644 --- a/tests/data_tests/test_loader.py +++ b/tests/data_tests/test_loader.py @@ -7,7 +7,7 @@ import torch from torch.utils.data import DataLoader from tests import get_tests_output_path -from TTS.tts.configs import BaseTTSConfig +from TTS.tts.configs.shared_configs import BaseTTSConfig from TTS.tts.datasets import TTSDataset from TTS.tts.datasets.formatters import ljspeech from TTS.utils.audio import AudioProcessor diff --git a/tests/tts_tests/test_align_tts_train.py b/tests/tts_tests/test_align_tts_train.py index f04a2358..f5d60d7c 100644 --- a/tests/tts_tests/test_align_tts_train.py +++ b/tests/tts_tests/test_align_tts_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import AlignTTSConfig +from TTS.tts.configs.align_tts_config import AlignTTSConfig config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_fast_pitch_train.py b/tests/tts_tests/test_fast_pitch_train.py index 89176ac9..71ba8b25 100644 --- a/tests/tts_tests/test_fast_pitch_train.py +++ b/tests/tts_tests/test_fast_pitch_train.py @@ -4,7 +4,7 @@ import shutil from tests import get_device_id, get_tests_output_path, run_cli from TTS.config.shared_configs import BaseAudioConfig -from TTS.tts.configs import FastPitchConfig +from TTS.tts.configs.fast_pitch_config import FastPitchConfig config_path = os.path.join(get_tests_output_path(), "test_fast_pitch_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_glow_tts.py b/tests/tts_tests/test_glow_tts.py index e139562c..82d0ec3b 100644 --- a/tests/tts_tests/test_glow_tts.py +++ b/tests/tts_tests/test_glow_tts.py @@ -6,7 +6,7 @@ import torch from torch import optim from tests import get_tests_input_path -from TTS.tts.configs import GlowTTSConfig +from TTS.tts.configs.glow_tts_config import GlowTTSConfig from TTS.tts.layers.losses import GlowTTSLoss from TTS.tts.models.glow_tts import GlowTTS from TTS.utils.audio import AudioProcessor diff --git a/tests/tts_tests/test_glow_tts_train.py b/tests/tts_tests/test_glow_tts_train.py index 24c5c4cf..e5901076 100644 --- a/tests/tts_tests/test_glow_tts_train.py +++ b/tests/tts_tests/test_glow_tts_train.py @@ -3,14 +3,14 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import GlowTTSConfig +from TTS.tts.configs.glow_tts_config import GlowTTSConfig config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") config = GlowTTSConfig( - batch_size=8, + batch_size=2, eval_batch_size=8, num_loader_workers=0, num_eval_loader_workers=0, @@ -27,6 +27,7 @@ config = GlowTTSConfig( test_sentences=[ "Be a voice, not an echo.", ], + data_dep_init_steps=1.0, ) config.audio.do_trim_silence = True config.audio.trim_db = 60 diff --git a/tests/tts_tests/test_speedy_speech_train.py b/tests/tts_tests/test_speedy_speech_train.py index a181ac24..7d7ecc7c 100644 --- a/tests/tts_tests/test_speedy_speech_train.py +++ b/tests/tts_tests/test_speedy_speech_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import SpeedySpeechConfig +from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_tacotron2_d-vectors_train.py b/tests/tts_tests/test_tacotron2_d-vectors_train.py index 1a8d78bf..c817badc 100644 --- a/tests/tts_tests/test_tacotron2_d-vectors_train.py +++ b/tests/tts_tests/test_tacotron2_d-vectors_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import Tacotron2Config +from TTS.tts.configs.tacotron2_config import Tacotron2Config config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") @@ -23,7 +23,7 @@ config = Tacotron2Config( epochs=1, print_step=1, print_eval=True, - use_speaker_embedding=True, + use_speaker_embedding=False, use_d_vector_file=True, test_sentences=[ "Be a voice, not an echo.", diff --git a/tests/tts_tests/test_tacotron2_model.py b/tests/tts_tests/test_tacotron2_model.py index 65d2bd9d..df184a6a 100644 --- a/tests/tts_tests/test_tacotron2_model.py +++ b/tests/tts_tests/test_tacotron2_model.py @@ -6,8 +6,8 @@ import torch from torch import nn, optim from tests import get_tests_input_path -from TTS.tts.configs import Tacotron2Config from TTS.tts.configs.shared_configs import GSTConfig +from TTS.tts.configs.tacotron2_config import Tacotron2Config from TTS.tts.layers.losses import MSELossMasked from TTS.tts.models.tacotron2 import Tacotron2 from TTS.utils.audio import AudioProcessor @@ -114,7 +114,7 @@ class MultiSpeakerTacotronTrainTest(unittest.TestCase): assert (param - param_ref).sum() == 0, param count += 1 optimizer = optim.Adam(model.parameters(), lr=config.lr) - for i in range(5): + for _ in range(5): outputs = model.forward( input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids} ) diff --git a/tests/tts_tests/test_tacotron2_speaker_emb_train.py b/tests/tts_tests/test_tacotron2_speaker_emb_train.py index 41d694f6..095016d8 100644 --- a/tests/tts_tests/test_tacotron2_speaker_emb_train.py +++ b/tests/tts_tests/test_tacotron2_speaker_emb_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import Tacotron2Config +from TTS.tts.configs.tacotron2_config import Tacotron2Config config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_tacotron2_tf_model.py b/tests/tts_tests/test_tacotron2_tf_model.py index 515a6834..fb1efcde 100644 --- a/tests/tts_tests/test_tacotron2_tf_model.py +++ b/tests/tts_tests/test_tacotron2_tf_model.py @@ -5,7 +5,7 @@ import numpy as np import tensorflow as tf import torch -from TTS.tts.configs import Tacotron2Config +from TTS.tts.configs.tacotron2_config import Tacotron2Config from TTS.tts.tf.models.tacotron2 import Tacotron2 from TTS.tts.tf.utils.tflite import convert_tacotron2_to_tflite, load_tflite_model diff --git a/tests/tts_tests/test_tacotron2_train.py b/tests/tts_tests/test_tacotron2_train.py index e947a54a..4f37ef89 100644 --- a/tests/tts_tests/test_tacotron2_train.py +++ b/tests/tts_tests/test_tacotron2_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import Tacotron2Config +from TTS.tts.configs.tacotron2_config import Tacotron2Config config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_tacotron2_train_fsspec_path.py b/tests/tts_tests/test_tacotron2_train_fsspec_path.py index 9e4ee102..5d14a983 100644 --- a/tests/tts_tests/test_tacotron2_train_fsspec_path.py +++ b/tests/tts_tests/test_tacotron2_train_fsspec_path.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import Tacotron2Config +from TTS.tts.configs.tacotron2_config import Tacotron2Config config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_tacotron_model.py b/tests/tts_tests/test_tacotron_model.py index 3f570276..6e0e712b 100644 --- a/tests/tts_tests/test_tacotron_model.py +++ b/tests/tts_tests/test_tacotron_model.py @@ -6,7 +6,8 @@ import torch from torch import nn, optim from tests import get_tests_input_path -from TTS.tts.configs import GSTConfig, TacotronConfig +from TTS.tts.configs.shared_configs import GSTConfig +from TTS.tts.configs.tacotron_config import TacotronConfig from TTS.tts.layers.losses import L1LossMasked from TTS.tts.models.tacotron import Tacotron from TTS.utils.audio import AudioProcessor diff --git a/tests/tts_tests/test_tacotron_train.py b/tests/tts_tests/test_tacotron_train.py index 0c35ee28..68071c66 100644 --- a/tests/tts_tests/test_tacotron_train.py +++ b/tests/tts_tests/test_tacotron_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import TacotronConfig +from TTS.tts.configs.tacotron_config import TacotronConfig config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/tts_tests/test_vits_train.py b/tests/tts_tests/test_vits_train.py index db9d2fc1..6398955e 100644 --- a/tests/tts_tests/test_vits_train.py +++ b/tests/tts_tests/test_vits_train.py @@ -3,7 +3,7 @@ import os import shutil from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs import VitsConfig +from TTS.tts.configs.vits_config import VitsConfig config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") diff --git a/tests/vocoder_tests/test_vocoder_wavernn.py b/tests/vocoder_tests/test_vocoder_wavernn.py index b5c769ee..d4a7b8dd 100644 --- a/tests/vocoder_tests/test_vocoder_wavernn.py +++ b/tests/vocoder_tests/test_vocoder_wavernn.py @@ -12,7 +12,7 @@ def test_wavernn(): config.model_args = WavernnArgs( rnn_dims=512, fc_dims=512, - mode=10, + mode="mold", mulaw=False, pad=2, use_aux_net=True, @@ -37,13 +37,13 @@ def test_wavernn(): assert np.all(output.shape == (2, 1280, 30)), output.shape # mode: gauss - config.model_params.mode = "gauss" + config.model_args.mode = "gauss" model = Wavernn(config) output = model(dummy_x, dummy_m) assert np.all(output.shape == (2, 1280, 2)), output.shape # mode: quantized - config.model_params.mode = 4 + config.model_args.mode = 4 model = Wavernn(config) output = model(dummy_x, dummy_m) assert np.all(output.shape == (2, 1280, 2 ** 4)), output.shape diff --git a/tests/vocoder_tests/test_wavernn_train.py b/tests/vocoder_tests/test_wavernn_train.py index 43fc5fb1..337e2425 100644 --- a/tests/vocoder_tests/test_wavernn_train.py +++ b/tests/vocoder_tests/test_wavernn_train.py @@ -11,7 +11,7 @@ output_path = os.path.join(get_tests_output_path(), "train_outputs") config = WavernnConfig( - model_params=WavernnArgs(), + model_args=WavernnArgs(), batch_size=8, eval_batch_size=8, num_loader_workers=0,