Merge branch 'dev' of github.com:idiap/coqui-ai-TTS into fix/macos-stream-generator

.cardboardlint.yml

@@ -1,5 +0,0 @@
-linters:
-- pylint:
-    # pylintrc: pylintrc
-    filefilter: ['- test_*.py', '+ *.py', '- *.npy']
-    # exclude:

.dockerignore

@@ -6,4 +6,4 @@ TTS.egg-info/
 tests/outputs/*
 tests/train_outputs/*
 __pycache__/
-*.pyc
+*.pyc

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -59,7 +59,7 @@ body:
         You can either run `TTS/bin/collect_env_info.py`
 
         ```bash
-        wget https://raw.githubusercontent.com/coqui-ai/TTS/main/TTS/bin/collect_env_info.py
+        wget https://raw.githubusercontent.com/idiap/coqui-ai-TTS/main/TTS/bin/collect_env_info.py
         python collect_env_info.py
         ```
 

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +1,8 @@
 blank_issues_enabled: false
 contact_links:
   - name: CoquiTTS GitHub Discussions
-    url: https://github.com/coqui-ai/TTS/discussions
+    url: https://github.com/idiap/coqui-ai-TTS/discussions
     about: Please ask and answer questions here.
   - name: Coqui Security issue disclosure
-    url: mailto:info@coqui.ai
+    url: mailto:enno.hermann@gmail.com
     about: Please report security vulnerabilities here.

.github/PR_TEMPLATE.md

@@ -5,11 +5,3 @@ Welcome to the 🐸TTS project! We are excited to see your interest, and appreci
 This repository is governed by the Contributor Covenant Code of Conduct. For more details, see the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file.
 
 In order to make a good pull request, please see our [CONTRIBUTING.md](CONTRIBUTING.md) file.
-
-Before accepting your pull request, you will be asked to sign a [Contributor License Agreement](https://cla-assistant.io/coqui-ai/TTS).
-
-This [Contributor License Agreement](https://cla-assistant.io/coqui-ai/TTS):
-
-- Protects you, Coqui, and the users of the code.
-- Does not change your rights to use your contributions for any purpose.
-- Does not change the license of the 🐸TTS project. It just makes the terms of your contribution clearer and lets us know you are OK to contribute.

.github/stale.yml

@@ -15,4 +15,3 @@ markComment: >
   for your contributions. You might also look our discussion channels.
 # Comment to post when closing a stale issue. Set to `false` to disable
 closeComment: false
-

.github/workflows/aux_tests.yml

@@ -1,51 +0,0 @@
-name: aux-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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make test_aux

.github/workflows/data_tests.yml

@@ -1,51 +0,0 @@
-name: data-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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends git make gcc
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make data_tests

.github/workflows/docker.yaml

@@ -10,7 +10,7 @@ on:
 jobs:
   docker-build:
     name: "Build and push Docker image"
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     strategy:
       matrix:
         arch: ["amd64"]
@@ -18,7 +18,7 @@ jobs:
         - "nvidia/cuda:11.8.0-base-ubuntu22.04" # GPU enabled
         - "python:3.10.8-slim" # CPU only
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Log in to the Container registry
         uses: docker/login-action@v1
         with:
@@ -29,11 +29,11 @@ jobs:
         id: compute-tag
         run: |
           set -ex
-          base="ghcr.io/coqui-ai/tts"
+          base="ghcr.io/idiap/coqui-tts"
           tags="" # PR build
 
           if [[ ${{ matrix.base }} = "python:3.10.8-slim" ]]; then
-            base="ghcr.io/coqui-ai/tts-cpu"
+            base="ghcr.io/idiap/coqui-tts-cpu"
           fi
 
           if [[ "${{ startsWith(github.ref, 'refs/heads/') }}" = "true" ]]; then
@@ -42,7 +42,7 @@ jobs:
             branch=${github_ref#*refs/heads/} # strip prefix to get branch name
             tags="${base}:${branch},${base}:${{ github.sha }},"
           elif [[ "${{ startsWith(github.ref, 'refs/tags/') }}" = "true" ]]; then
-            VERSION="v$(cat TTS/VERSION)"
+            VERSION="v$(grep -m 1 version pyproject.toml | grep -P '\d+\.\d+\.\d+' -o)"
             if [[ "${{ github.ref }}" != "refs/tags/${VERSION}" ]]; then
               echo "Pushed tag does not match VERSION file. Aborting push."
               exit 1
@@ -63,3 +63,58 @@ jobs:
           push: ${{ github.event_name == 'push' }}
           build-args: "BASE=${{ matrix.base }}"
           tags: ${{ steps.compute-tag.outputs.tags }}
+  docker-dev-build:
+    name: "Build the development Docker image"
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        arch: ["amd64"]
+        base:
+        - "nvidia/cuda:11.8.0-base-ubuntu22.04" # GPU enabled
+    steps:
+      - uses: actions/checkout@v4
+      - name: Log in to the Container registry
+        uses: docker/login-action@v1
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Compute Docker tags, check VERSION file matches tag
+        id: compute-tag
+        run: |
+          set -ex
+          base="ghcr.io/idiap/coqui-tts-dev"
+          tags="" # PR build
+
+          if [[ ${{ matrix.base }} = "python:3.10.8-slim" ]]; then
+            base="ghcr.io/idiap/coqui-tts-dev-cpu"
+          fi
+
+          if [[ "${{ startsWith(github.ref, 'refs/heads/') }}" = "true" ]]; then
+            # Push to branch
+            github_ref="${{ github.ref }}"
+            branch=${github_ref#*refs/heads/} # strip prefix to get branch name
+            tags="${base}:${branch},${base}:${{ github.sha }},"
+          elif [[ "${{ startsWith(github.ref, 'refs/tags/') }}" = "true" ]]; then
+            VERSION="v$(grep -m 1 version pyproject.toml | grep -P '\d+\.\d+\.\d+' -o)"
+            if [[ "${{ github.ref }}" != "refs/tags/${VERSION}" ]]; then
+              echo "Pushed tag does not match VERSION file. Aborting push."
+              exit 1
+            fi
+            tags="${base}:${VERSION},${base}:latest,${base}:${{ github.sha }}"
+          fi
+          echo "::set-output name=tags::${tags}"
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v1
+      - name: Set up Docker Buildx
+        id: buildx
+        uses: docker/setup-buildx-action@v1
+      - name: Build and push
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: dockerfiles/Dockerfile.dev
+          platforms: linux/${{ matrix.arch }}
+          push: false
+          build-args: "BASE=${{ matrix.base }}"
+          tags: ${{ steps.compute-tag.outputs.tags }}

.github/workflows/inference_tests.yml

@@ -1,53 +0,0 @@
-name: inference_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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: |
-          export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends git make gcc
-          sudo apt-get install espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make inference_tests

.github/workflows/pypi-release.yml

@@ -8,18 +8,18 @@ defaults:
       bash
 jobs:
   build-sdist:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Verify tag matches version
         run: |
           set -ex
-          version=$(cat TTS/VERSION)
+          version=$(grep -m 1 version pyproject.toml | grep -P '\d+\.\d+\.\d+' -o)
           tag="${GITHUB_REF/refs\/tags\/}"
           if [[ "v$version" != "$tag" ]]; then
             exit 1
           fi
-      - uses: actions/setup-python@v2
+      - uses: actions/setup-python@v5
         with:
           python-version: 3.9
       - run: |
@@ -28,67 +28,63 @@ jobs:
           python -m build
       - run: |
           pip install dist/*.tar.gz
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v4
         with:
           name: sdist
           path: dist/*.tar.gz
   build-wheels:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v2
+      - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install pip requirements
+      - name: Install build requirements
         run: |
-          python -m pip install -U pip setuptools wheel build
+          python -m pip install -U pip setuptools wheel build numpy cython
-          python -m pip install -r requirements.txt
       - name: Setup and install manylinux1_x86_64 wheel
         run: |
           python setup.py bdist_wheel --plat-name=manylinux1_x86_64
           python -m pip install dist/*-manylinux*.whl
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v4
         with:
           name: wheel-${{ matrix.python-version }}
           path: dist/*-manylinux*.whl
   publish-artifacts:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     needs: [build-sdist, build-wheels]
+    environment:
+      name: release
+      url: https://pypi.org/p/coqui-tts
+    permissions:
+      id-token: write
     steps:
       - run: |
           mkdir dist
-      - uses: actions/download-artifact@v2
+      - uses: actions/download-artifact@v4
         with:
           name: "sdist"
           path: "dist/"
-      - uses: actions/download-artifact@v2
+      - uses: actions/download-artifact@v4
         with:
           name: "wheel-3.9"
           path: "dist/"
-      - uses: actions/download-artifact@v2
+      - uses: actions/download-artifact@v4
         with:
           name: "wheel-3.10"
           path: "dist/"
-      - uses: actions/download-artifact@v2
+      - uses: actions/download-artifact@v4
         with:
           name: "wheel-3.11"
           path: "dist/"
+      - uses: actions/download-artifact@v4
+        with:
+          name: "wheel-3.12"
+          path: "dist/"
       - run: |
           ls -lh dist/
-      - name: Setup PyPI config
+      - name: Publish package distributions to PyPI
-        run: |
+        uses: pypa/gh-action-pypi-publish@release/v1
-          cat << EOF > ~/.pypirc
-          [pypi]
-          username=__token__
-          password=${{ secrets.PYPI_TOKEN }}
-          EOF
-      - uses: actions/setup-python@v2
-        with:
-          python-version: 3.9
-      - run: |
-          python -m pip install twine
-      - run: |
-          twine upload --repository pypi dist/*

.github/workflows/style_check.yml

@@ -7,12 +7,6 @@ on:
   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:
@@ -21,26 +15,15 @@ jobs:
         python-version: [3.9]
         experimental: [false]
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           architecture: x64
           cache: 'pip'
           cache-dependency-path: 'requirements*'
-      - name: check OS
+      - name: Install/upgrade dev dependencies
-        run: cat /etc/os-release
+        run: python3 -m pip install -r requirements.dev.txt
-      - name: Install dependencies
+      - name: Lint check
-        run: |
+        run: make lint
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Style check
-        run: make style

.github/workflows/tests.yml

@@ -0,0 +1,81 @@
+name: tests
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    types: [opened, synchronize, reopened]
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [3.9, "3.10", "3.11", "3.12"]
+        subset: ["data_tests", "inference_tests", "test_aux", "test_text", "test_tts", "test_tts2", "test_vocoder", "test_xtts", "test_zoo0", "test_zoo1", "test_zoo2"]
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          architecture: x64
+          cache: 'pip'
+          cache-dependency-path: 'requirements*'
+      - name: check OS
+        run: cat /etc/os-release
+      - name: set ENV
+        run: export TRAINER_TELEMETRY=0
+      - name: Install Espeak
+        if: contains(fromJSON('["inference_tests", "test_text", "test_tts", "test_tts2", "test_xtts", "test_zoo0", "test_zoo1", "test_zoo2"]'), matrix.subset)
+        run: |
+          sudo apt-get update
+          sudo apt-get install espeak espeak-ng
+      - name: Install dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y --no-install-recommends git make gcc
+          make system-deps
+      - name: Install/upgrade Python setup deps
+        run: python3 -m pip install --upgrade pip setuptools wheel uv
+      - name: Replace scarf urls
+        if: contains(fromJSON('["data_tests", "inference_tests", "test_aux", "test_tts", "test_tts2", "test_xtts", "test_zoo0", "test_zoo1", "test_zoo2"]'), matrix.subset)
+        run: |
+          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
+      - name: Install TTS
+        run: |
+          resolution=highest
+          if [ "${{ matrix.python-version }}" == "3.9" ]; then
+            resolution=lowest-direct
+          fi
+          python3 -m uv pip install --resolution=$resolution --system "coqui-tts[dev,server,languages] @ ."
+      - name: Unit tests
+        run: make ${{ matrix.subset }}
+      - name: Upload coverage data
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-data-${{ matrix.subset }}-${{ matrix.python-version }}
+          path: .coverage.*
+          if-no-files-found: ignore
+  coverage:
+    if: always()
+    needs: test
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+      - uses: actions/download-artifact@v4
+        with:
+          pattern: coverage-data-*
+          merge-multiple: true
+      - name: Combine coverage
+        run: |
+          python -Im pip install --upgrade coverage[toml]
+
+          python -Im coverage combine
+          python -Im coverage html --skip-covered --skip-empty
+
+          python -Im coverage report --format=markdown >> $GITHUB_STEP_SUMMARY

.github/workflows/text_tests.yml

@@ -1,50 +0,0 @@
-name: text-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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends git make gcc
-          sudo apt-get install espeak
-          sudo apt-get install espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make test_text

.github/workflows/tts_tests.yml

@@ -1,53 +0,0 @@
-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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends git make gcc
-          sudo apt-get install espeak
-          sudo apt-get install espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make test_tts

.github/workflows/tts_tests2.yml

@@ -1,53 +0,0 @@
-name: tts-tests2
-
-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, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends git make gcc
-          sudo apt-get install espeak
-          sudo apt-get install espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make test_tts2

.github/workflows/vocoder_tests.yml

@@ -1,48 +0,0 @@
-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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make test_vocoder

.github/workflows/xtts_tests.yml

@@ -1,53 +0,0 @@
-name: xtts-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: [3.9, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends git make gcc
-          sudo apt-get install espeak
-          sudo apt-get install espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: make test_xtts

.github/workflows/zoo_tests0.yml

@@ -1,54 +0,0 @@
-name: zoo-tests-0
-
-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, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          sudo apt-get install espeak espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: |
-          nose2 -F -v -B TTS tests.zoo_tests.test_models.test_models_offset_0_step_3
-          nose2 -F -v -B TTS tests.zoo_tests.test_models.test_voice_conversion

.github/workflows/zoo_tests1.yml

@@ -1,53 +0,0 @@
-name: zoo-tests-1
-
-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, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          sudo apt-get install espeak espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\/hf\/bark\//https:\/\/huggingface.co\/erogol\/bark\/resolve\/main\//g' TTS/.models.json
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: nose2 -F -v -B --with-coverage --coverage TTS tests.zoo_tests.test_models.test_models_offset_1_step_3

.github/workflows/zoo_tests2.yml

@@ -1,52 +0,0 @@
-name: zoo-tests-2
-
-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, "3.10", "3.11"]
-        experimental: [false]
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-          cache: 'pip'
-          cache-dependency-path: 'requirements*'
-      - name: check OS
-        run: cat /etc/os-release
-      - name: set ENV
-        run: export TRAINER_TELEMETRY=0
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          sudo apt-get install espeak espeak-ng
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Replace scarf urls
-        run: |
-          sed -i 's/https:\/\/coqui.gateway.scarf.sh\//https:\/\/github.com\/coqui-ai\/TTS\/releases\/download\//g' TTS/.models.json
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Unit tests
-        run: nose2 -F -v -B --with-coverage --coverage TTS tests.zoo_tests.test_models.test_models_offset_2_step_3

.gitignore

@@ -169,4 +169,4 @@ wandb
 depot/*
 coqui_recipes/*
 local_scripts/*
-coqui_demos/*
+coqui_demos/*

.pre-commit-config.yaml

@@ -1,27 +1,24 @@
 repos:
-  - repo: 'https://github.com/pre-commit/pre-commit-hooks'
+  - repo: "https://github.com/pre-commit/pre-commit-hooks"
-    rev: v2.3.0
+    rev: v4.5.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
-  - repo: 'https://github.com/psf/black'
+  - repo: "https://github.com/psf/black"
-    rev: 22.3.0
+    rev: 24.2.0
     hooks:
       - id: black
         language_version: python3
-  - repo: https://github.com/pycqa/isort
+  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 5.8.0
+    rev: v0.3.0
     hooks:
-      - id: isort
+      - id: ruff
-        name: isort (python)
+        args: [--fix, --exit-non-zero-on-fix]
-      - id: isort
+  - repo: local
-        name: isort (cython)
-        types: [cython]
-      - id: isort
-        name: isort (pyi)
-        types: [pyi]
-  - repo: https://github.com/pycqa/pylint
-    rev: v2.8.2
     hooks:
-    -   id: pylint
+      - id: generate_requirements.py
+        name: generate_requirements.py
+        language: system
+        entry: python scripts/generate_requirements.py
+        files: "pyproject.toml|requirements.*\\.txt|tools/generate_requirements.py"

