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setup.py update

This commit is contained in:
Eren Golge 2019-09-05 12:54:45 +02:00
parent dc69074a56
commit 8ff17dfab1
3 changed files with 406 additions and 102 deletions
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4
.compute
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@ -10,7 +10,7 @@ wget https://www.dropbox.com/s/wqn5v3wkktw9lmo/install.sh?dl=0 -O install.sh
sudo sh install.sh sudo sh install.sh
python3 setup.py develop python3 setup.py develop
# cp -R ${USER_DIR}/GermanData ../tmp/ # cp -R ${USER_DIR}/GermanData ../tmp/
python3 distribute.py --config_path config.json --data_path /data/ro/shared/data/keithito/LJSpeech-1.1/ # python3 distribute.py --config_path config.json --data_path /data/ro/shared/data/keithito/LJSpeech-1.1/
# cp -R ${USER_DIR}/Mozilla_22050 ../tmp/ # cp -R ${USER_DIR}/Mozilla_22050 ../tmp/
# python3 distribute.py --config_path config_tacotron_gst.json --data_path ../tmp/Mozilla_22050/ # python3 distribute.py --config_path config_tacotron_gst.json --data_path ../tmp/Mozilla_22050/
# while true; do sleep 1000000; done while true; do sleep 1000000; done

501
notebooks/Benchmark.ipynb
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@ -19,7 +19,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -29,11 +29,28 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/miniconda3/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [ "source": [
"%load_ext autoreload\n", "%load_ext autoreload\n",
"%autoreload 2\n", "%autoreload 2\n",
@ -42,7 +59,6 @@
"import io\n", "import io\n",
"import torch \n", "import torch \n",
"import time\n", "import time\n",
"import json\n",
"import numpy as np\n", "import numpy as np\n",
"from collections import OrderedDict\n", "from collections import OrderedDict\n",
"from matplotlib import pylab as plt\n", "from matplotlib import pylab as plt\n",
@ -70,23 +86,21 @@
"from IPython.display import Audio\n", "from IPython.display import Audio\n",
"\n", "\n",
"import os\n", "import os\n",
"os.environ['CUDA_VISIBLE_DEVICES']='1'" "os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
"os.environ['OMP_NUM_THREADS']='1'\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n", "def tts(model, text, CONFIG, use_cuda, ap, use_gl, speaker_id=None, figures=True):\n",
" t_1 = time.time()\n", " t_1 = time.time()\n",
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, False, CONFIG.enable_eos_bos_chars)\n", " waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, truncated=False, speaker_id=speaker_id, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n",
" if CONFIG.model == \"Tacotron\" and not use_gl:\n", " if CONFIG.model == \"Tacotron\" and not use_gl:\n",
" # coorect the normalization differences b/w TTS and the Vocoder.\n",
" mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n", " mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
" mel_postnet_spec = ap._denormalize(mel_postnet_spec)\n",
" mel_postnet_spec = ap_vocoder._normalize(mel_postnet_spec)\n",
" if not use_gl:\n", " if not use_gl:\n",
" waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=11000, overlap=550)\n", " waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=11000, overlap=550)\n",
"\n", "\n",
@ -103,18 +117,31 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-3306702a6bbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mVOCODER_MODEL_PATH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/model_checkpoints/best_model.pth.tar\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mVOCODER_CONFIG_PATH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mVOCODER_CONFIG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVOCODER_CONFIG_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0muse_cuda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/projects/TTS/tts_namespace/TTS/utils/generic_utils.py\u001b[0m in \u001b[0;36mload_config\u001b[0;34m(config_path)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAttrDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0minput_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0minput_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'\\\\\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json'"
]
}
],
"source": [ "source": [
"# Set constants\n", "# Set constants\n",
"ROOT_PATH = '/media/erogol/data_ssd/Models/libri_tts/5049/'\n", "ROOT_PATH = '/media/erogol/data_ssd/Models/libri_tts/5049/'\n",
"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n", "MODEL_PATH = ROOT_PATH + 'best_model.pth.tar'\n",
"CONFIG_PATH = ROOT_PATH + '/config.json'\n", "CONFIG_PATH = ROOT_PATH + '/config.json'\n",
"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n", "OUT_FOLDER = \"/home/erogol/Dropbox/AudioSamples/benchmark_samples/\"\n",
"CONFIG = load_config(CONFIG_PATH)\n", "CONFIG = load_config(CONFIG_PATH)\n",
"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Models/wavernn/universal/4910/best_model_16K.pth.tar\"\n", "VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/model_checkpoints/best_model.pth.tar\"\n",
