mirror of https://github.com/coqui-ai/TTS.git
update benchmark notebook
This commit is contained in:
Eren Golge
parent
5b6b1f354d
commit
113f5860b8
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@ -19,7 +19,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -29,28 +29,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/erogol/miniconda3/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n",
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" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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@ -59,6 +42,7 @@
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"import io\n",
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"import torch \n",
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"import time\n",
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"import json\n",
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"import numpy as np\n",
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"from collections import OrderedDict\n",
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"from matplotlib import pylab as plt\n",
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@ -86,23 +70,25 @@
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"from IPython.display import Audio\n",
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"\n",
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
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"os.environ['OMP_NUM_THREADS']='1'\n"
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"os.environ['CUDA_VISIBLE_DEVICES']='1'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tts(model, text, CONFIG, use_cuda, ap, use_gl, speaker_id=None, figures=True):\n",
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"def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
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" t_1 = time.time()\n",
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" 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",
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" 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",
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" if CONFIG.model == \"Tacotron\" and not use_gl:\n",
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" # coorect the normalization differences b/w TTS and the Vocoder.\n",
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" mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
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" mel_postnet_spec = ap._denormalize(mel_postnet_spec)\n",
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" mel_postnet_spec = ap_vocoder._normalize(mel_postnet_spec)\n",
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" if not use_gl:\n",
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" waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=11000, overlap=550)\n",
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" waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=8000, overlap=400)\n",
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"\n",
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" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
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" if figures: \n",
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@ -117,31 +103,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: '/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"\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",
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"\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",
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"\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'"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Set constants\n",
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"ROOT_PATH = '/media/erogol/data_ssd/Models/libri_tts/5049/'\n",
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"MODEL_PATH = ROOT_PATH + 'best_model.pth.tar'\n",
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"ROOT_PATH = '/media/erogol/data_ssd/Models/libri_tts/5099/'\n",
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"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
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"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
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"OUT_FOLDER = \"/home/erogol/Dropbox/AudioSamples/benchmark_samples/\"\n",
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"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
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"CONFIG = load_config(CONFIG_PATH)\n",
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"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/model_checkpoints/best_model.pth.tar\"\n",
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"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json\"\n",
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"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Models/wavernn/ljspeech/mold_ljspeech_best_model/checkpoint_433000.pth.tar\"\n",
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"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Models/wavernn/ljspeech/mold_ljspeech_best_model/config.json\"\n",
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"VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
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"use_cuda = False\n",
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"\n",
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@ -149,10 +122,12 @@
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"# CONFIG.windowing = False\n",
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"# CONFIG.prenet_dropout = False\n",
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"# CONFIG.separate_stopnet = True\n",
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"CONFIG.use_forward_attn = True\n",
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"# CONFIG.forward_attn_mask = True\n",
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"# CONFIG.stopnet = True\n",
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"\n",
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"# Set the vocoder\n",
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"use_gl = True # use GL if True\n",
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"use_gl = False # use GL if True\n",
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"batched_wavernn = True # use batched wavernn inference if True"
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]
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},
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@ -165,9 +140,17 @@
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"# LOAD TTS MODEL\n",
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"from utils.text.symbols import symbols, phonemes\n",
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"\n",
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"# multi speaker \n",
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"if CONFIG.use_speaker_embedding:\n",
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" speakers = json.load(open(f\"{ROOT_PATH}/speakers.json\", 'r'))\n",
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" speakers_idx_to_id = {v: k for k, v in speakers.items()}\n",
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"else:\n",
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" speakers = []\n",
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" speaker_id = None\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
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"model = setup_model(num_chars, CONFIG)\n",
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"model = setup_model(num_chars, len(speakers), CONFIG)\n",
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"\n",
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"# load the audio processor\n",
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"ap = AudioProcessor(**CONFIG.audio) \n",
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@ -184,7 +167,12 @@
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"if use_cuda:\n",
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" model.cuda()\n",
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"model.eval()\n",
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"print(cp['step'])"
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"print(cp['step'])\n",
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"print(cp['r'])\n",
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"\n",
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"# set model stepsize\n",
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"if 'r' in cp:\n",
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" model.decoder.set_r(cp['r'])"
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]
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},
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{
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@ -196,25 +184,28 @@
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"# LOAD WAVERNN\n",
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"if use_gl == False:\n",
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" from WaveRNN.models.wavernn import Model\n",
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" from WaveRNN.utils.audio import AudioProcessor as AudioProcessorVocoder\n",
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" bits = 10\n",
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"\n",
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" ap_vocoder = AudioProcessorVocoder(**VOCODER_CONFIG.audio) \n",
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" wavernn = Model(\n",
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" rnn_dims=512,\n",
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" fc_dims=512,\n",
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" mode=\"mold\",\n",
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" pad=2,\n",
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" upsample_factors=VOCODER_CONFIG.upsample_factors, # set this depending on dataset\n",
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" mode=VOCODER_CONFIG.mode,\n",
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" mulaw=VOCODER_CONFIG.mulaw,\n",
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" pad=VOCODER_CONFIG.pad,\n",
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" upsample_factors=VOCODER_CONFIG.upsample_factors,\n",
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" feat_dims=VOCODER_CONFIG.audio[\"num_mels\"],\n",
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" compute_dims=128,\n",
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" res_out_dims=128,\n",
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" res_blocks=10,\n",
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" hop_length=ap.hop_length,\n",
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" sample_rate=ap.sample_rate,\n",
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" hop_length=ap_vocoder.hop_length,\n",
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" sample_rate=ap_vocoder.sample_rate,\n",
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" use_upsample_net = True,\n",
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" use_aux_net = True\n",
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" ).cuda()\n",
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"\n",
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"\n",
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" check = torch.load(VOCODER_MODEL_PATH)\n",
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" wavernn.load_state_dict(check['model'])\n",
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" wavernn.load_state_dict(check['model'], strict=False)\n",
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" if use_cuda:\n",
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" wavernn.cuda()\n",
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" wavernn.eval();\n",
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@ -230,111 +221,67 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'model' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\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",
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"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
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]
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}
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],
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"outputs": [],
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"source": [
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"model.eval()\n",
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"model.decoder.max_decoder_steps = 2000\n",
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"speaker_id = 0\n",
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"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",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'model' is not defined",
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"output_type": "error",
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"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": [
|
||||
"sentence = \"Be a voice, not an echo.\" # '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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"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": [
|
||||
"sentence = \"The human voice is the most perfect instrument of all.\"\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": 8,
|
||||
"metadata": {},
|
||||
"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": [
|
||||
"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, speaker_id=speaker_id, use_gl=use_gl, figures=True)"
|
||||
"speaker_id = None\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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.eval()\n",
|
||||
"model.decoder.max_decoder_steps = 2000\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",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \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": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"This cake is great. It's so delicious and moist.\"\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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -347,76 +294,51 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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, 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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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, 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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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, 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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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, 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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -429,136 +351,91 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -568,7 +445,7 @@
|
|||
"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)"
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -581,107 +458,62 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"metadata": {},
|
||||
"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)"
|
||||
"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": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"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": [],
|
||||
"source": [
|
||||
"# !zip benchmark_samples/samples.zip benchmark_samples/*"
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
Loading…
Reference in New Issue