.pylintrc

@@ -1,599 +0,0 @@
-[MASTER]
-
-# A comma-separated list of package or module names from where C extensions may
-# be loaded. Extensions are loading into the active Python interpreter and may
-# run arbitrary code.
-extension-pkg-whitelist=
-
-# Add files or directories to the blacklist. They should be base names, not
-# paths.
-ignore=CVS
-
-# Add files or directories matching the regex patterns to the blacklist. The
-# regex matches against base names, not paths.
-ignore-patterns=
-
-# Python code to execute, usually for sys.path manipulation such as
-# pygtk.require().
-#init-hook=
-
-# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
-# number of processors available to use.
-jobs=1
-
-# Control the amount of potential inferred values when inferring a single
-# object. This can help the performance when dealing with large functions or
-# complex, nested conditions.
-limit-inference-results=100
-
-# List of plugins (as comma separated values of python modules names) to load,
-# usually to register additional checkers.
-load-plugins=
-
-# Pickle collected data for later comparisons.
-persistent=yes
-
-# Specify a configuration file.
-#rcfile=
-
-# When enabled, pylint would attempt to guess common misconfiguration and emit
-# user-friendly hints instead of false-positive error messages.
-suggestion-mode=yes
-
-# Allow loading of arbitrary C extensions. Extensions are imported into the
-# active Python interpreter and may run arbitrary code.
-unsafe-load-any-extension=no
-
-
-[MESSAGES CONTROL]
-
-# Only show warnings with the listed confidence levels. Leave empty to show
-# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
-confidence=
-
-# Disable the message, report, category or checker with the given id(s). You
-# can either give multiple identifiers separated by comma (,) or put this
-# option multiple times (only on the command line, not in the configuration
-# file where it should appear only once). You can also use "--disable=all" to
-# disable everything first and then reenable specific checks. For example, if
-# you want to run only the similarities checker, you can use "--disable=all
-# --enable=similarities". If you want to run only the classes checker, but have
-# no Warning level messages displayed, use "--disable=all --enable=classes
-# --disable=W".
-disable=missing-docstring,
-        too-many-public-methods,
-        too-many-lines,
-        bare-except,
-        ## for avoiding weird p3.6 CI linter error
-        ## TODO: see later if we can remove this
-        assigning-non-slot,
-        unsupported-assignment-operation,
-        ## end
-        line-too-long,
-        fixme,
-        wrong-import-order,
-        ungrouped-imports,
-        wrong-import-position,
-        import-error,
-        invalid-name,
-        too-many-instance-attributes,
-        arguments-differ,
-        arguments-renamed,
-        no-name-in-module,
-        no-member,
-        unsubscriptable-object,
-        print-statement,
-        parameter-unpacking,
-        unpacking-in-except,
-        old-raise-syntax,
-        backtick,
-        long-suffix,
-        old-ne-operator,
-        old-octal-literal,
-        import-star-module-level,
-        non-ascii-bytes-literal,
-        raw-checker-failed,
-        bad-inline-option,
-        locally-disabled,
-        file-ignored,
-        suppressed-message,
-        useless-suppression,
-        deprecated-pragma,
-        use-symbolic-message-instead,
-        useless-object-inheritance,
-        too-few-public-methods,
-        too-many-branches,
-        too-many-arguments,
-        too-many-locals,
-        too-many-statements,
-        apply-builtin,
-        basestring-builtin,
-        buffer-builtin,
-        cmp-builtin,
-        coerce-builtin,
-        execfile-builtin,
-        file-builtin,
-        long-builtin,
-        raw_input-builtin,
-        reduce-builtin,
-        standarderror-builtin,
-        unicode-builtin,
-        xrange-builtin,
-        coerce-method,
-        delslice-method,
-        getslice-method,
-        setslice-method,
-        no-absolute-import,
-        old-division,
-        dict-iter-method,
-        dict-view-method,
-        next-method-called,
-        metaclass-assignment,
-        indexing-exception,
-        raising-string,
-        reload-builtin,
-        oct-method,
-        hex-method,
-        nonzero-method,
-        cmp-method,
-        input-builtin,
-        round-builtin,
-        intern-builtin,
-        unichr-builtin,
-        map-builtin-not-iterating,
-        zip-builtin-not-iterating,
-        range-builtin-not-iterating,
-        filter-builtin-not-iterating,
-        using-cmp-argument,
-        eq-without-hash,
-        div-method,
-        idiv-method,
-        rdiv-method,
-        exception-message-attribute,
-        invalid-str-codec,
-        sys-max-int,
-        bad-python3-import,
-        deprecated-string-function,
-        deprecated-str-translate-call,
-        deprecated-itertools-function,
-        deprecated-types-field,
-        next-method-defined,
-        dict-items-not-iterating,
-        dict-keys-not-iterating,
-        dict-values-not-iterating,
-        deprecated-operator-function,
-        deprecated-urllib-function,
-        xreadlines-attribute,
-        deprecated-sys-function,
-        exception-escape,
-        comprehension-escape,
-        duplicate-code,
-        not-callable,
-        import-outside-toplevel,
-        logging-fstring-interpolation,
-        logging-not-lazy
-
-# Enable the message, report, category or checker with the given id(s). You can
-# either give multiple identifier separated by comma (,) or put this option
-# multiple time (only on the command line, not in the configuration file where
-# it should appear only once). See also the "--disable" option for examples.
-enable=c-extension-no-member
-
-
-[REPORTS]
-
-# Python expression which should return a note less than 10 (10 is the highest
-# note). You have access to the variables errors warning, statement which
-# respectively contain the number of errors / warnings messages and the total
-# number of statements analyzed. This is used by the global evaluation report
-# (RP0004).
-evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
-
-# Template used to display messages. This is a python new-style format string
-# used to format the message information. See doc for all details.
-#msg-template=
-
-# Set the output format. Available formats are text, parseable, colorized, json
-# and msvs (visual studio). You can also give a reporter class, e.g.
-# mypackage.mymodule.MyReporterClass.
-output-format=text
-
-# Tells whether to display a full report or only the messages.
-reports=no
-
-# Activate the evaluation score.
-score=yes
-
-
-[REFACTORING]
-
-# Maximum number of nested blocks for function / method body
-max-nested-blocks=5
-
-# Complete name of functions that never returns. When checking for
-# inconsistent-return-statements if a never returning function is called then
-# it will be considered as an explicit return statement and no message will be
-# printed.
-never-returning-functions=sys.exit
-
-
-[LOGGING]
-
-# Format style used to check logging format string. `old` means using %
-# formatting, while `new` is for `{}` formatting.
-logging-format-style=old
-
-# Logging modules to check that the string format arguments are in logging
-# function parameter format.
-logging-modules=logging
-
-
-[SPELLING]
-
-# Limits count of emitted suggestions for spelling mistakes.
-max-spelling-suggestions=4
-
-# Spelling dictionary name. Available dictionaries: none. To make it working
-# install python-enchant package..
-spelling-dict=
-
-# List of comma separated words that should not be checked.
-spelling-ignore-words=
-
-# A path to a file that contains private dictionary; one word per line.
-spelling-private-dict-file=
-
-# Tells whether to store unknown words to indicated private dictionary in
-# --spelling-private-dict-file option instead of raising a message.
-spelling-store-unknown-words=no
-
-
-[MISCELLANEOUS]
-
-# List of note tags to take in consideration, separated by a comma.
-notes=FIXME,
-      XXX,
-      TODO
-
-
-[TYPECHECK]
-
-# List of decorators that produce context managers, such as
-# contextlib.contextmanager. Add to this list to register other decorators that
-# produce valid context managers.
-contextmanager-decorators=contextlib.contextmanager
-
-# List of members which are set dynamically and missed by pylint inference
-# system, and so shouldn't trigger E1101 when accessed. Python regular
-# expressions are accepted.
-generated-members=numpy.*,torch.*
-
-# Tells whether missing members accessed in mixin class should be ignored. A
-# mixin class is detected if its name ends with "mixin" (case insensitive).
-ignore-mixin-members=yes
-
-# Tells whether to warn about missing members when the owner of the attribute
-# is inferred to be None.
-ignore-none=yes
-
-# This flag controls whether pylint should warn about no-member and similar
-# checks whenever an opaque object is returned when inferring. The inference
-# can return multiple potential results while evaluating a Python object, but
-# some branches might not be evaluated, which results in partial inference. In
-# that case, it might be useful to still emit no-member and other checks for
-# the rest of the inferred objects.
-ignore-on-opaque-inference=yes
-
-# List of class names for which member attributes should not be checked (useful
-# for classes with dynamically set attributes). This supports the use of
-# qualified names.
-ignored-classes=optparse.Values,thread._local,_thread._local
-
-# List of module names for which member attributes should not be checked
-# (useful for modules/projects where namespaces are manipulated during runtime
-# and thus existing member attributes cannot be deduced by static analysis. It
-# supports qualified module names, as well as Unix pattern matching.
-ignored-modules=
-
-# Show a hint with possible names when a member name was not found. The aspect
-# of finding the hint is based on edit distance.
-missing-member-hint=yes
-
-# The minimum edit distance a name should have in order to be considered a
-# similar match for a missing member name.
-missing-member-hint-distance=1
-
-# The total number of similar names that should be taken in consideration when
-# showing a hint for a missing member.
-missing-member-max-choices=1
-
-
-[VARIABLES]
-
-# List of additional names supposed to be defined in builtins. Remember that
-# you should avoid defining new builtins when possible.
-additional-builtins=
-
-# Tells whether unused global variables should be treated as a violation.
-allow-global-unused-variables=yes
-
-# List of strings which can identify a callback function by name. A callback
-# name must start or end with one of those strings.
-callbacks=cb_,
-          _cb
-
-# A regular expression matching the name of dummy variables (i.e. expected to
-# not be used).
-dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
-
-# Argument names that match this expression will be ignored. Default to name
-# with leading underscore.
-ignored-argument-names=_.*|^ignored_|^unused_
-
-# Tells whether we should check for unused import in __init__ files.
-init-import=no
-
-# List of qualified module names which can have objects that can redefine
-# builtins.
-redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
-
-
-[FORMAT]
-
-# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
-expected-line-ending-format=
-
-# Regexp for a line that is allowed to be longer than the limit.
-ignore-long-lines=^\s*(# )?<?https?://\S+>?$
-
-# Number of spaces of indent required inside a hanging or continued line.
-indent-after-paren=4
-
-# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
-# tab).
-indent-string='    '
-
-# Maximum number of characters on a single line.
-max-line-length=120
-
-# Maximum number of lines in a module.
-max-module-lines=1000
-
-# List of optional constructs for which whitespace checking is disabled. `dict-
-# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
-# `trailing-comma` allows a space between comma and closing bracket: (a, ).
-# `empty-line` allows space-only lines.
-no-space-check=trailing-comma,
-               dict-separator
-
-# Allow the body of a class to be on the same line as the declaration if body
-# contains single statement.
-single-line-class-stmt=no
-
-# Allow the body of an if to be on the same line as the test if there is no
-# else.
-single-line-if-stmt=no
-
-
-[SIMILARITIES]
-
-# Ignore comments when computing similarities.
-ignore-comments=yes
-
-# Ignore docstrings when computing similarities.
-ignore-docstrings=yes
-
-# Ignore imports when computing similarities.
-ignore-imports=no
-
-# Minimum lines number of a similarity.
-min-similarity-lines=4
-
-
-[BASIC]
-
-# Naming style matching correct argument names.
-argument-naming-style=snake_case
-
-# Regular expression matching correct argument names. Overrides argument-
-# naming-style.
-argument-rgx=[a-z_][a-z0-9_]{0,30}$
-
-# Naming style matching correct attribute names.
-attr-naming-style=snake_case
-
-# Regular expression matching correct attribute names. Overrides attr-naming-
-# style.
-#attr-rgx=
-
-# Bad variable names which should always be refused, separated by a comma.
-bad-names=
-
-# Naming style matching correct class attribute names.
-class-attribute-naming-style=any
-
-# Regular expression matching correct class attribute names. Overrides class-
-# attribute-naming-style.
-#class-attribute-rgx=
-
-# Naming style matching correct class names.
-class-naming-style=PascalCase
-
-# Regular expression matching correct class names. Overrides class-naming-
-# style.
-#class-rgx=
-
-# Naming style matching correct constant names.
-const-naming-style=UPPER_CASE
-
-# Regular expression matching correct constant names. Overrides const-naming-
-# style.
-#const-rgx=
-
-# Minimum line length for functions/classes that require docstrings, shorter
-# ones are exempt.
-docstring-min-length=-1
-
-# Naming style matching correct function names.
-function-naming-style=snake_case
-
-# Regular expression matching correct function names. Overrides function-
-# naming-style.
-#function-rgx=
-
-# Good variable names which should always be accepted, separated by a comma.
-good-names=i,
-           j,
-           k,
-           x,
-           ex,
-           Run,
-           _
-
-# Include a hint for the correct naming format with invalid-name.
-include-naming-hint=no
-
-# Naming style matching correct inline iteration names.
-inlinevar-naming-style=any
-
-# Regular expression matching correct inline iteration names. Overrides
-# inlinevar-naming-style.
-#inlinevar-rgx=
-
-# Naming style matching correct method names.
-method-naming-style=snake_case
-
-# Regular expression matching correct method names. Overrides method-naming-
-# style.
-#method-rgx=
-
-# Naming style matching correct module names.
-module-naming-style=snake_case
-
-# Regular expression matching correct module names. Overrides module-naming-
-# style.
-#module-rgx=
-
-# Colon-delimited sets of names that determine each other's naming style when
-# the name regexes allow several styles.
-name-group=
-
-# Regular expression which should only match function or class names that do
-# not require a docstring.
-no-docstring-rgx=^_
-
-# List of decorators that produce properties, such as abc.abstractproperty. Add
-# to this list to register other decorators that produce valid properties.
-# These decorators are taken in consideration only for invalid-name.
-property-classes=abc.abstractproperty
-
-# Naming style matching correct variable names.
-variable-naming-style=snake_case
-
-# Regular expression matching correct variable names. Overrides variable-
-# naming-style.
-variable-rgx=[a-z_][a-z0-9_]{0,30}$
-
-
-[STRING]
-
-# This flag controls whether the implicit-str-concat-in-sequence should
-# generate a warning on implicit string concatenation in sequences defined over
-# several lines.
-check-str-concat-over-line-jumps=no
-
-
-[IMPORTS]
-
-# Allow wildcard imports from modules that define __all__.
-allow-wildcard-with-all=no
-
-# Analyse import fallback blocks. This can be used to support both Python 2 and
-# 3 compatible code, which means that the block might have code that exists
-# only in one or another interpreter, leading to false positives when analysed.
-analyse-fallback-blocks=no
-
-# Deprecated modules which should not be used, separated by a comma.
-deprecated-modules=optparse,tkinter.tix
-
-# Create a graph of external dependencies in the given file (report RP0402 must
-# not be disabled).
-ext-import-graph=
-
-# Create a graph of every (i.e. internal and external) dependencies in the
-# given file (report RP0402 must not be disabled).
-import-graph=
-
-# Create a graph of internal dependencies in the given file (report RP0402 must
-# not be disabled).
-int-import-graph=
-
-# Force import order to recognize a module as part of the standard
-# compatibility libraries.
-known-standard-library=
-
-# Force import order to recognize a module as part of a third party library.
-known-third-party=enchant
-
-
-[CLASSES]
-
-# List of method names used to declare (i.e. assign) instance attributes.
-defining-attr-methods=__init__,
-                      __new__,
-                      setUp
-
-# List of member names, which should be excluded from the protected access
-# warning.
-exclude-protected=_asdict,
-                  _fields,
-                  _replace,
-                  _source,
-                  _make
-
-# List of valid names for the first argument in a class method.
-valid-classmethod-first-arg=cls
-
-# List of valid names for the first argument in a metaclass class method.
-valid-metaclass-classmethod-first-arg=cls
-
-
-[DESIGN]
-
-# Maximum number of arguments for function / method.
-max-args=5
-
-# Maximum number of attributes for a class (see R0902).
-max-attributes=7
-
-# Maximum number of boolean expressions in an if statement.
-max-bool-expr=5
-
-# Maximum number of branch for function / method body.
-max-branches=12
-
-# Maximum number of locals for function / method body.
-max-locals=15
-
-# Maximum number of parents for a class (see R0901).
-max-parents=15
-
-# Maximum number of public methods for a class (see R0904).
-max-public-methods=20
-
-# Maximum number of return / yield for function / method body.
-max-returns=6
-
-# Maximum number of statements in function / method body.
-max-statements=50
-
-# Minimum number of public methods for a class (see R0903).
-min-public-methods=2
-
-
-[EXCEPTIONS]
-
-# Exceptions that will emit a warning when being caught. Defaults to
-# "BaseException, Exception".
-overgeneral-exceptions=BaseException,
-                       Exception

.readthedocs.yml

@@ -14,8 +14,9 @@ build:
 # Optionally set the version of Python and requirements required to build your docs
 python:
   install:
-    - requirements: docs/requirements.txt
+    - path: .
-    - requirements: requirements.txt
+      extra_requirements:
+        - docs
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

CITATION.cff

@@ -10,11 +10,11 @@ authors:
 version: 1.4
 doi: 10.5281/zenodo.6334862
 license: "MPL-2.0"
-url: "https://www.coqui.ai"
+url: "https://github.com/idiap/coqui-ai-TTS"
-repository-code: "https://github.com/coqui-ai/TTS"
+repository-code: "https://github.com/idiap/coqui-ai-TTS"
 keywords:
   - machine learning
   - deep learning
   - artificial intelligence
   - text to speech
-  - TTS
+  - TTS

CODE_OF_CONDUCT.md

@@ -119,11 +119,11 @@ This Code of Conduct is adapted from the [Contributor Covenant][homepage],
 version 2.0, available at
 [https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0].
 
-Community Impact Guidelines were inspired by 
+Community Impact Guidelines were inspired by
 [Mozilla's code of conduct enforcement ladder][Mozilla CoC].
 
 For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available 
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available
 at [https://www.contributor-covenant.org/translations][translations].
 
 [homepage]: https://www.contributor-covenant.org

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 Welcome to the 🐸TTS!
 
-This repository is governed by [the Contributor Covenant Code of Conduct](https://github.com/coqui-ai/TTS/blob/main/CODE_OF_CONDUCT.md).
+This repository is governed by [the Contributor Covenant Code of Conduct](https://github.com/idiap/coqui-ai-TTS/blob/main/CODE_OF_CONDUCT.md).
 
 ## Where to start.
 We welcome everyone who likes to contribute to 🐸TTS.
@@ -15,13 +15,13 @@ If you like to contribute code, squash a bug but if you don't know where to star
 
     You can pick something out of our road map. We keep the progess of the project in this simple issue thread. It has new model proposals or developmental updates etc.
 
-- [Github Issues Tracker](https://github.com/coqui-ai/TTS/issues)
+- [Github Issues Tracker](https://github.com/idiap/coqui-ai-TTS/issues)
 
     This is a place to find feature requests, bugs.
 
     Issues with the ```good first issue``` tag are good place for beginners to take on.
 
-- ✨**PR**✨ [pages](https://github.com/coqui-ai/TTS/pulls) with the ```🚀new version``` tag.
+- ✨**PR**✨ [pages](https://github.com/idiap/coqui-ai-TTS/pulls) with the ```🚀new version``` tag.
 
     We list all the target improvements for the next version. You can pick one of them and start contributing.
 
@@ -46,21 +46,21 @@ Let us know if you encounter a problem along the way.
 
 The following steps are tested on an Ubuntu system.
 
-1. Fork 🐸TTS[https://github.com/coqui-ai/TTS] by clicking the fork button at the top right corner of the project page.
+1. Fork 🐸TTS[https://github.com/idiap/coqui-ai-TTS] by clicking the fork button at the top right corner of the project page.
 
 2. Clone 🐸TTS and add the main repo as a new remote named ```upstream```.
 
     ```bash
-    $ git clone git@github.com:<your Github name>/TTS.git
+    $ git clone git@github.com:<your Github name>/coqui-ai-TTS.git
-    $ cd TTS
+    $ cd coqui-ai-TTS
-    $ git remote add upstream https://github.com/coqui-ai/TTS.git
+    $ git remote add upstream https://github.com/idiap/coqui-ai-TTS.git
     ```
 
 3. Install 🐸TTS for development.
 
     ```bash
     $ make system-deps  # intended to be used on Ubuntu (Debian). Let us know if you have a different OS.
-    $ make install
+    $ make install_dev
     ```
 
 4. Create a new branch with an informative name for your goal.
@@ -82,13 +82,13 @@ The following steps are tested on an Ubuntu system.
     $ 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.
+9. Format your code. We use ```black``` for code formatting.
 
     ```bash
     $ make style
     ```
 
-10. Run the linter and correct the issues raised. We use ```pylint``` for linting.  It helps to enforce a coding standard, offers simple refactoring suggestions.
+10. Run the linter and correct the issues raised. We use ```ruff``` for linting.  It helps to enforce a coding standard, offers simple refactoring suggestions.
 
     ```bash
     $ make lint
@@ -105,7 +105,7 @@ The following steps are tested on an Ubuntu system.
 
     ```bash
     $ git fetch upstream
-    $ git rebase upstream/master
+    $ git rebase upstream/main
     # or for the development version
     $ git rebase upstream/dev
     ```
@@ -124,7 +124,7 @@ The following steps are tested on an Ubuntu system.
 
 13. Let's discuss until it is perfect. 💪
 
-    We might ask you for certain changes that would appear in the ✨**PR**✨'s page under 🐸TTS[https://github.com/coqui-ai/TTS/pulls].
+    We might ask you for certain changes that would appear in the ✨**PR**✨'s page under 🐸TTS[https://github.com/idiap/coqui-ai-TTS/pulls].
 
 14. Once things look perfect, We merge it to the ```dev``` branch and make it ready for the next version.
 
@@ -132,14 +132,14 @@ The following steps are tested on an Ubuntu system.
 
 If you prefer working within a Docker container as your development environment, you can do the following:
 
-1. Fork 🐸TTS[https://github.com/coqui-ai/TTS] by clicking the fork button at the top right corner of the project page.
+1. Fork 🐸TTS[https://github.com/idiap/coqui-ai-TTS] by clicking the fork button at the top right corner of the project page.
 
 2. Clone 🐸TTS and add the main repo as a new remote named ```upsteam```.
 
     ```bash
-    $ git clone git@github.com:<your Github name>/TTS.git
+    $ git clone git@github.com:<your Github name>/coqui-ai-TTS.git
-    $ cd TTS
+    $ cd coqui-ai-TTS
-    $ git remote add upstream https://github.com/coqui-ai/TTS.git
+    $ git remote add upstream https://github.com/idiap/coqui-ai-TTS.git
     ```
 
 3. Build the Docker Image as your development environment (it installs all of the dependencies for you):

Dockerfile

@@ -3,6 +3,7 @@ FROM ${BASE}
 
 RUN apt-get update && apt-get upgrade -y
 RUN apt-get install -y --no-install-recommends gcc g++ make python3 python3-dev python3-pip python3-venv python3-wheel espeak-ng libsndfile1-dev && rm -rf /var/lib/apt/lists/*
+RUN pip3 install -U pip setuptools
 RUN pip3 install llvmlite --ignore-installed
 
 # Install Dependencies:

LICENSE.txt

@@ -35,7 +35,7 @@ Mozilla Public License Version 2.0
     means any form of the work other than Source Code Form.
 
 1.7. "Larger Work"
-    means a work that combines Covered Software with other material, in 
+    means a work that combines Covered Software with other material, in
     a separate file or files, that is not Covered Software.
 