"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Models/wavernn/universal/4910/config_16K.json\"\n", "VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json\"\n",
"VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n", "VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
"use_cuda = False\n", "use_cuda = False\n",
"\n", "\n",
@ -122,8 +149,6 @@
"# CONFIG.windowing = False\n", "# CONFIG.windowing = False\n",
"# CONFIG.prenet_dropout = False\n", "# CONFIG.prenet_dropout = False\n",
"# CONFIG.separate_stopnet = True\n", "# CONFIG.separate_stopnet = True\n",
"# CONFIG.use_forward_attn = True\n",
"# CONFIG.forward_attn_mask = True\n",
"# CONFIG.stopnet = True\n", "# CONFIG.stopnet = True\n",
"\n", "\n",
"# Set the vocoder\n", "# Set the vocoder\n",
@ -138,19 +163,11 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# LOAD TTS MODEL\n", "# LOAD TTS MODEL\n",
"from TTS.utils.text.symbols import symbols, phonemes\n", "from utils.text.symbols import symbols, phonemes\n",
"\n",
"# multi speaker \n",
"if CONFIG.use_speaker_embedding:\n",
" speakers = json.load(open(f\"{ROOT_PATH}/speakers.json\", 'r'))\n",
" speakers_idx_to_id = {v: k for k, v in speakers.items()}\n",
"else:\n",
" speakers = []\n",
" speaker_id = None\n",
"\n", "\n",
"# load the model\n", "# load the model\n",
"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n", "num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
"model = setup_model(num_chars, len(speakers), CONFIG)\n", "model = setup_model(num_chars, CONFIG)\n",
"\n", "\n",
"# load the audio processor\n", "# load the audio processor\n",
"ap = AudioProcessor(**CONFIG.audio) \n", "ap = AudioProcessor(**CONFIG.audio) \n",
@ -167,12 +184,7 @@
"if use_cuda:\n", "if use_cuda:\n",
" model.cuda()\n", " model.cuda()\n",
"model.eval()\n", "model.eval()\n",
"print(cp['step'])\n", "print(cp['step'])"
"print(cp['r'])\n",
"\n",
"# set model stepsize \n",
"if 'r' in cp:\n",
" model.decoder.set_r(cp['r'])"
] ]
}, },
{ {
@ -184,28 +196,25 @@
"# LOAD WAVERNN\n", "# LOAD WAVERNN\n",
"if use_gl == False:\n", "if use_gl == False:\n",
" from WaveRNN.models.wavernn import Model\n", " from WaveRNN.models.wavernn import Model\n",
" from WaveRNN.utils.audio import AudioProcessor as AudioProcessorVocoder\n",
" bits = 10\n", " bits = 10\n",
" ap_vocoder = AudioProcessorVocoder(**VOCODER_CONFIG.audio) \n", "\n",
" wavernn = Model(\n", " wavernn = Model(\n",
" rnn_dims=512,\n", " rnn_dims=512,\n",
" fc_dims=512,\n", " fc_dims=512,\n",
" mode=VOCODER_CONFIG.mode,\n", " mode=\"mold\",\n",
" mulaw=VOCODER_CONFIG.mulaw,\n", " pad=2,\n",
" pad=VOCODER_CONFIG.pad,\n", " upsample_factors=VOCODER_CONFIG.upsample_factors, # set this depending on dataset\n",
" upsample_factors=VOCODER_CONFIG.upsample_factors,\n",
" feat_dims=VOCODER_CONFIG.audio[\"num_mels\"],\n", " feat_dims=VOCODER_CONFIG.audio[\"num_mels\"],\n",
" compute_dims=128,\n", " compute_dims=128,\n",
" res_out_dims=128,\n", " res_out_dims=128,\n",
" res_blocks=10,\n", " res_blocks=10,\n",
" hop_length=ap_vocoder.hop_length,\n", " hop_length=ap.hop_length,\n",
" sample_rate=ap_vocoder.sample_rate,\n", " sample_rate=ap.sample_rate,\n",
" use_upsample_net = True,\n",
" use_aux_net = True\n",
" ).cuda()\n", " ).cuda()\n",
"\n", "\n",
"\n",
" check = torch.load(VOCODER_MODEL_PATH)\n", " check = torch.load(VOCODER_MODEL_PATH)\n",
" wavernn.load_state_dict(check['model'], strict=False)\n", " wavernn.load_state_dict(check['model'])\n",
" if use_cuda:\n", " if use_cuda:\n",
" wavernn.cuda()\n", " wavernn.cuda()\n",
" wavernn.eval();\n", " wavernn.eval();\n",
@ -221,73 +230,111 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-e285d5bde9fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_decoder_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mspeaker_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Bill got in the habit of asking himself “Is that thought true?” And if he wasn’t absolutely certain it was, he just let it go.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_gl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_gl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [ "source": [
"model.eval()\n", "model.eval()\n",
"model.decoder.max_decoder_steps = 2000\n", "model.decoder.max_decoder_steps = 2000\n",
"speaker_id = 500\n", "speaker_id = 0\n",
"sentence = \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n", "sentence = \"Bill got in the habit of asking himself “Is that thought true?” And if he wasn’t absolutely certain it was, he just let it go.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-621056ffa667>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Be a voice, not an echo.\"\u001b[0m \u001b[0;31m# 'echo' is not in training set.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_gl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_gl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [ "source": [
