Medix-AI/coqui-tts
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new Benchmark notebook

This commit is contained in:
Eren Golge 2019-08-23 13:07:58 +02:00
parent e02fc51fde
commit a757c6240e
1 changed files with 96 additions and 276 deletions
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notebooks/Benchmark.ipynb
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@ -20,9 +20,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"TTS_PATH = \"/home/erogol/projects/\"\n", "TTS_PATH = \"/home/erogol/projects/\"\n",
@ -33,7 +31,6 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": true,
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [],
@ -45,6 +42,7 @@
"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",
@ -72,23 +70,23 @@
"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'\n", "os.environ['CUDA_VISIBLE_DEVICES']='1'"
"os.environ['OMP_NUM_THREADS']='1'\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"def tts(model, text, CONFIG, use_cuda, ap, use_gl, speaker_id=None, figures=True):\n", "def tts(model, text, CONFIG, use_cuda, ap, use_gl, 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, truncated=False, speaker_id=speaker_id, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\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",
" 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",
@ -106,19 +104,17 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Set constants\n", "# Set constants\n",
"ROOT_PATH = '/media/erogol/data_ssd/Data/models/mozilla_models/4845/'\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/Data/models/wavernn/mozilla/mozilla-May24-4763/model_checkpoints/best_model.pth.tar\"\n", "VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Models/wavernn/universal/4910/best_model_16K.pth.tar\"\n",
"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json\"\n", "VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Models/wavernn/universal/4910/config_16K.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",
@ -126,6 +122,8 @@
"# 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",
@ -136,17 +134,23 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# LOAD TTS MODEL\n", "# LOAD TTS MODEL\n",
"from utils.text.symbols import symbols, phonemes\n", "from utils.text.symbols import symbols, phonemes\n",
"\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",
"# 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, CONFIG)\n", "model = setup_model(num_chars, len(speakers), CONFIG)\n",
"\n", "\n",
"# load the audio processor\n", "# load the audio processor\n",
"ap = AudioProcessor(**CONFIG.audio) \n", "ap = AudioProcessor(**CONFIG.audio) \n",
@ -163,39 +167,45 @@
"if use_cuda:\n", "if use_cuda:\n",
" model.cuda()\n", " model.cuda()\n",
"model.eval()\n", "model.eval()\n",
"print(cp['step'])" "print(cp['step'])\n",
"print(cp['r'])\n",
"\n",
"# set model stepsize \n",
"if 'r' in cp:\n",
" model.decoder.set_r(cp['r'])"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# 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",
"\n", " ap_vocoder = AudioProcessorVocoder(**VOCODER_CONFIG.audio) \n",
" wavernn = Model(\n", " wavernn = Model(\n",
" rnn_dims=512,\n", " rnn_dims=512,\n",
" fc_dims=512,\n", " fc_dims=512,\n",
" mode=\"mold\",\n", " mode=VOCODER_CONFIG.mode,\n",
" pad=2,\n", " mulaw=VOCODER_CONFIG.mulaw,\n",
" upsample_factors=VOCODER_CONFIG.upsample_factors, # set this depending on dataset\n", " pad=VOCODER_CONFIG.pad,\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.hop_length,\n", " hop_length=ap_vocoder.hop_length,\n",
" sample_rate=ap.sample_rate,\n", " sample_rate=ap_vocoder.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'])\n", " wavernn.load_state_dict(check['model'], strict=False)\n",
" if use_cuda:\n", " if use_cuda:\n",
" wavernn.cuda()\n", " wavernn.cuda()\n",
" wavernn.eval();\n", " wavernn.eval();\n",
@ -209,70 +219,75 @@
"### Comparision with https://mycroft.ai/blog/available-voices/" "### Comparision with https://mycroft.ai/blog/available-voices/"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.eval()\n",
"model.decoder.max_decoder_steps = 2000\n",
"speaker_id = 500\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)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": true, "scrolled": true
"scrolled": false
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"model.eval()\n", "model.eval()\n",
"model.decoder.max_decoder_steps = 2000\n", "model.decoder.max_decoder_steps = 2000\n",
"speaker_id = 0\n", "sentence = \"Seine Fuerenden Berater hatten Donald Trump seit Wochen beschworen, berichteten US-Medien: Lassen Sie das mit den Zoellen bleiben.\"\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": null,
"metadata": { "metadata": {
"collapsed": true,
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \n", "sentence = \"Der Klimawandel bedroht die Gletscher im Himalaya.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"The human voice is the most perfect instrument of all.\"\n", "sentence = \"Zwei Unternehmen verlieren einem Medienbericht zufolge ihre Verträge als Maut-Inkasso-Manager.\" # 'echo' is not in training set. \n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n", "sentence = \"Eine Ausländermaut nach dem Geschmack der CSU wird es nicht geben - das bedauert außerhalb der Partei fast niemand.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {
"collapsed": true,
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"This cake is great. It's so delicious and moist.\"\n", "sentence = \"Angela Merkel ist als Klimakanzlerin gestartet.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
@ -285,61 +300,51 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n", "sentence = \"Dann vernachlässigte sie das Thema.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n", "sentence = \"Nun, kurz vor dem Ende, will sie damit noch einmal neu anfangen.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n", "sentence = \"Nun ist der Spieltempel pleite, und manchen Dorfbewohnern fehlt das Geld zum Essen.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n", "sentence = \"Andrea Nahles will in der Fraktion die Vertrauensfrage stellen.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"The buses aren't the problem, they actually provide a solution.\"\n", "sentence=\"Die Erfolge der Grünen bringen eine Reihe Unerfahrener in die Parlamente.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
] ]
}, },
{ {
@ -352,212 +357,27 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sentence = \"Generative adversarial network or variational auto-encoder.\"\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
},
"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
},
"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
},
"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,
"scrolled": false
},
"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,
"scrolled": false
},
"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
},
"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
},
"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
},
"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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sentence = \"Eren, how are you?\"\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": "markdown",
"metadata": {}, "metadata": {},
"source": [
"### Hard Sentences"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sentence = \"Encouraged, he started with a minute a day.\"\n", "sentence=\"Die Luftfahrtbranche arbeitet daran, CO2-neutral zu werden.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "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", "sentence=\"Michael Kretschmer versucht seit Monaten, die Bürger zu umgarnen.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)" "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": null,
"metadata": { "metadata": {},
"collapsed": 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
},
"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,
"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
},
"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
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# !zip benchmark_samples/samples.zip benchmark_samples/*" "# !zip benchmark_samples/samples.zip benchmark_samples/*"
@ -566,9 +386,9 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3(mztts)", "display_name": "Python 3",
"language": "python", "language": "python",
"name": "mztts" "name": "python3"
}, },
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
@ -580,9 +400,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.8" "version": "3.7.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 4
} }