 1.8. "License"

MANIFEST.in

@@ -1,9 +1,6 @@
 include README.md
 include LICENSE.txt
-include requirements.*.txt
 include *.cff
-include requirements.txt
-include TTS/VERSION
 recursive-include TTS *.json
 recursive-include TTS *.html
 recursive-include TTS *.png
@@ -11,5 +8,3 @@ recursive-include TTS *.md
 recursive-include TTS *.py
 recursive-include TTS *.pyx
 recursive-include images *.png
-recursive-exclude tests *
-prune tests*

Makefile

@@ -1,5 +1,5 @@
 .DEFAULT_GOAL := help
-.PHONY: test system-deps dev-deps deps style lint install help docs
+.PHONY: test system-deps dev-deps style lint install install_dev help docs
 
 help:
 	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
@@ -11,47 +11,50 @@ test_all:	## run tests and don't stop on an error.
 	./run_bash_tests.sh
 
 test:	## run tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests
+	coverage run -m nose2 -F -v -B tests
 
 test_vocoder:	## run vocoder tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.vocoder_tests
+	coverage run -m nose2 -F -v -B tests.vocoder_tests
 
 test_tts:	## run tts tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.tts_tests
+	coverage run -m nose2 -F -v -B tests.tts_tests
 
 test_tts2:	## run tts tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.tts_tests2
+	coverage run -m nose2 -F -v -B tests.tts_tests2
 
 test_xtts:
-	nose2 -F -v -B --with-coverage --coverage TTS tests.xtts_tests
+	coverage run -m nose2 -F -v -B tests.xtts_tests
 
 test_aux:	## run aux tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.aux_tests
+	coverage run -m nose2 -F -v -B tests.aux_tests
 	./run_bash_tests.sh
 
-test_zoo:	## run zoo tests.
+test_zoo0:	## run zoo tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.zoo_tests
+	coverage run -m nose2 -F -v -B tests.zoo_tests.test_models.test_models_offset_0_step_3 \
+	tests.zoo_tests.test_models.test_voice_conversion
+test_zoo1:	## run zoo tests.
+	coverage run -m nose2 -F -v -B tests.zoo_tests.test_models.test_models_offset_1_step_3
+test_zoo2:	## run zoo tests.
+	coverage run -m nose2 -F -v -B tests.zoo_tests.test_models.test_models_offset_2_step_3
 
 inference_tests: ## run inference tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.inference_tests
+	coverage run -m nose2 -F -v -B tests.inference_tests
 
 data_tests: ## run data tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.data_tests
+	coverage run -m nose2 -F -v -B tests.data_tests
 
 test_text: ## run text tests.
-	nose2 -F -v -B --with-coverage --coverage TTS tests.text_tests
+	coverage run -m nose2 -F -v -B tests.text_tests
 
 test_failed:  ## only run tests failed the last time.
-	nose2 -F -v -B --with-coverage --coverage TTS tests
+	coverage run -m nose2 -F -v -B tests
 
 style:	## update code style.
 	black ${target_dirs}
-	isort ${target_dirs}
 
-lint:	## run pylint linter.
+lint:	## run linters.
-	pylint ${target_dirs}
+	ruff check ${target_dirs}
 	black ${target_dirs} --check
-	isort ${target_dirs} --check-only
 
 system-deps:	## install linux system deps
 	sudo apt-get install -y libsndfile1-dev
@@ -59,20 +62,15 @@ system-deps:	## install linux system deps
 dev-deps:  ## install development deps
 	pip install -r requirements.dev.txt
 
-doc-deps:  ## install docs dependencies
-	pip install -r docs/requirements.txt
-
 build-docs: ## build the docs
 	cd docs && make clean && make build
 
-hub-deps:  ## install deps for torch hub use
+install:	## install 🐸 TTS
-	pip install -r requirements.hub.txt
-
-deps:	## install 🐸 requirements.
-	pip install -r requirements.txt
-
-install:	## install 🐸 TTS for development.
 	pip install -e .[all]
 
+install_dev:	## install 🐸 TTS for development.
+	pip install -e .[all,dev]
+	pre-commit install
+
 docs:	## build the docs
 	$(MAKE) -C docs clean && $(MAKE) -C docs html

README.md

@@ -1,17 +1,18 @@
 
-## 🐸Coqui.ai News
+## 🐸Coqui TTS News
+- 📣 Fork of the [original, unmaintained repository](https://github.com/coqui-ai/TTS). New PyPI package: [coqui-tts](https://pypi.org/project/coqui-tts)
 - 📣 ⓍTTSv2 is here with 16 languages and better performance across the board.
-- 📣 ⓍTTS fine-tuning code is out. Check the [example recipes](https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech).
+- 📣 ⓍTTS fine-tuning code is out. Check the [example recipes](https://github.com/idiap/coqui-ai-TTS/tree/dev/recipes/ljspeech).
 - 📣 ⓍTTS can now stream with <200ms latency.
-- 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released [Blog Post](https://coqui.ai/blog/tts/open_xtts), [Demo](https://huggingface.co/spaces/coqui/xtts), [Docs](https://tts.readthedocs.io/en/dev/models/xtts.html)
+- 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released [Blog Post](https://coqui.ai/blog/tts/open_xtts), [Demo](https://huggingface.co/spaces/coqui/xtts), [Docs](https://coqui-tts.readthedocs.io/en/latest/models/xtts.html)
-- 📣 [🐶Bark](https://github.com/suno-ai/bark) is now available for inference with unconstrained voice cloning. [Docs](https://tts.readthedocs.io/en/dev/models/bark.html)
+- 📣 [🐶Bark](https://github.com/suno-ai/bark) is now available for inference with unconstrained voice cloning. [Docs](https://coqui-tts.readthedocs.io/en/latest/models/bark.html)
 - 📣 You can use [~1100 Fairseq models](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS.
-- 📣 🐸TTS now supports 🐢Tortoise with faster inference. [Docs](https://tts.readthedocs.io/en/dev/models/tortoise.html)
+- 📣 🐸TTS now supports 🐢Tortoise with faster inference. [Docs](https://coqui-tts.readthedocs.io/en/latest/models/tortoise.html)
 
 <div align="center">
 <img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" />
 
-## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
+## <img src="https://raw.githubusercontent.com/idiap/coqui-ai-TTS/main/images/coqui-log-green-TTS.png" height="56"/>
 
 
 **🐸TTS is a library for advanced Text-to-Speech generation.**
@@ -25,23 +26,15 @@ ______________________________________________________________________
 
 [![Discord](https://img.shields.io/discord/1037326658807533628?color=%239B59B6&label=chat%20on%20discord)](https://discord.gg/5eXr5seRrv)
 [![License](<https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg>)](https://opensource.org/licenses/MPL-2.0)
-[![PyPI version](https://badge.fury.io/py/TTS.svg)](https://badge.fury.io/py/TTS)
+[![PyPI version](https://badge.fury.io/py/coqui-tts.svg)](https://badge.fury.io/py/coqui-tts)
-[![Covenant](https://camo.githubusercontent.com/7d620efaa3eac1c5b060ece5d6aacfcc8b81a74a04d05cd0398689c01c4463bb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6e7472696275746f72253230436f76656e616e742d76322e3025323061646f707465642d6666363962342e737667)](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md)
+[![Covenant](https://camo.githubusercontent.com/7d620efaa3eac1c5b060ece5d6aacfcc8b81a74a04d05cd0398689c01c4463bb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6e7472696275746f72253230436f76656e616e742d76322e3025323061646f707465642d6666363962342e737667)](https://github.com/idiap/coqui-ai-TTS/blob/main/CODE_OF_CONDUCT.md)
-[![Downloads](https://pepy.tech/badge/tts)](https://pepy.tech/project/tts)
+[![Downloads](https://pepy.tech/badge/coqui-tts)](https://pepy.tech/project/coqui-tts)
 [![DOI](https://zenodo.org/badge/265612440.svg)](https://zenodo.org/badge/latestdoi/265612440)
 
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/aux_tests.yml/badge.svg)
+![GithubActions](https://github.com/idiap/coqui-ai-TTS/actions/workflows/tests.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/data_tests.yml/badge.svg)
+![GithubActions](https://github.com/idiap/coqui-ai-TTS/actions/workflows/docker.yaml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/docker.yaml/badge.svg)
+![GithubActions](https://github.com/idiap/coqui-ai-TTS/actions/workflows/style_check.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/inference_tests.yml/badge.svg)
+[![Docs](<https://readthedocs.org/projects/coqui-tts/badge/?version=latest&style=plastic>)](https://coqui-tts.readthedocs.io/en/latest/)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/style_check.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/text_tests.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/tts_tests.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/vocoder_tests.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/zoo_tests0.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/zoo_tests1.yml/badge.svg)
-![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/zoo_tests2.yml/badge.svg)
-[![Docs](<https://readthedocs.org/projects/tts/badge/?version=latest&style=plastic>)](https://tts.readthedocs.io/en/latest/)
 
 </div>
 
@@ -57,28 +50,26 @@ Please use our dedicated channels for questions and discussion. Help is much mor
 | 👩‍💻 **Usage Questions**          | [GitHub Discussions]                    |
 | 🗯 **General Discussion**       | [GitHub Discussions] or [Discord]   |
 
-[github issue tracker]: https://github.com/coqui-ai/tts/issues
+[github issue tracker]: https://github.com/idiap/coqui-ai-TTS/issues
-[github discussions]: https://github.com/coqui-ai/TTS/discussions
+[github discussions]: https://github.com/idiap/coqui-ai-TTS/discussions
 [discord]: https://discord.gg/5eXr5seRrv
 [Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials
 
+The [issues](https://github.com/coqui-ai/TTS/issues) and
+[discussions](https://github.com/coqui-ai/TTS/discussions) in the original
+repository are also still a useful source of information.
+
 
 ## 🔗 Links and Resources
 | Type                            | Links                               |
 | ------------------------------- | --------------------------------------- |
-| 💼 **Documentation**              | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
+| 💼 **Documentation**              | [ReadTheDocs](https://coqui-tts.readthedocs.io/en/latest/)
-| 💾 **Installation**               | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#installation)|
+| 💾 **Installation**               | [TTS/README.md](https://github.com/idiap/coqui-ai-TTS/tree/dev#installation)|
-| 👩‍💻 **Contributing**               | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)|
+| 👩‍💻 **Contributing**               | [CONTRIBUTING.md](https://github.com/idiap/coqui-ai-TTS/blob/main/CONTRIBUTING.md)|
 | 📌 **Road Map**                   | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378)
-| 🚀 **Released Models**            | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)|
+| 🚀 **Released Models**            | [Standard models](https://github.com/idiap/coqui-ai-TTS/blob/dev/TTS/.models.json) and [Fairseq models in ~1100 languages](https://github.com/idiap/coqui-ai-TTS#example-text-to-speech-using-fairseq-models-in-1100-languages-)|
 | 📰 **Papers**                    | [TTS Papers](https://github.com/erogol/TTS-papers)|
 
-
-## 🥇 TTS Performance
-<p align="center"><img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/TTS-performance.png" width="800" /></p>
-
-Underlined "TTS*" and "Judy*" are **internal** 🐸TTS models that are not released open-source. They are here to show the potential. Models prefixed with a dot (.Jofish .Abe and .Janice) are real human voices.
-
 ## Features
 - High-performance Deep Learning models for Text2Speech tasks.
     - Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
@@ -144,21 +135,48 @@ Underlined "TTS*" and "Judy*" are **internal** 🐸TTS models that are not relea
 You can also help us implement more models.
 
 ## Installation
-🐸TTS is tested on Ubuntu 18.04 with **python >= 3.9, < 3.12.**.
+🐸TTS is tested on Ubuntu 22.04 with **python >= 3.9, < 3.13.**.
 
-If you are only interested in [synthesizing speech](https://tts.readthedocs.io/en/latest/inference.html) with the released 🐸TTS models, installing from PyPI is the easiest option.
+If you are only interested in [synthesizing speech](https://coqui-tts.readthedocs.io/en/latest/inference.html) with the released 🐸TTS models, installing from PyPI is the easiest option.
 
 ```bash
-pip install TTS
+pip install coqui-tts
 ```
 
 If you plan to code or train models, clone 🐸TTS and install it locally.
 
 ```bash
-git clone https://github.com/coqui-ai/TTS
+git clone https://github.com/idiap/coqui-ai-TTS
-pip install -e .[all,dev,notebooks]  # Select the relevant extras
+cd coqui-ai-TTS
+pip install -e .
 ```
 
+### Optional dependencies
+
+The following extras allow the installation of optional dependencies:
+
+| Name | Description |
+|------|-------------|
+| `all` | All optional dependencies, except `dev` and `docs` |
+| `dev` | Development dependencies |
+| `docs` | Dependencies for building the documentation |
+| `notebooks` | Dependencies only used in notebooks |
+| `server` | Dependencies to run the TTS server |
+| `bn` | Bangla G2P |
+| `ja` | Japanese G2P |
+| `ko` | Korean G2P |
+| `zh` | Chinese G2P |
+| `languages` | All language-specific dependencies |
+
+You can install extras with one of the following commands:
+
+```bash
+pip install coqui-tts[server,ja]
+pip install -e .[server,ja]
+```
+
+### Platforms
+
 If you are on Ubuntu (Debian), you can also run following commands for installation.
 
 ```bash
@@ -166,7 +184,9 @@ $ make system-deps  # intended to be used on Ubuntu (Debian). Let us know if you
 $ make install
 ```
 
-If you are on Windows, 👑@GuyPaddock wrote installation instructions [here](https://stackoverflow.com/questions/66726331/how-can-i-run-mozilla-tts-coqui-tts-training-with-cuda-on-a-windows-system).
+If you are on Windows, 👑@GuyPaddock wrote installation instructions
+[here](https://stackoverflow.com/questions/66726331/how-can-i-run-mozilla-tts-coqui-tts-training-with-cuda-on-a-windows-system)
+(note that these are out of date, e.g. you need to have at least Python 3.9).
 
 
 ## Docker Image
@@ -180,7 +200,8 @@ python3 TTS/server/server.py --model_name tts_models/en/vctk/vits # To start a s
 ```
 
 You can then enjoy the TTS server [here](http://[::1]:5002/)
-More details about the docker images (like GPU support) can be found [here](https://tts.readthedocs.io/en/latest/docker_images.html)
+More details about the docker images (like GPU support) can be found
+[here](https://coqui-tts.readthedocs.io/en/latest/docker_images.html)
 
 
 ## Synthesizing speech by 🐸TTS
@@ -254,11 +275,10 @@ You can find the language ISO codes [here](https://dl.fbaipublicfiles.com/mms/tt
 and learn about the Fairseq models [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms).
 