"model.eval()\n", "sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \n",
"model.decoder.max_decoder_steps = 2000\n", "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
"sentence = \"Seine Fuerenden Berater hatten Donald Trump seit Wochen beschworen, berichteten US-Medien: Lassen Sie das mit den Zoellen bleiben.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"sentence = \"Der Klimawandel bedroht die Gletscher im Himalaya.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-26967668a1a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"The human voice is the most perfect instrument of all.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_gl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_gl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [ "source": [
"sentence = \"Zwei Unternehmen verlieren einem Medienbericht zufolge ihre Verträge als Maut-Inkasso-Manager.\" # 'echo' is not in training set. \n", "sentence = \"The human voice is the most perfect instrument of all.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-28cb5023e353>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"I'm sorry Dave. I'm afraid I can't do that.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_gl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_gl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [ "source": [
"sentence = \"Eine Ausländermaut nach dem Geschmack der CSU wird es nicht geben - das bedauert außerhalb der Partei fast niemand.\"\n", "sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Angela Merkel ist als Klimakanzlerin gestartet.\"\n", "sentence = \"This cake is great. It's so delicious and moist.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
@ -300,51 +347,76 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Dann vernachlässigte sie das Thema.\"\n", "sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Nun, kurz vor dem Ende, will sie damit noch einmal neu anfangen.\"\n", "sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Nun ist der Spieltempel pleite, und manchen Dorfbewohnern fehlt das Geld zum Essen.\"\n", "sentence = \"Here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Andrea Nahles will in der Fraktion die Vertrauensfrage stellen.\"\n", "sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence=\"Die Erfolge der Grünen bringen eine Reihe Unerfahrener in die Parlamente.\"\n", "sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
@ -357,11 +429,136 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence=\"Die Luftfahrtbranche arbeitet daran, CO2-neutral zu werden.\"\n", "sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"Basilar membrane and otolaryngology are not auto-correlations.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \" He has read the whole thing.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"He reads books.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"Thisss isrealy awhsome.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"This is your internet browser, Firefox.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"This is your internet browser Firefox.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"The quick brown fox jumps over the lazy dog.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"Does the quick brown fox jump over the lazy dog?\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
@ -370,14 +567,118 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence=\"Michael Kretschmer versucht seit Monaten, die Bürger zu umgarnen.\"\n", "sentence = \"Eren, how are you?\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hard Sentences"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"Encouraged, he started with a minute a day.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"His meditation consisted of “body scanning” which involved focusing his mind and energy on each section of the body from head to toe .\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning . \"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"sentence = \"If he decided to watch TV he really watched it.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"scrolled": true
},
"outputs": [],
"source": [
"sentence = \"Often we try to bring about change through sheer effort and we put all of our energy into a new initiative .\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# for twb dataset\n",
"sentence = \"In our preparation for Easter, God in his providence offers us each year the season of Lent as a sacramental sign of our conversion.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# !zip benchmark_samples/samples.zip benchmark_samples/*" "# !zip benchmark_samples/samples.zip benchmark_samples/*"

3
setup.py
View File

@ -90,4 +90,7 @@ setup(
"soundfile", "soundfile",
"phonemizer @ https://github.com/bootphon/phonemizer/tarball/master", "phonemizer @ https://github.com/bootphon/phonemizer/tarball/master",
], ],
dependency_links=[
"http://github.com/bootphon/phonemizer/tarball/master#egg=phonemizer-1.0.1"
]
) )