 ```python
-# TTS with on the fly voice conversion
+# TTS with fairseq models
 api = TTS("tts_models/deu/fairseq/vits")
-api.tts_with_vc_to_file(
+api.tts_to_file(
     "Wie sage ich auf Italienisch, dass ich dich liebe?",
-    speaker_wav="target/speaker.wav",
     file_path="output.wav"
 )
 ```

TTS/.models.json

@@ -46,7 +46,7 @@
                     "hf_url": [
                         "https://coqui.gateway.scarf.sh/hf/bark/coarse_2.pt",
                         "https://coqui.gateway.scarf.sh/hf/bark/fine_2.pt",
-                        "https://coqui.gateway.scarf.sh/hf/text_2.pt",
+                        "https://coqui.gateway.scarf.sh/hf/bark/text_2.pt",
                         "https://coqui.gateway.scarf.sh/hf/bark/config.json",
                         "https://coqui.gateway.scarf.sh/hf/bark/hubert.pt",
                         "https://coqui.gateway.scarf.sh/hf/bark/tokenizer.pth"

TTS/VERSION

@@ -1 +0,0 @@
-0.22.0

TTS/__init__.py

@@ -1,6 +1,3 @@
-import os
+import importlib.metadata
 
-with open(os.path.join(os.path.dirname(__file__), "VERSION"), "r", encoding="utf-8") as f:
+__version__ = importlib.metadata.version("coqui-tts")
-    version = f.read().strip()
-
-__version__ = version

TTS/api.py

@@ -1,15 +1,16 @@
+import logging
 import tempfile
 import warnings
 from pathlib import Path
-from typing import Union
 
-import numpy as np
 from torch import nn
 
+from TTS.config import load_config
 from TTS.utils.audio.numpy_transforms import save_wav
 from TTS.utils.manage import ModelManager
 from TTS.utils.synthesizer import Synthesizer
-from TTS.config import load_config
+
+logger = logging.getLogger(__name__)
 
 
 class TTS(nn.Module):
@@ -61,7 +62,7 @@ class TTS(nn.Module):
             gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
         """
         super().__init__()
-        self.manager = ModelManager(models_file=self.get_models_file_path(), progress_bar=progress_bar, verbose=False)
+        self.manager = ModelManager(models_file=self.get_models_file_path(), progress_bar=progress_bar)
         self.config = load_config(config_path) if config_path else None
         self.synthesizer = None
         self.voice_converter = None
@@ -99,7 +100,7 @@ class TTS(nn.Module):
             isinstance(self.model_name, str)
             and "xtts" in self.model_name
             or self.config
-            and ("xtts" in self.config.model or len(self.config.languages) > 1)
+            and ("xtts" in self.config.model or "languages" in self.config and len(self.config.languages) > 1)
         ):
             return True
         if hasattr(self.synthesizer.tts_model, "language_manager") and self.synthesizer.tts_model.language_manager:
@@ -122,8 +123,9 @@ class TTS(nn.Module):
     def get_models_file_path():
         return Path(__file__).parent / ".models.json"
 
-    def list_models(self):
+    @staticmethod
-        return ModelManager(models_file=TTS.get_models_file_path(), progress_bar=False, verbose=False)
+    def list_models():
+        return ModelManager(models_file=TTS.get_models_file_path(), progress_bar=False).list_models()
 
     def download_model_by_name(self, model_name: str):
         model_path, config_path, model_item = self.manager.download_model(model_name)
@@ -168,9 +170,7 @@ class TTS(nn.Module):
         self.synthesizer = None
         self.model_name = model_name
 
-        model_path, config_path, vocoder_path, vocoder_config_path, model_dir = self.download_model_by_name(
+        model_path, config_path, vocoder_path, vocoder_config_path, model_dir = self.download_model_by_name(model_name)
-            model_name
-        )
 
         # init synthesizer
         # None values are fetch from the model
@@ -231,7 +231,7 @@ class TTS(nn.Module):
             raise ValueError("Model is not multi-speaker but `speaker` is provided.")
         if not self.is_multi_lingual and language is not None:
             raise ValueError("Model is not multi-lingual but `language` is provided.")
-        if not emotion is None and not speed is None:
+        if emotion is not None and speed is not None:
             raise ValueError("Emotion and speed can only be used with Coqui Studio models. Which is discontinued.")
 
     def tts(

TTS/bin/collect_env_info.py

@@ -1,4 +1,6 @@
 """Get detailed info about the working environment."""
+
+import json
 import os
 import platform
 import sys
@@ -6,11 +8,10 @@ import sys
 import numpy
 import torch
 
-sys.path += [os.path.abspath(".."), os.path.abspath(".")]
-import json
-
 import TTS
 
+sys.path += [os.path.abspath(".."), os.path.abspath(".")]
+
 
 def system_info():
     return {

TTS/bin/compute_attention_masks.py

@@ -1,5 +1,6 @@
 import argparse
 import importlib
+import logging
 import os
 from argparse import RawTextHelpFormatter
 
@@ -7,15 +8,18 @@ import numpy as np
 import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
+from trainer.io import load_checkpoint
 
 from TTS.config import load_config
 from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.models import setup_model
 from TTS.tts.utils.text.characters import make_symbols, phonemes, symbols
 from TTS.utils.audio import AudioProcessor
-from TTS.utils.io import load_checkpoint
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     # pylint: disable=bad-option-value
     parser = argparse.ArgumentParser(
         description="""Extract attention masks from trained Tacotron/Tacotron2 models.
@@ -31,7 +35,7 @@ Example run:
         --data_path /root/LJSpeech-1.1/
         --batch_size 32
         --dataset ljspeech
-        --use_cuda True
+        --use_cuda
 """,
         formatter_class=RawTextHelpFormatter,
     )
@@ -58,7 +62,7 @@ Example run:
         help="Dataset metafile inclusing file paths with transcripts.",
     )
     parser.add_argument("--data_path", type=str, default="", help="Defines the data path. It overwrites config.json.")
-    parser.add_argument("--use_cuda", type=bool, default=False, help="enable/disable cuda.")
+    parser.add_argument("--use_cuda", action=argparse.BooleanOptionalAction, default=False, help="enable/disable cuda.")
 
     parser.add_argument(
         "--batch_size", default=16, type=int, help="Batch size for the model. Use batch_size=1 if you have no CUDA."
@@ -70,7 +74,7 @@ Example run:
 
     # if the vocabulary was passed, replace the default
     if "characters" in C.keys():
-        symbols, phonemes = make_symbols(**C.characters)
+        symbols, phonemes = make_symbols(**C.characters)  # noqa: F811
 
     # load the model
     num_chars = len(phonemes) if C.use_phonemes else len(symbols)

TTS/bin/compute_embeddings.py

@@ -1,4 +1,5 @@
 import argparse
+import logging
 import os
 from argparse import RawTextHelpFormatter
 
@@ -10,6 +11,7 @@ from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.utils.managers import save_file
 from TTS.tts.utils.speakers import SpeakerManager
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 
 def compute_embeddings(
@@ -100,6 +102,8 @@ def compute_embeddings(
 
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     parser = argparse.ArgumentParser(
         description="""Compute embedding vectors for each audio file in a dataset and store them keyed by `{dataset_name}#{file_path}` in a .pth file\n\n"""
         """
@@ -146,7 +150,7 @@ if __name__ == "__main__":
         default=False,
         action="store_true",
     )
-    parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False)
+    parser.add_argument("--disable_cuda", action="store_true", help="Flag to disable cuda.", default=False)
     parser.add_argument("--no_eval", help="Do not compute eval?. Default False", default=False, action="store_true")
     parser.add_argument(
         "--formatter_name",

TTS/bin/compute_statistics.py

@@ -3,6 +3,7 @@
 
 import argparse
 import glob
+import logging
 import os
 
 import numpy as np
@@ -12,10 +13,13 @@ from tqdm import tqdm
 from TTS.config import load_config
 from TTS.tts.datasets import load_tts_samples
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 
 def main():
     """Run preprocessing process."""
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     parser = argparse.ArgumentParser(description="Compute mean and variance of spectrogtram features.")
     parser.add_argument("config_path", type=str, help="TTS config file path to define audio processin parameters.")
     parser.add_argument("out_path", type=str, help="save path (directory and filename).")

TTS/bin/eval_encoder.py

@@ -1,4 +1,5 @@
 import argparse
+import logging
 from argparse import RawTextHelpFormatter
 
 import torch
@@ -7,6 +8,7 @@ from tqdm import tqdm
 from TTS.config import load_config
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.utils.speakers import SpeakerManager
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 
 def compute_encoder_accuracy(dataset_items, encoder_manager):
@@ -51,6 +53,8 @@ def compute_encoder_accuracy(dataset_items, encoder_manager):
 
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     parser = argparse.ArgumentParser(
         description="""Compute the accuracy of the encoder.\n\n"""
         """
@@ -71,8 +75,8 @@ if __name__ == "__main__":
         type=str,
         help="Path to dataset config file.",
     )
-    parser.add_argument("--use_cuda", type=bool, help="flag to set cuda.", default=True)
+    parser.add_argument("--use_cuda", action=argparse.BooleanOptionalAction, help="flag to set cuda.", default=True)
-    parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
+    parser.add_argument("--eval", action=argparse.BooleanOptionalAction, help="compute eval.", default=True)
 
     args = parser.parse_args()
 

TTS/bin/extract_tts_spectrograms.py

@@ -2,12 +2,14 @@
 """Extract Mel spectrograms with teacher forcing."""
 
 import argparse
+import logging
 import os
 
 import numpy as np
 import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
+from trainer.generic_utils import count_parameters
 
 from TTS.config import load_config
 from TTS.tts.datasets import TTSDataset, load_tts_samples
@@ -16,12 +18,12 @@ from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.audio.numpy_transforms import quantize
-from TTS.utils.generic_utils import count_parameters
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 use_cuda = torch.cuda.is_available()
 
 
-def setup_loader(ap, r, verbose=False):
+def setup_loader(ap, r):
     tokenizer, _ = TTSTokenizer.init_from_config(c)
     dataset = TTSDataset(
         outputs_per_step=r,
@@ -37,7 +39,6 @@ def setup_loader(ap, r, verbose=False):
         phoneme_cache_path=c.phoneme_cache_path,
         precompute_num_workers=0,
         use_noise_augment=False,
-        verbose=verbose,
         speaker_id_mapping=speaker_manager.name_to_id if c.use_speaker_embedding else None,
         d_vector_mapping=speaker_manager.embeddings if c.use_d_vector_file else None,
     )
@@ -257,7 +258,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     print("\n > Model has {} parameters".format(num_params), flush=True)
     # set r
     r = 1 if c.model.lower() == "glow_tts" else model.decoder.r
-    own_loader = setup_loader(ap, r, verbose=True)
+    own_loader = setup_loader(ap, r)
 
     extract_spectrograms(
         own_loader,
@@ -272,6 +273,8 @@ def main(args):  # pylint: disable=redefined-outer-name
 
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     parser = argparse.ArgumentParser()
     parser.add_argument("--config_path", type=str, help="Path to config file for training.", required=True)
     parser.add_argument("--checkpoint_path", type=str, help="Model file to be restored.", required=True)
@@ -279,7 +282,7 @@ if __name__ == "__main__":
     parser.add_argument("--debug", default=False, action="store_true", help="Save audio files for debug")
     parser.add_argument("--save_audio", default=False, action="store_true", help="Save audio files")
     parser.add_argument("--quantize_bits", type=int, default=0, help="Save quantized audio files if non-zero")
-    parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
+    parser.add_argument("--eval", action=argparse.BooleanOptionalAction, help="compute eval.", default=True)
     args = parser.parse_args()
 
     c = load_config(args.config_path)

TTS/bin/find_unique_chars.py

@@ -1,12 +1,17 @@
 """Find all the unique characters in a dataset"""
+
 import argparse
+import logging
 from argparse import RawTextHelpFormatter
 
 from TTS.config import load_config
-from TTS.tts.datasets import load_tts_samples
+from TTS.tts.datasets import find_unique_chars, load_tts_samples
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 
 def main():
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     # pylint: disable=bad-option-value
     parser = argparse.ArgumentParser(
         description="""Find all the unique characters or phonemes in a dataset.\n\n"""
@@ -28,17 +33,7 @@ def main():
     )
 
     items = train_items + eval_items
-
+    find_unique_chars(items)
-    texts = "".join(item["text"] for item in items)
-    chars = set(texts)
-    lower_chars = filter(lambda c: c.islower(), chars)
-    chars_force_lower = [c.lower() for c in chars]
-    chars_force_lower = set(chars_force_lower)
-
-    print(f" > Number of unique characters: {len(chars)}")
-    print(f" > Unique characters: {''.join(sorted(chars))}")
-    print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
-    print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
 
 
 if __name__ == "__main__":

TTS/bin/find_unique_phonemes.py

@@ -1,5 +1,7 @@
 """Find all the unique characters in a dataset"""
+
 import argparse
+import logging
 import multiprocessing
 from argparse import RawTextHelpFormatter
 
@@ -8,15 +10,18 @@ from tqdm.contrib.concurrent import process_map
 from TTS.config import load_config
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.utils.text.phonemizers import Gruut
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 
 def compute_phonemes(item):
     text = item["text"]
     ph = phonemizer.phonemize(text).replace("|", "")
-    return set(list(ph))
+    return set(ph)
 
 
 def main():
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     # pylint: disable=W0601
     global c, phonemizer
     # pylint: disable=bad-option-value

TTS/bin/remove_silence_using_vad.py

@@ -1,5 +1,6 @@
 import argparse
 import glob
+import logging
 import multiprocessing
 import os
 import pathlib
@@ -7,6 +8,7 @@ import pathlib
 import torch
 from tqdm import tqdm
 
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 from TTS.utils.vad import get_vad_model_and_utils, remove_silence
 
 torch.set_num_threads(1)
@@ -75,8 +77,10 @@ def preprocess_audios():
 
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     parser = argparse.ArgumentParser(
-        description="python TTS/bin/remove_silence_using_vad.py -i=VCTK-Corpus/ -o=VCTK-Corpus-removed-silence/ -g=wav48_silence_trimmed/*/*_mic1.flac --trim_just_beginning_and_end True"
+        description="python TTS/bin/remove_silence_using_vad.py -i=VCTK-Corpus/ -o=VCTK-Corpus-removed-silence/ -g=wav48_silence_trimmed/*/*_mic1.flac --trim_just_beginning_and_end"
     )
     parser.add_argument("-i", "--input_dir", type=str, help="Dataset root dir", required=True)
     parser.add_argument("-o", "--output_dir", type=str, help="Output Dataset dir", default="")
@@ -91,20 +95,20 @@ if __name__ == "__main__":
     parser.add_argument(
         "-t",
         "--trim_just_beginning_and_end",
-        type=bool,
+        action=argparse.BooleanOptionalAction,
         default=True,
-        help="If True this script will trim just the beginning and end nonspeech parts. If False all nonspeech parts will be trim. Default True",
+        help="If True this script will trim just the beginning and end nonspeech parts. If False all nonspeech parts will be trimmed.",
     )
     parser.add_argument(
         "-c",
         "--use_cuda",
-        type=bool,
+        action=argparse.BooleanOptionalAction,
         default=False,
         help="If True use cuda",
     )
     parser.add_argument(
         "--use_onnx",
-        type=bool,
+        action=argparse.BooleanOptionalAction,
         default=False,
         help="If True use onnx",
     )

TTS/bin/synthesize.py

@@ -1,14 +1,20 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
+
+"""Command line interface."""
 
 import argparse
 import contextlib
+import logging
 import sys
 from argparse import RawTextHelpFormatter
 
 # pylint: disable=redefined-outer-name, unused-argument
 from pathlib import Path
 
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
+
+logger = logging.getLogger(__name__)
+
 description = """
 Synthesize speech on command line.
 
@@ -131,17 +137,8 @@ $ tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<mode
 """
 
 
-def str2bool(v):
+def parse_args() -> argparse.Namespace:
-    if isinstance(v, bool):
+    """Parse arguments."""
-        return v
-    if v.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    if v.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    raise argparse.ArgumentTypeError("Boolean value expected.")
-
-
-def main():
     parser = argparse.ArgumentParser(
         description=description.replace("    ```\n", ""),
         formatter_class=RawTextHelpFormatter,
@@ -149,10 +146,7 @@ def main():
 
     parser.add_argument(
         "--list_models",
-        type=str2bool,
+        action="store_true",
-        nargs="?",
-        const=True,
-        default=False,
         help="list available pre-trained TTS and vocoder models.",
     )
 
@@ -200,7 +194,7 @@ def main():
         default="tts_output.wav",
         help="Output wav file path.",
     )
-    parser.add_argument("--use_cuda", type=bool, help="Run model on CUDA.", default=False)
+    parser.add_argument("--use_cuda", action="store_true", help="Run model on CUDA.")
     parser.add_argument("--device", type=str, help="Device to run model on.", default="cpu")
     parser.add_argument(
         "--vocoder_path",
@@ -219,12 +213,9 @@ def main():
     parser.add_argument(
         "--pipe_out",
         help="stdout the generated TTS wav file for shell pipe.",
-        type=str2bool,
+        action="store_true",
-        nargs="?",
-        const=True,
-        default=False,
     )
-    
+
     # args for multi-speaker synthesis
     parser.add_argument("--speakers_file_path", type=str, help="JSON file for multi-speaker model.", default=None)
     parser.add_argument("--language_ids_file_path", type=str, help="JSON file for multi-lingual model.", default=None)
@@ -254,25 +245,18 @@ def main():
     parser.add_argument(
         "--list_speaker_idxs",
         help="List available speaker ids for the defined multi-speaker model.",
-        type=str2bool,
+        action="store_true",
-        nargs="?",
-        const=True,
-        default=False,
     )
     parser.add_argument(
         "--list_language_idxs",
         help="List available language ids for the defined multi-lingual model.",
-        type=str2bool,
+        action="store_true",
-        nargs="?",
-        const=True,
-        default=False,
     )
     # aux args
     parser.add_argument(
         "--save_spectogram",
-        type=bool,
+        action="store_true",
-        help="If true save raw spectogram for further (vocoder) processing in out_path.",
+        help="Save raw spectogram for further (vocoder) processing in out_path.",
-        default=False,
     )
     parser.add_argument(
         "--reference_wav",
@@ -288,8 +272,8 @@ def main():
     )
     parser.add_argument(
         "--progress_bar",
-        type=str2bool,
+        action=argparse.BooleanOptionalAction,
-        help="If true shows a progress bar for the model download. Defaults to True",
+        help="Show a progress bar for the model download.",
         default=True,
     )
 
@@ -330,19 +314,23 @@ def main():
     ]
     if not any(check_args):
         parser.parse_args(["-h"])
+    return args
+
+
+def main():
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+    args = parse_args()
 
     pipe_out = sys.stdout if args.pipe_out else None
 
     with contextlib.redirect_stdout(None if args.pipe_out else sys.stdout):
         # Late-import to make things load faster
-        from TTS.api import TTS
         from TTS.utils.manage import ModelManager
         from TTS.utils.synthesizer import Synthesizer
 
         # load model manager
         path = Path(__file__).parent / "../.models.json"
         manager = ModelManager(path, progress_bar=args.progress_bar)
-        api = TTS()
 
         tts_path = None
         tts_config_path = None
@@ -379,10 +367,8 @@ def main():
             if model_item["model_type"] == "tts_models":
                 tts_path = model_path
                 tts_config_path = config_path
-                if "default_vocoder" in model_item:
+                if args.vocoder_name is None and "default_vocoder" in model_item:
-                    args.vocoder_name = (
+                    args.vocoder_name = model_item["default_vocoder"]
-                        model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
-                    )
 
             # voice conversion model
             if model_item["model_type"] == "voice_conversion_models":
@@ -437,31 +423,37 @@ def main():
 
         # query speaker ids of a multi-speaker model.
         if args.list_speaker_idxs:
-            print(
+            if synthesizer.tts_model.speaker_manager is None:
-                " > Available speaker ids: (Set --speaker_idx flag to one of these values to use the multi-speaker model."
+                logger.info("Model only has a single speaker.")
+                return
+            logger.info(
+                "Available speaker ids: (Set --speaker_idx flag to one of these values to use the multi-speaker model."
             )
-            print(synthesizer.tts_model.speaker_manager.name_to_id)
+            logger.info(synthesizer.tts_model.speaker_manager.name_to_id)
             return
 
         # query langauge ids of a multi-lingual model.
         if args.list_language_idxs:
-            print(
+            if synthesizer.tts_model.language_manager is None:
-                " > Available language ids: (Set --language_idx flag to one of these values to use the multi-lingual model."
+                logger.info("Monolingual model.")
+                return
+            logger.info(
+                "Available language ids: (Set --language_idx flag to one of these values to use the multi-lingual model."
             )
-            print(synthesizer.tts_model.language_manager.name_to_id)
+            logger.info(synthesizer.tts_model.language_manager.name_to_id)
             return
 
         # check the arguments against a multi-speaker model.
         if synthesizer.tts_speakers_file and (not args.speaker_idx and not args.speaker_wav):
-            print(
+            logger.error(
-                " [!] Looks like you use a multi-speaker model. Define `--speaker_idx` to "
+                "Looks like you use a multi-speaker model. Define `--speaker_idx` to "
                 "select the target speaker. You can list the available speakers for this model by `--list_speaker_idxs`."
             )
             return
 
         # RUN THE SYNTHESIS
         if args.text:
-            print(" > Text: {}".format(args.text))
+            logger.info("Text: %s", args.text)
 
         # kick it
         if tts_path is not None:
@@ -486,8 +478,8 @@ def main():
             )
 
         # save the results
-        print(" > Saving output to {}".format(args.out_path))
         synthesizer.save_wav(wav, args.out_path, pipe_out=pipe_out)
+        logger.info("Saved output to %s", args.out_path)
 
 
 if __name__ == "__main__":

TTS/bin/train_encoder.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+import logging
 import os
 import sys
 import time
@@ -8,6 +9,7 @@ import traceback
 
 import torch
 from torch.utils.data import DataLoader
+from trainer.generic_utils import count_parameters, remove_experiment_folder
 from trainer.io import copy_model_files, save_best_model, save_checkpoint
 from trainer.torch import NoamLR
 from trainer.trainer_utils import get_optimizer
@@ -18,7 +20,7 @@ from TTS.encoder.utils.training import init_training
 from TTS.encoder.utils.visual import plot_embeddings
 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
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 from TTS.utils.samplers import PerfectBatchSampler
 from TTS.utils.training import check_update
 
@@ -31,7 +33,7 @@ print(" > Using CUDA: ", use_cuda)
 print(" > Number of GPUs: ", num_gpus)
 
 
-def setup_loader(ap: AudioProcessor, is_val: bool = False, verbose: bool = False):
+def setup_loader(ap: AudioProcessor, is_val: bool = False):
     num_utter_per_class = c.num_utter_per_class if not is_val else c.eval_num_utter_per_class
     num_classes_in_batch = c.num_classes_in_batch if not is_val else c.eval_num_classes_in_batch
 
@@ -42,7 +44,6 @@ def setup_loader(ap: AudioProcessor, is_val: bool = False, verbose: bool = False
         voice_len=c.voice_len,
         num_utter_per_class=num_utter_per_class,
         num_classes_in_batch=num_classes_in_batch,
-        verbose=verbose,
         augmentation_config=c.audio_augmentation if not is_val else None,
         use_torch_spec=c.model_params.get("use_torch_spec", False),
     )
@@ -160,9 +161,6 @@ def train(model, optimizer, scheduler, criterion, data_loader, eval_data_loader,
             loader_time = time.time() - end_time
             global_step += 1
 
-            # setup lr
-            if c.lr_decay:
-                scheduler.step()
             optimizer.zero_grad()
 
             # dispatch data to GPU
@@ -181,6 +179,10 @@ def train(model, optimizer, scheduler, criterion, data_loader, eval_data_loader,
             grad_norm, _ = check_update(model, c.grad_clip)
             optimizer.step()
 
+            # setup lr
+            if c.lr_decay:
+                scheduler.step()
+
             step_time = time.time() - start_time
             epoch_time += step_time
 
@@ -278,9 +280,9 @@ def main(args):  # pylint: disable=redefined-outer-name
     # pylint: disable=redefined-outer-name
     meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=True)
 
-    train_data_loader, train_classes, map_classid_to_classname = setup_loader(ap, is_val=False, verbose=True)
+    train_data_loader, train_classes, map_classid_to_classname = setup_loader(ap, is_val=False)
     if c.run_eval:
-        eval_data_loader, _, _ = setup_loader(ap, is_val=True, verbose=True)
+        eval_data_loader, _, _ = setup_loader(ap, is_val=True)
     else:
         eval_data_loader = None
 
@@ -316,6 +318,8 @@ def main(args):  # pylint: disable=redefined-outer-name
 
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     args, c, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = init_training()
 
     try:

TTS/bin/train_tts.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from dataclasses import dataclass, field
 
@@ -6,6 +7,7 @@ from trainer import Trainer, TrainerArgs
 from TTS.config import load_config, register_config
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.models import setup_model
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 
 
 @dataclass
@@ -15,6 +17,8 @@ class TrainTTSArgs(TrainerArgs):
 
 def main():
     """Run `tts` model training directly by a `config.json` file."""
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     # init trainer args
     train_args = TrainTTSArgs()
     parser = train_args.init_argparse(arg_prefix="")

TTS/bin/train_vocoder.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from dataclasses import dataclass, field
 
@@ -5,6 +6,7 @@ from trainer import Trainer, TrainerArgs
 
 from TTS.config import load_config, register_config
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
 from TTS.vocoder.models import setup_model
 
@@ -16,6 +18,8 @@ class TrainVocoderArgs(TrainerArgs):
 
 def main():
     """Run `tts` model training directly by a `config.json` file."""
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     # init trainer args
     train_args = TrainVocoderArgs()
     parser = train_args.init_argparse(arg_prefix="")

TTS/bin/tune_wavegrad.py

@@ -1,5 +1,7 @@
 """Search a good noise schedule for WaveGrad for a given number of inference iterations"""
+
 import argparse
+import logging
 from itertools import product as cartesian_product
 
 import numpy as np
@@ -9,11 +11,14 @@ from tqdm import tqdm
 
 from TTS.config import load_config
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 from TTS.vocoder.datasets.preprocess import load_wav_data
 from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
 from TTS.vocoder.models import setup_model
 
 if __name__ == "__main__":
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
+
     parser = argparse.ArgumentParser()
     parser.add_argument("--model_path", type=str, help="Path to model checkpoint.")
     parser.add_argument("--config_path", type=str, help="Path to model config file.")
@@ -54,7 +59,6 @@ if __name__ == "__main__":
         return_segments=False,
         use_noise_augment=False,
         use_cache=False,
-        verbose=True,
     )
     loader = DataLoader(
         dataset,

TTS/config/__init__.py

@@ -17,9 +17,12 @@ def read_json_with_comments(json_path):
     with fsspec.open(json_path, "r", encoding="utf-8") as f:
         input_str = f.read()
     # handle comments but not urls with //
-    input_str = re.sub(r"(\"(?:[^\"\\]|\\.)*\")|(/\*(?:.|[\\n\\r])*?\*/)|(//.*)", lambda m: m.group(1) or m.group(2) or "", input_str)
+    input_str = re.sub(
+        r"(\"(?:[^\"\\]|\\.)*\")|(/\*(?:.|[\\n\\r])*?\*/)|(//.*)", lambda m: m.group(1) or m.group(2) or "", input_str
+    )
     return json.loads(input_str)
 
+
 def register_config(model_name: str) -> Coqpit:
     """Find the right config for the given model name.
 

TTS/demos/xtts_ft_demo/requirements.txt

@@ -1,2 +1,2 @@
 faster_whisper==0.9.0
-gradio==4.7.1
+gradio==4.7.1

TTS/demos/xtts_ft_demo/utils/formatter.py

@@ -1,23 +1,17 @@
-import os
 import gc
-import torchaudio
+import os
+
 import pandas
-from faster_whisper import WhisperModel
-from glob import glob
-
-from tqdm import tqdm
-
 import torch
 import torchaudio
-# torch.set_num_threads(1)
+from faster_whisper import WhisperModel
+from tqdm import tqdm
 
+# torch.set_num_threads(1)
 from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
 
 torch.set_num_threads(16)
 
-
-import os
-
 audio_types = (".wav", ".mp3", ".flac")
 
 
@@ -25,9 +19,10 @@ def list_audios(basePath, contains=None):
     # return the set of files that are valid
     return list_files(basePath, validExts=audio_types, contains=contains)
 
+
 def list_files(basePath, validExts=None, contains=None):
     # loop over the directory structure
-    for (rootDir, dirNames, filenames) in os.walk(basePath):
+    for rootDir, dirNames, filenames in os.walk(basePath):
         # loop over the filenames in the current directory
         for filename in filenames:
             # if the contains string is not none and the filename does not contain
@@ -36,7 +31,7 @@ def list_files(basePath, validExts=None, contains=None):
                 continue
 
             # determine the file extension of the current file
-            ext = filename[filename.rfind("."):].lower()
+            ext = filename[filename.rfind(".") :].lower()
 
             # check to see if the file is an audio and should be processed
             if validExts is None or ext.endswith(validExts):
@@ -44,13 +39,22 @@ def list_files(basePath, validExts=None, contains=None):
                 audioPath = os.path.join(rootDir, filename)
                 yield audioPath
 
-def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0.2, eval_percentage=0.15, speaker_name="coqui", gradio_progress=None):
+
+def format_audio_list(
+    audio_files,
+    target_language="en",
+    out_path=None,
+    buffer=0.2,
+    eval_percentage=0.15,
+    speaker_name="coqui",
+    gradio_progress=None,
+):
     audio_total_size = 0
     # make sure that ooutput file exists
     os.makedirs(out_path, exist_ok=True)
 
     # Loading Whisper
-    device = "cuda" if torch.cuda.is_available() else "cpu" 
+    device = "cuda" if torch.cuda.is_available() else "cpu"
 
     print("Loading Whisper Model!")
     asr_model = WhisperModel("large-v2", device=device, compute_type="float16")
@@ -69,7 +73,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
             wav = torch.mean(wav, dim=0, keepdim=True)
 
         wav = wav.squeeze()
-        audio_total_size += (wav.size(-1) / sr)
+        audio_total_size += wav.size(-1) / sr
 
         segments, _ = asr_model.transcribe(audio_path, word_timestamps=True, language=target_language)
         segments = list(segments)
@@ -94,7 +98,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
                     # get previous sentence end
                     previous_word_end = words_list[word_idx - 1].end
                     # add buffer or get the silence midle between the previous sentence and the current one
-                    sentence_start = max(sentence_start - buffer, (previous_word_end + sentence_start)/2)
+                    sentence_start = max(sentence_start - buffer, (previous_word_end + sentence_start) / 2)
 
                 sentence = word.word
                 first_word = False
@@ -118,19 +122,16 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
 
                 # Average the current word end and next word start
                 word_end = min((word.end + next_word_start) / 2, word.end + buffer)
-                
+
                 absoulte_path = os.path.join(out_path, audio_file)
                 os.makedirs(os.path.dirname(absoulte_path), exist_ok=True)
                 i += 1
                 first_word = True
 
-                audio = wav[int(sr*sentence_start):int(sr*word_end)].unsqueeze(0)
+                audio = wav[int(sr * sentence_start) : int(sr * word_end)].unsqueeze(0)
                 # if the audio is too short ignore it (i.e < 0.33 seconds)
-                if audio.size(-1) >= sr/3:
+                if audio.size(-1) >= sr / 3:
-                    torchaudio.save(absoulte_path,
+                    torchaudio.save(absoulte_path, audio, sr)
-                        audio,
-                        sr
-                    )
                 else:
                     continue
 
@@ -140,21 +141,21 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
 
     df = pandas.DataFrame(metadata)
     df = df.sample(frac=1)
-    num_val_samples = int(len(df)*eval_percentage)
+    num_val_samples = int(len(df) * eval_percentage)
 
     df_eval = df[:num_val_samples]
     df_train = df[num_val_samples:]
 
-    df_train = df_train.sort_values('audio_file')
+    df_train = df_train.sort_values("audio_file")
     train_metadata_path = os.path.join(out_path, "metadata_train.csv")
     df_train.to_csv(train_metadata_path, sep="|", index=False)
 
     eval_metadata_path = os.path.join(out_path, "metadata_eval.csv")
-    df_eval = df_eval.sort_values('audio_file')
+    df_eval = df_eval.sort_values("audio_file")
     df_eval.to_csv(eval_metadata_path, sep="|", index=False)
 
     # deallocate VRAM and RAM
     del asr_model, df_train, df_eval, df, metadata
     gc.collect()
 
-    return train_metadata_path, eval_metadata_path, audio_total_size
+    return train_metadata_path, eval_metadata_path, audio_total_size

TTS/demos/xtts_ft_demo/utils/gpt_train.py

@@ -1,5 +1,5 @@
-import os
 import gc
+import os
 
 from trainer import Trainer, TrainerArgs
 
@@ -25,7 +25,6 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     BATCH_SIZE = batch_size  # set here the batch size
     GRAD_ACUMM_STEPS = grad_acumm  # set here the grad accumulation steps
 
-
     # Define here the dataset that you want to use for the fine-tuning on.
     config_dataset = BaseDatasetConfig(
         formatter="coqui",
@@ -43,7 +42,6 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
     os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
 
-
     # DVAE files
     DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
     MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"
@@ -55,8 +53,9 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     # download DVAE files if needed
     if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
         print(" > Downloading DVAE files!")
-        ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
+        ModelManager._download_model_files(
-
+            [MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
+        )
 
     # Download XTTS v2.0 checkpoint if needed
     TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
@@ -160,7 +159,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
 
     # get the longest text audio file to use as speaker reference
     samples_len = [len(item["text"].split(" ")) for item in train_samples]
-    longest_text_idx =  samples_len.index(max(samples_len))
+    longest_text_idx = samples_len.index(max(samples_len))
     speaker_ref = train_samples[longest_text_idx]["audio_file"]
 
     trainer_out_path = trainer.output_path

TTS/demos/xtts_ft_demo/xtts_demo.py

@@ -1,19 +1,16 @@
 import argparse
+import logging
 import os
 import sys
 import tempfile
+import traceback
 
 import gradio as gr
-import librosa.display
-import numpy as np
-
-import os
 import torch
 import torchaudio
-import traceback
+
 from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
 from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
-
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 
@@ -23,7 +20,10 @@ def clear_gpu_cache():
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
 
+
 XTTS_MODEL = None
+
+
 def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
     global XTTS_MODEL
     clear_gpu_cache()
@@ -40,17 +40,23 @@ def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
     print("Model Loaded!")
     return "Model Loaded!"
 
+
 def run_tts(lang, tts_text, speaker_audio_file):
     if XTTS_MODEL is None or not speaker_audio_file:
         return "You need to run the previous step to load the model !!", None, None
 
-    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
+    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(
+        audio_path=speaker_audio_file,
+        gpt_cond_len=XTTS_MODEL.config.gpt_cond_len,
+        max_ref_length=XTTS_MODEL.config.max_ref_len,
+        sound_norm_refs=XTTS_MODEL.config.sound_norm_refs,
+    )
     out = XTTS_MODEL.inference(
         text=tts_text,
         language=lang,
         gpt_cond_latent=gpt_cond_latent,
         speaker_embedding=speaker_embedding,
-        temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
+        temperature=XTTS_MODEL.config.temperature,  # Add custom parameters here
         length_penalty=XTTS_MODEL.config.length_penalty,
         repetition_penalty=XTTS_MODEL.config.repetition_penalty,
         top_k=XTTS_MODEL.config.top_k,
@@ -65,9 +71,7 @@ def run_tts(lang, tts_text, speaker_audio_file):
     return "Speech generated !", out_path, speaker_audio_file
 
 
-
+# define a logger to redirect
-
-# define a logger to redirect 
 class Logger:
     def __init__(self, filename="log.out"):
         self.log_file = filename
@@ -85,21 +89,19 @@ class Logger:
     def isatty(self):
         return False
 
+
 # redirect stdout and stderr to a file
 sys.stdout = Logger()
 sys.stderr = sys.stdout
 
 
 # logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-import logging
+
 logging.basicConfig(
-    level=logging.INFO,
+    level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[logging.StreamHandler(sys.stdout)]
-    format="%(asctime)s [%(levelname)s] %(message)s",
-    handlers=[
-        logging.StreamHandler(sys.stdout)
-    ]
 )
 
+
 def read_logs():
     sys.stdout.flush()
     with open(sys.stdout.log_file, "r") as f:
@@ -107,12 +109,11 @@ def read_logs():
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser(
         description="""XTTS fine-tuning demo\n\n"""
         """
         Example runs:
-        python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port 
+        python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port
         """,
         formatter_class=argparse.RawTextHelpFormatter,
     )
@@ -190,12 +191,11 @@ if __name__ == "__main__":
                     "zh",
                     "hu",
                     "ko",
-                    "ja"
+                    "ja",
+                    "hi",
                 ],
             )
-            progress_data = gr.Label(
+            progress_data = gr.Label(label="Progress:")
-                label="Progress:"
-            )
             logs = gr.Textbox(
                 label="Logs:",
                 interactive=False,
@@ -203,20 +203,30 @@ if __name__ == "__main__":
             demo.load(read_logs, None, logs, every=1)
 
             prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
-        
+
             def preprocess_dataset(audio_path, language, out_path, progress=gr.Progress(track_tqdm=True)):
                 clear_gpu_cache()
                 out_path = os.path.join(out_path, "dataset")
                 os.makedirs(out_path, exist_ok=True)
                 if audio_path is None:
-                    return "You should provide one or multiple audio files! If you provided it, probably the upload of the files is not finished yet!", "", ""
+                    return (
+                        "You should provide one or multiple audio files! If you provided it, probably the upload of the files is not finished yet!",
+                        "",
+                        "",
+                    )
                 else:
                     try:
-                        train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
+                        train_meta, eval_meta, audio_total_size = format_audio_list(
+                            audio_path, target_language=language, out_path=out_path, gradio_progress=progress
+                        )
                     except:
                         traceback.print_exc()
                         error = traceback.format_exc()
-                        return f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}", "", ""
+                        return (
+                            f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}",
+                            "",
+                            "",
+                        )
 
                 clear_gpu_cache()
 
@@ -236,7 +246,7 @@ if __name__ == "__main__":
             eval_csv = gr.Textbox(
                 label="Eval CSV:",
             )
-            num_epochs =  gr.Slider(
+            num_epochs = gr.Slider(
                 label="Number of epochs:",
                 minimum=1,
                 maximum=100,
@@ -264,9 +274,7 @@ if __name__ == "__main__":
                 step=1,
                 value=args.max_audio_length,
             )
-            progress_train = gr.Label(
+            progress_train = gr.Label(label="Progress:")
-                label="Progress:"
-            )
             logs_tts_train = gr.Textbox(
                 label="Logs:",
                 interactive=False,
@@ -274,18 +282,41 @@ if __name__ == "__main__":
             demo.load(read_logs, None, logs_tts_train, every=1)
             train_btn = gr.Button(value="Step 2 - Run the training")
 
-            def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
+            def train_model(
+                language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length
+            ):
                 clear_gpu_cache()
                 if not train_csv or not eval_csv:
-                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                    return (
+                        "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !",
+                        "",
+                        "",
+                        "",
+                        "",
+                    )
                 try:
                     # convert seconds to waveform frames
                     max_audio_length = int(max_audio_length * 22050)
-                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path, max_audio_length=max_audio_length)
+                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(
+                        language,
+                        num_epochs,
+                        batch_size,
+                        grad_acumm,
+                        train_csv,
+                        eval_csv,
+                        output_path=output_path,
+                        max_audio_length=max_audio_length,
+                    )
                 except:
                     traceback.print_exc()
                     error = traceback.format_exc()
-                    return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
+                    return (
+                        f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}",
+                        "",
+                        "",
+                        "",
+                        "",
+                    )
 
                 # copy original files to avoid parameters changes issues
                 os.system(f"cp {config_path} {exp_path}")
@@ -312,9 +343,7 @@ if __name__ == "__main__":
                         label="XTTS vocab path:",
                         value="",
                     )
-                    progress_load = gr.Label(
+                    progress_load = gr.Label(label="Progress:")
-                        label="Progress:"
-                    )
                     load_btn = gr.Button(value="Step 3 - Load Fine-tuned XTTS model")
 
                 with gr.Column() as col2:
@@ -342,7 +371,8 @@ if __name__ == "__main__":
                             "hu",
                             "ko",
                             "ja",
-                        ]
+                            "hi",
+                        ],
                     )
                     tts_text = gr.Textbox(
                         label="Input Text.",
@@ -351,9 +381,7 @@ if __name__ == "__main__":
                     tts_btn = gr.Button(value="Step 4 - Inference")
 
                 with gr.Column() as col3:
-                    progress_gen = gr.Label(
+                    progress_gen = gr.Label(label="Progress:")
-                        label="Progress:"
-                    )
                     tts_output_audio = gr.Audio(label="Generated Audio.")
                     reference_audio = gr.Audio(label="Reference audio used.")
 
@@ -371,7 +399,6 @@ if __name__ == "__main__":
                 ],
             )
 
-
             train_btn.click(
                 fn=train_model,
                 inputs=[
@@ -386,14 +413,10 @@ if __name__ == "__main__":
                 ],
                 outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
             )
-            
+
             load_btn.click(
                 fn=load_model,
-                inputs=[
+                inputs=[xtts_checkpoint, xtts_config, xtts_vocab],
-                    xtts_checkpoint,
-                    xtts_config,
-                    xtts_vocab
-                ],
                 outputs=[progress_load],
             )
 
@@ -407,9 +430,4 @@ if __name__ == "__main__":
                 outputs=[progress_gen, tts_output_audio, reference_audio],
             )
 
-    demo.launch(
+    demo.launch(share=True, debug=False, server_port=args.port, server_name="0.0.0.0")
-        share=True,
-        debug=False,
-        server_port=args.port,
-        server_name="0.0.0.0"
-    )

TTS/encoder/README.md

@@ -14,5 +14,5 @@ To run the code, you need to follow the same flow as in TTS.
 
 - Define 'config.json' for your needs. Note that, audio parameters should match your TTS model.
 - Example training call ```python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360```
-- Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth model/config/path/config.json dataset/path/ output_path``` . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files.
+- Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda /model/path/best_model.pth model/config/path/config.json dataset/path/ output_path``` . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files.
 - Watch training on Tensorboard as in TTS

TTS/encoder/configs/emotion_encoder_config.py

@@ -1,4 +1,4 @@
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 
 from TTS.encoder.configs.base_encoder_config import BaseEncoderConfig
 

TTS/encoder/configs/speaker_encoder_config.py

@@ -1,4 +1,4 @@
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 
 from TTS.encoder.configs.base_encoder_config import BaseEncoderConfig
 

TTS/encoder/dataset.py

@@ -1,3 +1,4 @@
+import logging
 import random
 
 import torch
@@ -5,6 +6,8 @@ from torch.utils.data import Dataset
 
 from TTS.encoder.utils.generic_utils import AugmentWAV
 
+logger = logging.getLogger(__name__)
+
 
 class EncoderDataset(Dataset):
     def __init__(
@@ -15,7 +18,6 @@ class EncoderDataset(Dataset):
         voice_len=1.6,
         num_classes_in_batch=64,
         num_utter_per_class=10,
-        verbose=False,
         augmentation_config=None,
         use_torch_spec=None,
     ):
@@ -24,7 +26,6 @@ class EncoderDataset(Dataset):
             ap (TTS.tts.utils.AudioProcessor): audio processor object.
             meta_data (list): list of dataset instances.
             seq_len (int): voice segment length in seconds.
-            verbose (bool): print diagnostic information.
         """
         super().__init__()
         self.config = config
@@ -33,7 +34,6 @@ class EncoderDataset(Dataset):
         self.seq_len = int(voice_len * self.sample_rate)
         self.num_utter_per_class = num_utter_per_class
         self.ap = ap
-        self.verbose = verbose
         self.use_torch_spec = use_torch_spec
         self.classes, self.items = self.__parse_items()
 
@@ -50,13 +50,12 @@ class EncoderDataset(Dataset):
             if "gaussian" in augmentation_config.keys():
                 self.gaussian_augmentation_config = augmentation_config["gaussian"]
 
-        if self.verbose:
+        logger.info("DataLoader initialization")
-            print("\n > DataLoader initialization")
+        logger.info(" | Classes per batch: %d", num_classes_in_batch)
-            print(f" | > Classes per Batch: {num_classes_in_batch}")
+        logger.info(" | Number of instances: %d", len(self.items))
-            print(f" | > Number of instances : {len(self.items)}")
+        logger.info(" | Sequence length: %d", self.seq_len)
-            print(f" | > Sequence length: {self.seq_len}")
+        logger.info(" | Number of classes: %d", len(self.classes))
-            print(f" | > Num Classes: {len(self.classes)}")
+        logger.info(" | Classes: %s", self.classes)
-            print(f" | > Classes: {self.classes}")
 
     def load_wav(self, filename):
         audio = self.ap.load_wav(filename, sr=self.ap.sample_rate)

TTS/encoder/losses.py

@@ -1,7 +1,11 @@
+import logging
+
 import torch
 import torch.nn.functional as F
 from torch import nn
 
+logger = logging.getLogger(__name__)
+
 
 # adapted from https://github.com/cvqluu/GE2E-Loss
 class GE2ELoss(nn.Module):
@@ -23,7 +27,7 @@ class GE2ELoss(nn.Module):
         self.b = nn.Parameter(torch.tensor(init_b))
         self.loss_method = loss_method
 
-        print(" > Initialized Generalized End-to-End loss")
+        logger.info("Initialized Generalized End-to-End loss")
 
         assert self.loss_method in ["softmax", "contrast"]
 
@@ -139,7 +143,7 @@ class AngleProtoLoss(nn.Module):
         self.b = nn.Parameter(torch.tensor(init_b))
         self.criterion = torch.nn.CrossEntropyLoss()
 
-        print(" > Initialized Angular Prototypical loss")
+        logger.info("Initialized Angular Prototypical loss")
 
     def forward(self, x, _label=None):
         """
@@ -177,7 +181,7 @@ class SoftmaxLoss(nn.Module):
         self.criterion = torch.nn.CrossEntropyLoss()
         self.fc = nn.Linear(embedding_dim, n_speakers)
 
-        print("Initialised Softmax Loss")
+        logger.info("Initialised Softmax Loss")
 
     def forward(self, x, label=None):
         # reshape for compatibility
@@ -212,7 +216,7 @@ class SoftmaxAngleProtoLoss(nn.Module):
         self.softmax = SoftmaxLoss(embedding_dim, n_speakers)
         self.angleproto = AngleProtoLoss(init_w, init_b)
 
-        print("Initialised SoftmaxAnglePrototypical Loss")
+        logger.info("Initialised SoftmaxAnglePrototypical Loss")
 
     def forward(self, x, label=None):
         """

TTS/encoder/models/base_encoder.py

@@ -1,12 +1,16 @@
+import logging
+
 import numpy as np
 import torch
 import torchaudio
 from coqpit import Coqpit
 from torch import nn
+from trainer.io import load_fsspec
 
 from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
 from TTS.utils.generic_utils import set_init_dict
-from TTS.utils.io import load_fsspec
+
+logger = logging.getLogger(__name__)
 
 
 class PreEmphasis(nn.Module):
@@ -118,13 +122,13 @@ class BaseEncoder(nn.Module):
         state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
         try:
             self.load_state_dict(state["model"])
-            print(" > Model fully restored. ")
+            logger.info("Model fully restored. ")
         except (KeyError, RuntimeError) as error:
             # If eval raise the error
             if eval:
                 raise error
 
-            print(" > Partial model initialization.")
+            logger.info("Partial model initialization.")
             model_dict = self.state_dict()
             model_dict = set_init_dict(model_dict, state["model"], c)
             self.load_state_dict(model_dict)
@@ -135,7 +139,7 @@ class BaseEncoder(nn.Module):
             try:
                 criterion.load_state_dict(state["criterion"])
             except (KeyError, RuntimeError) as error:
-                print(" > Criterion load ignored because of:", error)
+                logger.exception("Criterion load ignored because of: %s", error)
 
         # instance and load the criterion for the encoder classifier in inference time
         if (

TTS/encoder/utils/generic_utils.py

@@ -1,4 +1,5 @@
 import glob
+import logging
 import os
 import random
 
@@ -8,6 +9,8 @@ from scipy import signal
 from TTS.encoder.models.lstm import LSTMSpeakerEncoder
 from TTS.encoder.models.resnet import ResNetSpeakerEncoder
 
+logger = logging.getLogger(__name__)
+
 
 class AugmentWAV(object):
     def __init__(self, ap, augmentation_config):
@@ -34,12 +37,14 @@ class AugmentWAV(object):
                     # ignore not listed directories
                     if noise_dir not in self.additive_noise_types:
                         continue
-                    if not noise_dir in self.noise_list:
+                    if noise_dir not in self.noise_list:
                         self.noise_list[noise_dir] = []
                     self.noise_list[noise_dir].append(wav_file)
 
-                print(
+                logger.info(
-                    f" | > Using Additive Noise Augmentation: with {len(additive_files)} audios instances from {self.additive_noise_types}"
+                    "Using Additive Noise Augmentation: with %d audios instances from %s",
+                    len(additive_files),
+                    self.additive_noise_types,
                 )
 
         self.use_rir = False
@@ -50,7 +55,7 @@ class AugmentWAV(object):
                 self.rir_files = glob.glob(os.path.join(self.rir_config["rir_path"], "**/*.wav"), recursive=True)
                 self.use_rir = True
 
-            print(f" | > Using RIR Noise Augmentation: with {len(self.rir_files)} audios instances")
+            logger.info("Using RIR Noise Augmentation: with %d audios instances", len(self.rir_files))
 
         self.create_augmentation_global_list()
 

TTS/encoder/utils/prepare_voxceleb.py

@@ -19,15 +19,19 @@
 # pylint: disable=too-many-locals, too-many-statements, too-many-arguments, too-many-instance-attributes
 """ voxceleb 1 & 2 """
 
+import csv
 import hashlib
+import logging
 import os
 import subprocess
 import sys
 import zipfile
 
-import pandas
 import soundfile as sf
-from absl import logging
+
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
+
+logger = logging.getLogger(__name__)
 
 SUBSETS = {
     "vox1_dev_wav": [
@@ -77,14 +81,14 @@ def download_and_extract(directory, subset, urls):
             zip_filepath = os.path.join(directory, url.split("/")[-1])
             if os.path.exists(zip_filepath):
                 continue
-            logging.info("Downloading %s to %s" % (url, zip_filepath))
+            logger.info("Downloading %s to %s" % (url, zip_filepath))
             subprocess.call(
                 "wget %s --user %s --password %s -O %s" % (url, USER["user"], USER["password"], zip_filepath),
                 shell=True,
             )
 
             statinfo = os.stat(zip_filepath)
-            logging.info("Successfully downloaded %s, size(bytes): %d" % (url, statinfo.st_size))
+            logger.info("Successfully downloaded %s, size(bytes): %d" % (url, statinfo.st_size))
 
         # concatenate all parts into zip files
         if ".zip" not in zip_filepath:
@@ -118,9 +122,9 @@ def exec_cmd(cmd):
     try:
         retcode = subprocess.call(cmd, shell=True)
         if retcode < 0:
-            logging.info(f"Child was terminated by signal {retcode}")
+            logger.info(f"Child was terminated by signal {retcode}")
     except OSError as e:
-        logging.info(f"Execution failed: {e}")
+        logger.info(f"Execution failed: {e}")
         retcode = -999
     return retcode
 
@@ -134,11 +138,11 @@ def decode_aac_with_ffmpeg(aac_file, wav_file):
         bool, True if success.
     """
     cmd = f"ffmpeg -i {aac_file} {wav_file}"
-    logging.info(f"Decoding aac file using command line: {cmd}")
+    logger.info(f"Decoding aac file using command line: {cmd}")
     ret = exec_cmd(cmd)
     if ret != 0:
-        logging.error(f"Failed to decode aac file with retcode {ret}")
+        logger.error(f"Failed to decode aac file with retcode {ret}")
-        logging.error("Please check your ffmpeg installation.")
+        logger.error("Please check your ffmpeg installation.")
         return False
     return True
 
@@ -152,7 +156,7 @@ def convert_audio_and_make_label(input_dir, subset, output_dir, output_file):
         output_file: the name of the newly generated csv file. e.g. vox1_dev_wav.csv
     """
 
-    logging.info("Preprocessing audio and label for subset %s" % subset)
+    logger.info("Preprocessing audio and label for subset %s" % subset)
     source_dir = os.path.join(input_dir, subset)
 
     files = []
@@ -185,9 +189,12 @@ def convert_audio_and_make_label(input_dir, subset, output_dir, output_file):
     # Write to CSV file which contains four columns:
     # "wav_filename", "wav_length_ms", "speaker_id", "speaker_name".
     csv_file_path = os.path.join(output_dir, output_file)
-    df = pandas.DataFrame(data=files, columns=["wav_filename", "wav_length_ms", "speaker_id", "speaker_name"])
+    with open(csv_file_path, "w", newline="", encoding="utf-8") as f:
-    df.to_csv(csv_file_path, index=False, sep="\t")
+        writer = csv.writer(f, delimiter="\t")
-    logging.info("Successfully generated csv file {}".format(csv_file_path))
+        writer.writerow(["wav_filename", "wav_length_ms", "speaker_id", "speaker_name"])
+        for wav_file in files:
+            writer.writerow(wav_file)
+    logger.info("Successfully generated csv file {}".format(csv_file_path))
 
 
 def processor(directory, subset, force_process):
@@ -200,16 +207,16 @@ def processor(directory, subset, force_process):
     if not force_process and os.path.exists(subset_csv):
         return subset_csv
 
-    logging.info("Downloading and process the voxceleb in %s", directory)
+    logger.info("Downloading and process the voxceleb in %s", directory)
-    logging.info("Preparing subset %s", subset)
+    logger.info("Preparing subset %s", subset)
     download_and_extract(directory, subset, urls[subset])
     convert_audio_and_make_label(directory, subset, directory, subset + ".csv")
-    logging.info("Finished downloading and processing")
+    logger.info("Finished downloading and processing")
     return subset_csv
 
 
 if __name__ == "__main__":
-    logging.set_verbosity(logging.INFO)
+    setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
     if len(sys.argv) != 4:
         print("Usage: python prepare_data.py save_directory user password")
         sys.exit()

TTS/encoder/utils/training.py

@@ -3,13 +3,13 @@ from dataclasses import dataclass, field
 
 from coqpit import Coqpit
 from trainer import TrainerArgs, get_last_checkpoint
+from trainer.generic_utils import get_experiment_folder_path, get_git_branch
 from trainer.io import copy_model_files
 from trainer.logging import logger_factory
 from trainer.logging.console_logger import ConsoleLogger
 
 from TTS.config import load_config, register_config
 from TTS.tts.utils.text.characters import parse_symbols
-from TTS.utils.generic_utils import get_experiment_folder_path, get_git_branch
 
 
 @dataclass
@@ -29,7 +29,7 @@ def process_args(args, config=None):
         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.
+        c (Coqpit): 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

TTS/model.py

@@ -1,5 +1,6 @@
+import os
 from abc import abstractmethod
-from typing import Dict
+from typing import Any, Union
 
 import torch
 from coqpit import Coqpit
@@ -16,7 +17,7 @@ class BaseTrainerModel(TrainerModel):
 
     @staticmethod
     @abstractmethod
-    def init_from_config(config: Coqpit):
+    def init_from_config(config: Coqpit) -> "BaseTrainerModel":
         """Init the model and all its attributes from the given config.
 
         Override this depending on your model.
@@ -24,7 +25,7 @@ class BaseTrainerModel(TrainerModel):
         ...
 
     @abstractmethod
-    def inference(self, input: torch.Tensor, aux_input={}) -> Dict:
+    def inference(self, input: torch.Tensor, aux_input: dict[str, Any] = {}) -> dict[str, Any]:
         """Forward pass for inference.
 
         It must return a dictionary with the main model output and all the auxiliary outputs. The key ```model_outputs```
@@ -45,15 +46,21 @@ class BaseTrainerModel(TrainerModel):
 
     @abstractmethod
     def load_checkpoint(
-        self, config: Coqpit, checkpoint_path: str, eval: bool = False, strict: bool = True, cache=False
+        self,
+        config: Coqpit,
+        checkpoint_path: Union[str, os.PathLike[Any]],
+        eval: bool = False,
+        strict: bool = True,
+        cache: bool = False,
     ) -> None:
-        """Load a model checkpoint gile and get ready for training or inference.
+        """Load a model checkpoint file and get ready for training or inference.
 
         Args:
             config (Coqpit): Model configuration.
-            checkpoint_path (str): Path to the model checkpoint file.
+            checkpoint_path (str | os.PathLike): Path to the model checkpoint file.
             eval (bool, optional): If true, init model for inference else for training. Defaults to False.
             strict (bool, optional): Match all checkpoint keys to model's keys. Defaults to True.
-            cache (bool, optional): If True, cache the file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to False.
+            cache (bool, optional): If True, cache the file locally for subsequent calls.
+                It is cached under `trainer.io.get_user_data_dir()/tts_cache`. Defaults to False.
         """
         ...

TTS/server/README.md

@@ -1,5 +1,8 @@
 # :frog: TTS demo server
-Before you use the server, make sure you [install](https://github.com/coqui-ai/TTS/tree/dev#install-tts)) :frog: TTS properly. Then, you can follow the steps below.
+Before you use the server, make sure you
+[install](https://github.com/idiap/coqui-ai-TTS/tree/dev#install-tts)) :frog: TTS
+properly and install the additional dependencies with `pip install
+coqui-tts[server]`. Then, you can follow the steps below.
 
 **Note:** If you install :frog:TTS using ```pip```, you can also use the ```tts-server``` end point on the terminal.
 
@@ -12,7 +15,7 @@ Run the server with the official models.
 ```python TTS/server/server.py  --model_name tts_models/en/ljspeech/tacotron2-DCA --vocoder_name vocoder_models/en/ljspeech/multiband-melgan```
 
 Run the server with the official models on a GPU.
-```CUDA_VISIBLE_DEVICES="0" python TTS/server/server.py  --model_name tts_models/en/ljspeech/tacotron2-DCA --vocoder_name vocoder_models/en/ljspeech/multiband-melgan --use_cuda True```
+```CUDA_VISIBLE_DEVICES="0" python TTS/server/server.py  --model_name tts_models/en/ljspeech/tacotron2-DCA --vocoder_name vocoder_models/en/ljspeech/multiband-melgan --use_cuda```
 
 Run the server with a custom models.
 ```python TTS/server/server.py  --tts_checkpoint /path/to/tts/model.pth --tts_config /path/to/tts/config.json --vocoder_checkpoint /path/to/vocoder/model.pth --vocoder_config /path/to/vocoder/config.json```

TTS/server/server.py

@@ -1,7 +1,11 @@
 #!flask/bin/python
+
+"""TTS demo server."""
+
 import argparse
 import io
 import json
+import logging
 import os
 import sys
 from pathlib import Path
@@ -9,24 +13,26 @@ from threading import Lock
 from typing import Union
 from urllib.parse import parse_qs
 
-from flask import Flask, render_template, render_template_string, request, send_file
+try:
+    from flask import Flask, render_template, render_template_string, request, send_file
+except ImportError as e:
+    msg = "Server requires requires flask, use `pip install coqui-tts[server]`"
+    raise ImportError(msg) from e
 
 from TTS.config import load_config
+from TTS.utils.generic_utils import ConsoleFormatter, setup_logger
 from TTS.utils.manage import ModelManager
 from TTS.utils.synthesizer import Synthesizer
 
+logger = logging.getLogger(__name__)
+setup_logger("TTS", level=logging.INFO, screen=True, formatter=ConsoleFormatter())
 
-def create_argparser():
-    def convert_boolean(x):
-        return x.lower() in ["true", "1", "yes"]
 
+def create_argparser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--list_models",
-        type=convert_boolean,
+        action="store_true",
-        nargs="?",
-        const=True,
-        default=False,
         help="list available pre-trained tts and vocoder models.",
     )
     parser.add_argument(
@@ -54,9 +60,13 @@ def create_argparser():
     parser.add_argument("--vocoder_config_path", type=str, help="Path to vocoder model config file.", default=None)
     parser.add_argument("--speakers_file_path", type=str, help="JSON file for multi-speaker model.", default=None)
     parser.add_argument("--port", type=int, default=5002, help="port to listen on.")
-    parser.add_argument("--use_cuda", type=convert_boolean, default=False, help="true to use CUDA.")
+    parser.add_argument("--use_cuda", action=argparse.BooleanOptionalAction, default=False, help="true to use CUDA.")
-    parser.add_argument("--debug", type=convert_boolean, default=False, help="true to enable Flask debug mode.")
+    parser.add_argument(
-    parser.add_argument("--show_details", type=convert_boolean, default=False, help="Generate model detail page.")
+        "--debug", action=argparse.BooleanOptionalAction, default=False, help="true to enable Flask debug mode."
+    )
+    parser.add_argument(
+        "--show_details", action=argparse.BooleanOptionalAction, default=False, help="Generate model detail page."
+    )
     return parser
 
 
@@ -66,10 +76,6 @@ args = create_argparser().parse_args()
 path = Path(__file__).parent / "../.models.json"
 manager = ModelManager(path)
 
-if args.list_models:
-    manager.list_models()
-    sys.exit()
-
 # update in-use models to the specified released models.
 model_path = None
 config_path = None
@@ -164,17 +170,15 @@ def index():
 def details():
     if args.config_path is not None and os.path.isfile(args.config_path):
         model_config = load_config(args.config_path)
-    else:
+    elif args.model_name is not None:
-        if args.model_name is not None:
+        model_config = load_config(config_path)
-            model_config = load_config(config_path)
 
     if args.vocoder_config_path is not None and os.path.isfile(args.vocoder_config_path):
         vocoder_config = load_config(args.vocoder_config_path)
+    elif args.vocoder_name is not None:
+        vocoder_config = load_config(vocoder_config_path)
     else:
-        if args.vocoder_name is not None:
+        vocoder_config = None
-            vocoder_config = load_config(vocoder_config_path)
-        else:
-            vocoder_config = None
 
     return render_template(
         "details.html",
@@ -197,9 +201,9 @@ def tts():
         style_wav = request.headers.get("style-wav") or request.values.get("style_wav", "")
         style_wav = style_wav_uri_to_dict(style_wav)
 
-        print(f" > Model input: {text}")
+        logger.info("Model input: %s", text)
-        print(f" > Speaker Idx: {speaker_idx}")
+        logger.info("Speaker idx: %s", speaker_idx)
-        print(f" > Language Idx: {language_idx}")
+        logger.info("Language idx: %s", language_idx)
         wavs = synthesizer.tts(text, speaker_name=speaker_idx, language_name=language_idx, style_wav=style_wav)
         out = io.BytesIO()
         synthesizer.save_wav(wavs, out)
@@ -243,7 +247,7 @@ def mary_tts_api_process():
             text = data.get("INPUT_TEXT", [""])[0]
         else:
             text = request.args.get("INPUT_TEXT", "")
-        print(f" > Model input: {text}")
+        logger.info("Model input: %s", text)
         wavs = synthesizer.tts(text)
         out = io.BytesIO()
         synthesizer.save_wav(wavs, out)

TTS/server/templates/details.html

@@ -128,4 +128,4 @@
 
 </body>
 
-</html>
+</html>

TTS/server/templates/index.html

@@ -30,7 +30,7 @@
 </head>
 
 <body>
-    <a href="https://github.com/coqui-ai/TTS"><img style="position: absolute; z-index:1000; top: 0; left: 0; border: 0;"
+    <a href="https://github.com/idiap/coqui-ai-TTS"><img style="position: absolute; z-index:1000; top: 0; left: 0; border: 0;"
             src="https://s3.amazonaws.com/github/ribbons/forkme_left_darkblue_121621.png" alt="Fork me on GitHub"></a>
 
     <!-- Navigation -->
@@ -151,4 +151,4 @@
 
 </body>
 
-</html>
+</html>

TTS/tts/configs/bark_config.py

@@ -2,11 +2,12 @@ import os
 from dataclasses import dataclass, field
 from typing import Dict
 
+from trainer.io import get_user_data_dir
+
 from TTS.tts.configs.shared_configs import BaseTTSConfig
 from TTS.tts.layers.bark.model import GPTConfig
 from TTS.tts.layers.bark.model_fine import FineGPTConfig
 from TTS.tts.models.bark import BarkAudioConfig
-from TTS.utils.generic_utils import get_user_data_dir
 
 
 @dataclass

TTS/tts/datasets/__init__.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import sys
 from collections import Counter
@@ -9,6 +10,8 @@ import numpy as np
 from TTS.tts.datasets.dataset import *
 from TTS.tts.datasets.formatters import *
 
+logger = logging.getLogger(__name__)
+
 
 def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
     """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.
@@ -122,7 +125,7 @@ def load_tts_samples(
 
         meta_data_train = add_extra_keys(meta_data_train, language, dataset_name)
 
-        print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
+        logger.info("Found %d files in %s", len(meta_data_train), Path(root_path).resolve())
         # load evaluation split if set
         if eval_split:
             if meta_file_val:
@@ -166,16 +169,15 @@ def _get_formatter_by_name(name):
     return getattr(thismodule, name.lower())
 
 
-def find_unique_chars(data_samples, verbose=True):
+def find_unique_chars(data_samples):
-    texts = "".join(item[0] for item in data_samples)
+    texts = "".join(item["text"] for item in data_samples)
     chars = set(texts)
     lower_chars = filter(lambda c: c.islower(), chars)
     chars_force_lower = [c.lower() for c in chars]
     chars_force_lower = set(chars_force_lower)
 
-    if verbose:
+    logger.info("Number of unique characters: %d", len(chars))
-        print(f" > Number of unique characters: {len(chars)}")
+    logger.info("Unique characters: %s", "".join(sorted(chars)))
-        print(f" > Unique characters: {''.join(sorted(chars))}")
+    logger.info("Unique lower characters: %s", "".join(sorted(lower_chars)))
-        print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
+    logger.info("Unique all forced to lower characters: %s", "".join(sorted(chars_force_lower)))
-        print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
     return chars_force_lower

TTS/tts/datasets/dataset.py

@@ -1,11 +1,13 @@
 import base64
 import collections
+import logging
 import os
 import random
 from typing import Dict, List, Union
 
 import numpy as np
 import torch
+import torchaudio
 import tqdm
 from torch.utils.data import Dataset
 
@@ -13,7 +15,7 @@ from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.audio.numpy_transforms import compute_energy as calculate_energy
 
-import mutagen
+logger = logging.getLogger(__name__)
 
 # to prevent too many open files error as suggested here
 # https://github.com/pytorch/pytorch/issues/11201#issuecomment-421146936
@@ -44,13 +46,15 @@ def string2filename(string):
     return filename
 
 
-def get_audio_size(audiopath):
+def get_audio_size(audiopath) -> int:
+    """Return the number of samples in the audio file."""
     extension = audiopath.rpartition(".")[-1].lower()
     if extension not in {"mp3", "wav", "flac"}:
-        raise RuntimeError(f"The audio format {extension} is not supported, please convert the audio files to mp3, flac, or wav format!")
+        raise RuntimeError(
+            f"The audio format {extension} is not supported, please convert the audio files to mp3, flac, or wav format!"
+        )
 
-    audio_info = mutagen.File(audiopath).info
+    return torchaudio.info(audiopath).num_frames
-    return int(audio_info.length * audio_info.sample_rate)
 
 
 class TTSDataset(Dataset):
@@ -78,7 +82,6 @@ class TTSDataset(Dataset):
         language_id_mapping: Dict = None,
         use_noise_augment: bool = False,
         start_by_longest: bool = False,
-        verbose: bool = False,
     ):
         """Generic 📂 data loader for `tts` models. It is configurable for different outputs and needs.
 
@@ -136,8 +139,6 @@ class TTSDataset(Dataset):
             use_noise_augment (bool): Enable adding random noise to wav for augmentation. Defaults to False.
 
             start_by_longest (bool): Start by longest sequence. It is especially useful to check OOM. Defaults to False.
-
-            verbose (bool): Print diagnostic information. Defaults to false.
         """
         super().__init__()
         self.batch_group_size = batch_group_size
@@ -161,7 +162,6 @@ class TTSDataset(Dataset):
         self.use_noise_augment = use_noise_augment
         self.start_by_longest = start_by_longest
 
-        self.verbose = verbose
         self.rescue_item_idx = 1
         self.pitch_computed = False
         self.tokenizer = tokenizer
@@ -179,8 +179,7 @@ class TTSDataset(Dataset):
             self.energy_dataset = EnergyDataset(
                 self.samples, self.ap, cache_path=energy_cache_path, precompute_num_workers=precompute_num_workers
             )
-        if self.verbose:
+        self.print_logs()
-            self.print_logs()
 
     @property
     def lengths(self):
@@ -213,11 +212,10 @@ class TTSDataset(Dataset):
 
     def print_logs(self, level: int = 0) -> None:
         indent = "\t" * level
-        print("\n")
+        logger.info("%sDataLoader initialization", indent)
-        print(f"{indent}> DataLoader initialization")
+        logger.info("%s| Tokenizer:", indent)
-        print(f"{indent}| > Tokenizer:")
         self.tokenizer.print_logs(level + 1)
-        print(f"{indent}| > Number of instances : {len(self.samples)}")
+        logger.info("%s| Number of instances : %d", indent, len(self.samples))
 
     def load_wav(self, filename):
         waveform = self.ap.load_wav(filename)
@@ -389,17 +387,15 @@ class TTSDataset(Dataset):
         text_lengths = [s["text_length"] for s in samples]
         self.samples = samples
 
-        if self.verbose:
+        logger.info("Preprocessing samples")
-            print(" | > Preprocessing samples")
+        logger.info("Max text length: {}".format(np.max(text_lengths)))
-            print(" | > Max text length: {}".format(np.max(text_lengths)))
+        logger.info("Min text length: {}".format(np.min(text_lengths)))
-            print(" | > Min text length: {}".format(np.min(text_lengths)))
+        logger.info("Avg text length: {}".format(np.mean(text_lengths)))
-            print(" | > Avg text length: {}".format(np.mean(text_lengths)))
+        logger.info("Max audio length: {}".format(np.max(audio_lengths)))
-            print(" | ")
+        logger.info("Min audio length: {}".format(np.min(audio_lengths)))
-            print(" | > Max audio length: {}".format(np.max(audio_lengths)))
+        logger.info("Avg audio length: {}".format(np.mean(audio_lengths)))
-            print(" | > Min audio length: {}".format(np.min(audio_lengths)))
+        logger.info("Num. instances discarded samples: %d", len(ignore_idx))
-            print(" | > Avg audio length: {}".format(np.mean(audio_lengths)))
+        logger.info("Batch group size: {}.".format(self.batch_group_size))
-            print(f" | > Num. instances discarded samples: {len(ignore_idx)}")
-            print(" | > Batch group size: {}.".format(self.batch_group_size))
 
     @staticmethod
     def _sort_batch(batch, text_lengths):
@@ -456,9 +452,11 @@ class TTSDataset(Dataset):
 
             # lengths adjusted by the reduction factor
             mel_lengths_adjusted = [
-                m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
+                (
-                if m.shape[1] % self.outputs_per_step
+                    m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
-                else m.shape[1]
+                    if m.shape[1] % self.outputs_per_step
+                    else m.shape[1]
+                )
                 for m in mel
             ]
 
@@ -640,7 +638,7 @@ class PhonemeDataset(Dataset):
 
         We use pytorch dataloader because we are lazy.
         """
-        print("[*] Pre-computing phonemes...")
+        logger.info("Pre-computing phonemes...")
         with tqdm.tqdm(total=len(self)) as pbar:
             batch_size = num_workers if num_workers > 0 else 1
             dataloder = torch.utils.data.DataLoader(
@@ -662,11 +660,10 @@ class PhonemeDataset(Dataset):
 
     def print_logs(self, level: int = 0) -> None:
         indent = "\t" * level
-        print("\n")
+        logger.info("%sPhonemeDataset", indent)
-        print(f"{indent}> PhonemeDataset ")
+        logger.info("%s| Tokenizer:", indent)
-        print(f"{indent}| > Tokenizer:")
         self.tokenizer.print_logs(level + 1)
-        print(f"{indent}| > Number of instances : {len(self.samples)}")
+        logger.info("%s| Number of instances : %d", indent, len(self.samples))
 
 
 class F0Dataset:
@@ -698,14 +695,12 @@ class F0Dataset:
         samples: Union[List[List], List[Dict]],
         ap: "AudioProcessor",
         audio_config=None,  # pylint: disable=unused-argument
-        verbose=False,
         cache_path: str = None,
         precompute_num_workers=0,
         normalize_f0=True,
     ):
         self.samples = samples
         self.ap = ap
-        self.verbose = verbose
         self.cache_path = cache_path
         self.normalize_f0 = normalize_f0
         self.pad_id = 0.0
@@ -729,7 +724,7 @@ class F0Dataset:
         return len(self.samples)
 
     def precompute(self, num_workers=0):
-        print("[*] Pre-computing F0s...")
+        logger.info("Pre-computing F0s...")
         with tqdm.tqdm(total=len(self)) as pbar:
             batch_size = num_workers if num_workers > 0 else 1
             # we do not normalize at preproessing
@@ -816,9 +811,8 @@ class F0Dataset:
 
     def print_logs(self, level: int = 0) -> None:
         indent = "\t" * level
-        print("\n")
+        logger.info("%sF0Dataset", indent)
-        print(f"{indent}> F0Dataset ")
+        logger.info("%s| Number of instances : %d", indent, len(self.samples))
-        print(f"{indent}| > Number of instances : {len(self.samples)}")
 
 
 class EnergyDataset:
@@ -849,14 +843,12 @@ class EnergyDataset:
         self,
         samples: Union[List[List], List[Dict]],
         ap: "AudioProcessor",
-        verbose=False,
         cache_path: str = None,
         precompute_num_workers=0,
         normalize_energy=True,
     ):
         self.samples = samples
         self.ap = ap
-        self.verbose = verbose
         self.cache_path = cache_path
         self.normalize_energy = normalize_energy
         self.pad_id = 0.0
@@ -880,7 +872,7 @@ class EnergyDataset:
         return len(self.samples)
 
     def precompute(self, num_workers=0):
-        print("[*] Pre-computing energys...")
+        logger.info("Pre-computing energys...")
         with tqdm.tqdm(total=len(self)) as pbar:
             batch_size = num_workers if num_workers > 0 else 1
             # we do not normalize at preproessing
@@ -968,6 +960,5 @@ class EnergyDataset:
 
     def print_logs(self, level: int = 0) -> None:
         indent = "\t" * level
-        print("\n")
+        logger.info("%senergyDataset")
-        print(f"{indent}> energyDataset ")
+        logger.info("%s| Number of instances : %d", indent, len(self.samples))
-        print(f"{indent}| > Number of instances : {len(self.samples)}")

TTS/tts/datasets/formatters.py

@@ -1,3 +1,5 @@
+import csv
+import logging
 import os
 import re
 import xml.etree.ElementTree as ET
@@ -5,9 +7,10 @@ from glob import glob
 from pathlib import Path
 from typing import List
 
-import pandas as pd
 from tqdm import tqdm
 
+logger = logging.getLogger(__name__)
+
 ########################
 # DATASETS
 ########################
@@ -23,32 +26,34 @@ def cml_tts(root_path, meta_file, ignored_speakers=None):
     num_cols = len(lines[0].split("|"))  # take the first row as reference
     for idx, line in enumerate(lines[1:]):
         if len(line.split("|")) != num_cols:
-            print(f" > Missing column in line {idx + 1} -> {line.strip()}")
+            logger.warning("Missing column in line %d -> %s", idx + 1, line.strip())
     # load metadata
-    metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
+    with open(Path(root_path) / meta_file, newline="", encoding="utf-8") as f:
-    assert all(x in metadata.columns for x in ["wav_filename", "transcript"])
+        reader = csv.DictReader(f, delimiter="|")
-    client_id = None if "client_id" in metadata.columns else "default"
+        metadata = list(reader)
-    emotion_name = None if "emotion_name" in metadata.columns else "neutral"
+    assert all(x in metadata[0] for x in ["wav_filename", "transcript"])
+    client_id = None if "client_id" in metadata[0] else "default"
+    emotion_name = None if "emotion_name" in metadata[0] else "neutral"
     items = []
     not_found_counter = 0
-    for row in metadata.itertuples():
+    for row in metadata:
-        if client_id is None and ignored_speakers is not None and row.client_id in ignored_speakers:
+        if client_id is None and ignored_speakers is not None and row["client_id"] in ignored_speakers:
             continue
-        audio_path = os.path.join(root_path, row.wav_filename)
+        audio_path = os.path.join(root_path, row["wav_filename"])
         if not os.path.exists(audio_path):
             not_found_counter += 1
             continue
         items.append(
             {
-                "text": row.transcript,
+                "text": row["transcript"],
                 "audio_file": audio_path,
-                "speaker_name": client_id if client_id is not None else row.client_id,
+                "speaker_name": client_id if client_id is not None else row["client_id"],
-                "emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
+                "emotion_name": emotion_name if emotion_name is not None else row["emotion_name"],
                 "root_path": root_path,
             }
         )
     if not_found_counter > 0:
-        print(f" | > [!] {not_found_counter} files not found")
+        logger.warning("%d files not found", not_found_counter)
     return items
 
 
@@ -61,32 +66,34 @@ def coqui(root_path, meta_file, ignored_speakers=None):
     num_cols = len(lines[0].split("|"))  # take the first row as reference
     for idx, line in enumerate(lines[1:]):
         if len(line.split("|")) != num_cols:
-            print(f" > Missing column in line {idx + 1} -> {line.strip()}")
+            logger.warning("Missing column in line %d -> %s", idx + 1, line.strip())
     # load metadata
-    metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
+    with open(Path(root_path) / meta_file, newline="", encoding="utf-8") as f:
-    assert all(x in metadata.columns for x in ["audio_file", "text"])
+        reader = csv.DictReader(f, delimiter="|")
-    speaker_name = None if "speaker_name" in metadata.columns else "coqui"
+        metadata = list(reader)
-    emotion_name = None if "emotion_name" in metadata.columns else "neutral"
+    assert all(x in metadata[0] for x in ["audio_file", "text"])
+    speaker_name = None if "speaker_name" in metadata[0] else "coqui"
+    emotion_name = None if "emotion_name" in metadata[0] else "neutral"
     items = []
     not_found_counter = 0
-    for row in metadata.itertuples():
+    for row in metadata:
-        if speaker_name is None and ignored_speakers is not None and row.speaker_name in ignored_speakers:
+        if speaker_name is None and ignored_speakers is not None and row["speaker_name"] in ignored_speakers:
             continue
-        audio_path = os.path.join(root_path, row.audio_file)
+        audio_path = os.path.join(root_path, row["audio_file"])
         if not os.path.exists(audio_path):
             not_found_counter += 1
             continue
         items.append(
             {
-                "text": row.text,
+                "text": row["text"],
                 "audio_file": audio_path,
-                "speaker_name": speaker_name if speaker_name is not None else row.speaker_name,
+                "speaker_name": speaker_name if speaker_name is not None else row["speaker_name"],
-                "emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
+                "emotion_name": emotion_name if emotion_name is not None else row["emotion_name"],
                 "root_path": root_path,
             }
         )
     if not_found_counter > 0:
-        print(f" | > [!] {not_found_counter} files not found")
+        logger.warning("%d files not found", not_found_counter)
     return items
 
 
@@ -169,7 +176,7 @@ def mailabs(root_path, meta_files=None, ignored_speakers=None):
         if isinstance(ignored_speakers, list):
             if speaker_name in ignored_speakers:
                 continue
-        print(" | > {}".format(csv_file))
+        logger.info(csv_file)
         with open(txt_file, "r", encoding="utf-8") as ttf:
             for line in ttf:
                 cols = line.split("|")
@@ -184,7 +191,7 @@ def mailabs(root_path, meta_files=None, ignored_speakers=None):
                     )
                 else:
                     # M-AI-Labs have some missing samples, so just print the warning
-                    print("> File %s does not exist!" % (wav_file))
+                    logger.warning("File %s does not exist!", wav_file)
     return items
 
 
@@ -249,7 +256,7 @@ def sam_accenture(root_path, meta_file, **kwargs):  # pylint: disable=unused-arg
         text = item.text
         wav_file = os.path.join(root_path, "vo_voice_quality_transformation", item.get("id") + ".wav")
         if not os.path.exists(wav_file):
-            print(f" [!] {wav_file} in metafile does not exist. Skipping...")
+            logger.warning("%s in metafile does not exist. Skipping...", wav_file)
             continue
         items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
     return items
@@ -370,7 +377,7 @@ def custom_turkish(root_path, meta_file, **kwargs):  # pylint: disable=unused-ar
                 continue
             text = cols[1].strip()
             items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
-    print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
+    logger.warning("%d files skipped. They don't exist...")
     return items
 
 
@@ -438,7 +445,7 @@ def vctk(root_path, meta_files=None, wavs_path="wav48_silence_trimmed", mic="mic
                 {"text": text, "audio_file": wav_file, "speaker_name": "VCTK_" + speaker_id, "root_path": root_path}
             )
         else:
-            print(f" [!] wav files don't exist - {wav_file}")
+            logger.warning("Wav file doesn't exist - %s", wav_file)
     return items
 
 

TTS/tts/layers/bark/hubert/hubert_manager.py

@@ -1,11 +1,14 @@
 # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer
 
+import logging
 import os.path
 import shutil
 import urllib.request
 
 import huggingface_hub
 
+logger = logging.getLogger(__name__)
+
 
 class HubertManager:
     @staticmethod
@@ -13,9 +16,9 @@ class HubertManager:
         download_url: str = "https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt", model_path: str = ""
     ):
         if not os.path.isfile(model_path):
-            print("Downloading HuBERT base model")
+            logger.info("Downloading HuBERT base model")
             urllib.request.urlretrieve(download_url, model_path)
-            print("Downloaded HuBERT")
+            logger.info("Downloaded HuBERT")
             return model_path
         return None
 
@@ -27,9 +30,9 @@ class HubertManager:
     ):
         model_dir = os.path.dirname(model_path)
         if not os.path.isfile(model_path):
-            print("Downloading HuBERT custom tokenizer")
+            logger.info("Downloading HuBERT custom tokenizer")
             huggingface_hub.hf_hub_download(repo, model, local_dir=model_dir, local_dir_use_symlinks=False)
             shutil.move(os.path.join(model_dir, model), model_path)
-            print("Downloaded tokenizer")
+            logger.info("Downloaded tokenizer")
             return model_path
         return None

TTS/tts/layers/bark/hubert/kmeans_hubert.py

@@ -7,8 +7,6 @@ License: MIT
 
 # Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py
 
-import logging
-from pathlib import Path
 
 import torch
 from einops import pack, unpack

TTS/tts/layers/bark/hubert/tokenizer.py

@@ -5,6 +5,7 @@ License: MIT
 """
 
 import json
+import logging
 import os.path
 from zipfile import ZipFile
 
@@ -12,6 +13,8 @@ import numpy
 import torch
 from torch import nn, optim
 
+logger = logging.getLogger(__name__)
+
 
 class HubertTokenizer(nn.Module):
     def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0):
@@ -85,7 +88,7 @@ class HubertTokenizer(nn.Module):
 
         # Print loss
         if log_loss:
-            print("Loss", loss.item())
+            logger.info("Loss %.3f", loss.item())
 
         # Backward pass
         loss.backward()
@@ -157,10 +160,10 @@ def auto_train(data_path, save_path="model.pth", load_model: str = None, save_ep
     data_x, data_y = [], []
 
     if load_model and os.path.isfile(load_model):
-        print("Loading model from", load_model)
+        logger.info("Loading model from %s", load_model)
         model_training = HubertTokenizer.load_from_checkpoint(load_model, "cuda")
     else:
-        print("Creating new model.")
+        logger.info("Creating new model.")
         model_training = HubertTokenizer(version=1).to("cuda")  # Settings for the model to run without lstm
     save_path = os.path.join(data_path, save_path)
     base_save_path = ".".join(save_path.split(".")[:-1])
@@ -191,5 +194,5 @@ def auto_train(data_path, save_path="model.pth", load_model: str = None, save_ep
         save_p_2 = f"{base_save_path}_epoch_{epoch}.pth"
         model_training.save(save_p)
         model_training.save(save_p_2)
-        print(f"Epoch {epoch} completed")
+        logger.info("Epoch %d completed", epoch)
         epoch += 1

TTS/tts/layers/bark/inference_funcs.py

@@ -2,10 +2,11 @@ import logging
 import os
 import re
 from glob import glob
-from typing import Dict, List
+from typing import Dict, List, Optional, Tuple
 
 import librosa
 import numpy as np
+import numpy.typing as npt
 import torch
 import torchaudio
 import tqdm
@@ -48,7 +49,7 @@ def get_voices(extra_voice_dirs: List[str] = []):  # pylint: disable=dangerous-d
     return voices
 
 
-def load_npz(npz_file):
+def load_npz(npz_file: str) -> Tuple[npt.NDArray[np.int64], npt.NDArray[np.int64], npt.NDArray[np.int64]]:
     x_history = np.load(npz_file)
     semantic = x_history["semantic_prompt"]
     coarse = x_history["coarse_prompt"]
@@ -56,7 +57,11 @@ def load_npz(npz_file):
     return semantic, coarse, fine
 
 
-def load_voice(model, voice: str, extra_voice_dirs: List[str] = []):  # pylint: disable=dangerous-default-value
+def load_voice(
+    model, voice: str, extra_voice_dirs: List[str] = []
+) -> Tuple[
+    Optional[npt.NDArray[np.int64]], Optional[npt.NDArray[np.int64]], Optional[npt.NDArray[np.int64]]
+]:  # pylint: disable=dangerous-default-value
     if voice == "random":
         return None, None, None
 
@@ -107,11 +112,10 @@ def generate_voice(
     model,
     output_path,
 ):
-    """Generate a new voice from a given audio and text prompt.
+    """Generate a new voice from a given audio.
 
     Args:
         audio (np.ndarray): The audio to use as a base for the new voice.
-        text (str): Transcription of the audio you are clonning.
         model (BarkModel): The BarkModel to use for generating the new voice.
         output_path (str): The path to save the generated voice to.
     """

TTS/tts/layers/bark/model.py

@@ -2,6 +2,7 @@
 Much of this code is adapted from Andrej Karpathy's NanoGPT
 (https://github.com/karpathy/nanoGPT)
 """
+
 import math
 from dataclasses import dataclass
 

TTS/tts/layers/bark/model_fine.py

@@ -2,6 +2,7 @@
 Much of this code is adapted from Andrej Karpathy's NanoGPT
 (https://github.com/karpathy/nanoGPT)
 """
+
 import math
 from dataclasses import dataclass
 

TTS/tts/layers/delightful_tts/acoustic_model.py

@@ -1,4 +1,5 @@
 ### credit: https://github.com/dunky11/voicesmith
+import logging
 from typing import Callable, Dict, Tuple
 
 import torch
@@ -20,6 +21,8 @@ from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
 from TTS.tts.layers.generic.aligner import AlignmentNetwork
 from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask
 
+logger = logging.getLogger(__name__)
+
 
 class AcousticModel(torch.nn.Module):
     def __init__(
@@ -217,7 +220,7 @@ class AcousticModel(torch.nn.Module):
     def _init_speaker_embedding(self):
         # pylint: disable=attribute-defined-outside-init
         if self.num_speakers > 0:
-            print(" > initialization of speaker-embedding layers.")
+            logger.info("Initialization of speaker-embedding layers.")
             self.embedded_speaker_dim = self.args.speaker_embedding_channels
             self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
 
@@ -362,7 +365,7 @@ class AcousticModel(torch.nn.Module):
 
         pos_encoding = positional_encoding(
             self.emb_dim,
-            max(token_embeddings.shape[1], max(mel_lens)),
+            max(token_embeddings.shape[1], *mel_lens),
             device=token_embeddings.device,
         )
         encoder_outputs = self.encoder(

TTS/tts/layers/glow_tts/glow.py

@@ -1,5 +1,4 @@
 import torch
-from packaging.version import Version
 from torch import nn
 from torch.nn import functional as F
 
@@ -90,10 +89,7 @@ class InvConvNear(nn.Module):
         self.no_jacobian = no_jacobian
         self.weight_inv = None
 
-        if Version(torch.__version__) < Version("1.9"):
+        w_init = torch.linalg.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_(), "complete")[0]
-            w_init = torch.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_())[0]
-        else:
-            w_init = torch.linalg.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_(), "complete")[0]
 
         if torch.det(w_init) < 0:
             w_init[:, 0] = -1 * w_init[:, 0]

TTS/tts/layers/glow_tts/transformer.py

@@ -5,6 +5,7 @@ from torch import nn
 from torch.nn import functional as F
 
 from TTS.tts.layers.generic.normalization import LayerNorm, LayerNorm2
+from TTS.tts.utils.helpers import convert_pad_shape
 
 
 class RelativePositionMultiHeadAttention(nn.Module):
@@ -300,7 +301,7 @@ class FeedForwardNetwork(nn.Module):
         pad_l = self.kernel_size - 1
         pad_r = 0
         padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, self._pad_shape(padding))
+        x = F.pad(x, convert_pad_shape(padding))
         return x
 
     def _same_padding(self, x):
@@ -309,15 +310,9 @@ class FeedForwardNetwork(nn.Module):
         pad_l = (self.kernel_size - 1) // 2
         pad_r = self.kernel_size // 2
         padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, self._pad_shape(padding))
+        x = F.pad(x, convert_pad_shape(padding))
         return x
 
-    @staticmethod
-    def _pad_shape(padding):
-        l = padding[::-1]
-        pad_shape = [item for sublist in l for item in sublist]
-        return pad_shape
-
 
 class RelativePositionTransformer(nn.Module):
     """Transformer with Relative Potional Encoding.

TTS/tts/layers/losses.py

@@ -1,3 +1,4 @@
+import logging
 import math
 
 import numpy as np
@@ -10,6 +11,8 @@ from TTS.tts.utils.helpers import sequence_mask
 from TTS.tts.utils.ssim import SSIMLoss as _SSIMLoss
 from TTS.utils.audio.torch_transforms import TorchSTFT
 
+logger = logging.getLogger(__name__)
+
 
 # pylint: disable=abstract-method
 # relates https://github.com/pytorch/pytorch/issues/42305
@@ -132,11 +135,11 @@ class SSIMLoss(torch.nn.Module):
         ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1))
 
         if ssim_loss.item() > 1.0:
-            print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0")
+            logger.info("SSIM loss is out-of-range (%.2f), setting it to 1.0", ssim_loss.item())
             ssim_loss = torch.tensor(1.0, device=ssim_loss.device)
 
         if ssim_loss.item() < 0.0:
-            print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0")
+            logger.info("SSIM loss is out-of-range (%.2f), setting it to 0.0", ssim_loss.item())
             ssim_loss = torch.tensor(0.0, device=ssim_loss.device)
 
         return ssim_loss
@@ -252,7 +255,7 @@ class GuidedAttentionLoss(torch.nn.Module):
 
     @staticmethod
     def _make_ga_mask(ilen, olen, sigma):
-        grid_x, grid_y = torch.meshgrid(torch.arange(olen).to(olen), torch.arange(ilen).to(ilen))
+        grid_x, grid_y = torch.meshgrid(torch.arange(olen).to(olen), torch.arange(ilen).to(ilen), indexing="ij")
         grid_x, grid_y = grid_x.float(), grid_y.float()
         return 1.0 - torch.exp(-((grid_y / ilen - grid_x / olen) ** 2) / (2 * (sigma**2)))
 

TTS/tts/layers/overflow/common_layers.py

@@ -1,3 +1,4 @@
+import logging
 from typing import List, Tuple
 
 import torch
@@ -8,6 +9,8 @@ from tqdm.auto import tqdm
 from TTS.tts.layers.tacotron.common_layers import Linear
 from TTS.tts.layers.tacotron.tacotron2 import ConvBNBlock
 
+logger = logging.getLogger(__name__)
+
 
 class Encoder(nn.Module):
     r"""Neural HMM Encoder
@@ -213,8 +216,8 @@ class Outputnet(nn.Module):
         original_tensor = std.clone().detach()
         std = torch.clamp(std, min=self.std_floor)
         if torch.any(original_tensor != std):
-            print(
+            logger.info(
-                "[*] Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about"
+                "Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about"
             )
         return std
 

TTS/tts/layers/overflow/neural_hmm.py

@@ -128,7 +128,8 @@ class NeuralHMM(nn.Module):
             # Get mean, std and transition vector from decoder for this timestep
             # Note: Gradient checkpointing currently doesn't works with multiple gpus inside a loop
             if self.use_grad_checkpointing and self.training:
-                mean, std, transition_vector = checkpoint(self.output_net, h_memory, inputs)
+                # TODO: use_reentrant=False is recommended
+                mean, std, transition_vector = checkpoint(self.output_net, h_memory, inputs, use_reentrant=True)
             else:
                 mean, std, transition_vector = self.output_net(h_memory, inputs)
 

TTS/tts/layers/overflow/plotting_utils.py

@@ -71,7 +71,7 @@ def plot_transition_probabilities_to_numpy(states, transition_probabilities, out
     ax.set_title("Transition probability of state")
     ax.set_xlabel("hidden state")
     ax.set_ylabel("probability")
-    ax.set_xticks([i for i in range(len(transition_probabilities))])  # pylint: disable=unnecessary-comprehension
+    ax.set_xticks(list(range(len(transition_probabilities))))
     ax.set_xticklabels([int(x) for x in states], rotation=90)
     plt.tight_layout()
     if not output_fig:

TTS/tts/layers/tacotron/tacotron.py

@@ -1,12 +1,16 @@
 # coding: utf-8
 # adapted from https://github.com/r9y9/tacotron_pytorch
 
+import logging
+
 import torch
 from torch import nn
 
 from .attentions import init_attn
 from .common_layers import Prenet
 
+logger = logging.getLogger(__name__)
+
 
 class BatchNormConv1d(nn.Module):
     r"""A wrapper for Conv1d with BatchNorm. It sets the activation
@@ -480,7 +484,7 @@ class Decoder(nn.Module):
             if t > inputs.shape[1] / 4 and (stop_token > 0.6 or attention[:, -1].item() > 0.6):
                 break
             if t > self.max_decoder_steps:
-                print("   | > Decoder stopped with 'max_decoder_steps")
+                logger.info("Decoder stopped with `max_decoder_steps` %d", self.max_decoder_steps)
                 break
         return self._parse_outputs(outputs, attentions, stop_tokens)
 

TTS/tts/layers/tacotron/tacotron2.py

@@ -1,3 +1,5 @@
+import logging
+
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -5,6 +7,8 @@ from torch.nn import functional as F
 from .attentions import init_attn
 from .common_layers import Linear, Prenet
 
+logger = logging.getLogger(__name__)
+
 
 # pylint: disable=no-value-for-parameter
 # pylint: disable=unexpected-keyword-arg
@@ -356,7 +360,7 @@ class Decoder(nn.Module):
             if stop_token > self.stop_threshold and t > inputs.shape[0] // 2:
                 break
             if len(outputs) == self.max_decoder_steps:
-                print(f"   > Decoder stopped with `max_decoder_steps` {self.max_decoder_steps}")
+                logger.info("Decoder stopped with `max_decoder_steps` %d", self.max_decoder_steps)
                 break
 
             memory = self._update_memory(decoder_output)
@@ -389,7 +393,7 @@ class Decoder(nn.Module):
             if stop_token > 0.7:
                 break
             if len(outputs) == self.max_decoder_steps:
-                print("   | > Decoder stopped with 'max_decoder_steps")
+                logger.info("Decoder stopped with `max_decoder_steps` %d", self.max_decoder_steps)
                 break
 
             self.memory_truncated = decoder_output

TTS/tts/layers/tortoise/arch_utils.py

@@ -1,6 +1,5 @@
 import functools
 import math
-import os
 
 import fsspec
 import torch

TTS/tts/layers/tortoise/audio_utils.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from glob import glob
 from typing import Dict, List
@@ -10,6 +11,8 @@ from scipy.io.wavfile import read
 
 from TTS.utils.audio.torch_transforms import TorchSTFT
 
+logger = logging.getLogger(__name__)
+
 
 def load_wav_to_torch(full_path):
     sampling_rate, data = read(full_path)
@@ -28,7 +31,7 @@ def check_audio(audio, audiopath: str):
     # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
     # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
     if torch.any(audio > 2) or not torch.any(audio < 0):
-        print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
+        logger.error("Error with %s. Max=%.2f min=%.2f", audiopath, audio.max(), audio.min())
     audio.clip_(-1, 1)
 
 
@@ -136,7 +139,7 @@ def load_voices(voices: List[str], extra_voice_dirs: List[str] = []):
     for voice in voices:
         if voice == "random":
             if len(voices) > 1:
-                print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
+                logger.warning("Cannot combine a random voice with a non-random voice. Just using a random voice.")
             return None, None
         clip, latent = load_voice(voice, extra_voice_dirs)
         if latent is None:

TTS/tts/layers/tortoise/clvp.py

@@ -126,7 +126,7 @@ class CLVP(nn.Module):
         text_latents = self.to_text_latent(text_latents)
         speech_latents = self.to_speech_latent(speech_latents)
 
-        text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
+        text_latents, speech_latents = (F.normalize(t, p=2, dim=-1) for t in (text_latents, speech_latents))
 
         temp = self.temperature.exp()
 

TTS/tts/layers/tortoise/diffusion.py

@@ -972,7 +972,7 @@ class GaussianDiffusion:
             assert False  # not currently supported for this type of diffusion.
         elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
             model_outputs = model(x_t, x_start, self._scale_timesteps(t), **model_kwargs)
-            terms.update({k: o for k, o in zip(model_output_keys, model_outputs)})
+            terms.update(dict(zip(model_output_keys, model_outputs)))
             model_output = terms[gd_out_key]
             if self.model_var_type in [
                 ModelVarType.LEARNED,

TTS/tts/layers/tortoise/dpm_solver.py

@@ -1,7 +1,10 @@
+import logging
 import math
 
 import torch
 
+logger = logging.getLogger(__name__)
+
 
 class NoiseScheduleVP:
     def __init__(
@@ -1171,7 +1174,7 @@ class DPM_Solver:
                 lambda_0 - lambda_s,
             )
             nfe += order
-        print("adaptive solver nfe", nfe)
+        logger.debug("adaptive solver nfe %d", nfe)
         return x
 
     def add_noise(self, x, t, noise=None):

TTS/tts/layers/tortoise/transformer.py

@@ -37,7 +37,7 @@ def route_args(router, args, depth):
     for key in matched_keys:
         val = args[key]
         for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])):
-            new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes)
+            new_f_args, new_g_args = (({key: val} if route else {}) for route in routes)
             routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args})
     return routed_args
 
@@ -152,7 +152,7 @@ class Attention(nn.Module):
         softmax = torch.softmax
 
         qkv = self.to_qkv(x).chunk(3, dim=-1)
-        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv)
+        q, k, v = (rearrange(t, "b n (h d) -> b h n d", h=h) for t in qkv)
 
         q = q * self.scale
 

TTS/tts/layers/tortoise/utils.py

@@ -1,8 +1,11 @@
+import logging
 import os
 from urllib import request
 
 from tqdm import tqdm
 
+logger = logging.getLogger(__name__)
+
 DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser("~"), ".cache", "tortoise", "models")
 MODELS_DIR = os.environ.get("TORTOISE_MODELS_DIR", DEFAULT_MODELS_DIR)
 MODELS_DIR = "/data/speech_synth/models/"
@@ -28,10 +31,10 @@ def download_models(specific_models=None):
         model_path = os.path.join(MODELS_DIR, model_name)
         if os.path.exists(model_path):
             continue
-        print(f"Downloading {model_name} from {url}...")
+        logger.info("Downloading %s from %s...", model_name, url)
         with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
             request.urlretrieve(url, model_path, lambda nb, bs, fs, t=t: t.update(nb * bs - t.n))
-        print("Done.")
+        logger.info("Done.")
 
 
 def get_model_path(model_name, models_dir=MODELS_DIR):

TTS/tts/layers/tortoise/xtransformers.py

@@ -84,7 +84,7 @@ def init_zero_(layer):
 
 
 def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
+    values = [d.pop(key) for key in keys]
     return dict(zip(keys, values))
 
 
@@ -107,7 +107,7 @@ def group_by_key_prefix(prefix, d):
 
 def groupby_prefix_and_trim(prefix, d):
     kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix) :], x[1]), tuple(kwargs_with_prefix.items())))
+    kwargs_without_prefix = {x[0][len(prefix) :]: x[1] for x in tuple(kwargs_with_prefix.items())}
     return kwargs_without_prefix, kwargs
 
 
@@ -428,7 +428,7 @@ class ShiftTokens(nn.Module):
         feats_per_shift = x.shape[-1] // segments
         splitted = x.split(feats_per_shift, dim=-1)
         segments_to_shift, rest = splitted[:segments], splitted[segments:]
-        segments_to_shift = list(map(lambda args: shift(*args, mask=mask), zip(segments_to_shift, shifts)))
+        segments_to_shift = [shift(*args, mask=mask) for args in zip(segments_to_shift, shifts)]
         x = torch.cat((*segments_to_shift, *rest), dim=-1)
         return self.fn(x, **kwargs)
 
@@ -635,7 +635,7 @@ class Attention(nn.Module):
         v = self.to_v(v_input)
 
         if not collab_heads:
-            q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+            q, k, v = (rearrange(t, "b n (h d) -> b h n d", h=h) for t in (q, k, v))
         else:
             q = einsum("b i d, h d -> b h i d", q, self.collab_mixing)
             k = rearrange(k, "b n d -> b () n d")
@@ -650,9 +650,9 @@ class Attention(nn.Module):
 
         if exists(rotary_pos_emb) and not has_context:
             l = rotary_pos_emb.shape[-1]
-            (ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k, v))
+            (ql, qr), (kl, kr), (vl, vr) = ((t[..., :l], t[..., l:]) for t in (q, k, v))
-            ql, kl, vl = map(lambda t: apply_rotary_pos_emb(t, rotary_pos_emb), (ql, kl, vl))
+            ql, kl, vl = (apply_rotary_pos_emb(t, rotary_pos_emb) for t in (ql, kl, vl))
-            q, k, v = map(lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr)))
+            q, k, v = (torch.cat(t, dim=-1) for t in ((ql, qr), (kl, kr), (vl, vr)))
 
         input_mask = None
         if any(map(exists, (mask, context_mask))):
@@ -664,7 +664,7 @@ class Attention(nn.Module):
             input_mask = q_mask * k_mask
 
         if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, "h n d -> b h n d", b=b), (self.mem_k, self.mem_v))
+            mem_k, mem_v = (repeat(t, "h n d -> b h n d", b=b) for t in (self.mem_k, self.mem_v))
             k = torch.cat((mem_k, k), dim=-2)
             v = torch.cat((mem_v, v), dim=-2)
             if exists(input_mask):
@@ -964,9 +964,7 @@ class AttentionLayers(nn.Module):
             seq_len = x.shape[1]
             if past_key_values is not None:
                 seq_len += past_key_values[0][0].shape[-2]
-            max_rotary_emb_length = max(
+            max_rotary_emb_length = max([(m.shape[1] if exists(m) else 0) + seq_len for m in mems] + [expected_seq_len])
-                list(map(lambda m: (m.shape[1] if exists(m) else 0) + seq_len, mems)) + [expected_seq_len]
-            )
             rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device)
 
         present_key_values = []
@@ -1200,7 +1198,7 @@ class TransformerWrapper(nn.Module):
 
         res = [out]
         if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            attn_maps = [t.post_softmax_attn for t in intermediates.attn_intermediates]
             res.append(attn_maps)
         if use_cache:
             res.append(intermediates.past_key_values)
@@ -1249,7 +1247,7 @@ class ContinuousTransformerWrapper(nn.Module):
 
         res = [out]
         if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            attn_maps = [t.post_softmax_attn for t in intermediates.attn_intermediates]
             res.append(attn_maps)
         if use_cache:
             res.append(intermediates.past_key_values)

TTS/tts/layers/vits/discriminator.py

@@ -2,7 +2,7 @@ import torch
 from torch import nn
 from torch.nn.modules.conv import Conv1d
 
-from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP, MultiPeriodDiscriminator
+from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP
 
 
 class DiscriminatorS(torch.nn.Module):

TTS/tts/layers/vits/networks.py

@@ -10,22 +10,6 @@ from TTS.tts.utils.helpers import sequence_mask
 LRELU_SLOPE = 0.1
 
 
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
 class TextEncoder(nn.Module):
     def __init__(
         self,