mirror of https://github.com/coqui-ai/TTS.git
589 lines
18 KiB
Plaintext
589 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is to test TTS models with benchmark sentences for speech synthesis.\n",
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"\n",
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"Before running this script please DON'T FORGET: \n",
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"- to set file paths.\n",
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"- to download related model files from TTS and WaveRNN.\n",
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"- to checkout right commit versions (given next to the model) of TTS and WaveRNN.\n",
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"- to set the right paths in the cell below.\n",
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"\n",
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"Repositories:\n",
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"- TTS: https://github.com/mozilla/TTS\n",
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"- WaveRNN: https://github.com/erogol/WaveRNN"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"TTS_PATH = \"/home/erogol/projects/\"\n",
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"WAVERNN_PATH =\"/home/erogol/projects/\""
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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": null,
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"metadata": {
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"collapsed": true,
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"scrolled": true
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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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"import os\n",
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"import sys\n",
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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 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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"\n",
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"%pylab inline\n",
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"rcParams[\"figure.figsize\"] = (16,5)\n",
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"\n",
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"# add libraries into environment\n",
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"sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n",
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"sys.path.append(WAVERNN_PATH) # set this if TTS is not installed globally\n",
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"\n",
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"import librosa\n",
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"import librosa.display\n",
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"\n",
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"from TTS.models.tacotron import Tacotron \n",
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"from TTS.layers import *\n",
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"from TTS.utils.data import *\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.generic_utils import load_config, setup_model\n",
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"from TTS.utils.text import text_to_sequence\n",
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"from TTS.utils.synthesis import synthesis\n",
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"from TTS.utils.visual import visualize\n",
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"\n",
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"import IPython\n",
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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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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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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" 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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" if CONFIG.model == \"Tacotron\" and not use_gl:\n",
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" mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\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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"\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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" visualize(alignment, mel_postnet_spec, stop_tokens, text, ap.hop_length, CONFIG, mel_spec) \n",
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" IPython.display.display(Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n",
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" os.makedirs(OUT_FOLDER, exist_ok=True)\n",
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" file_name = text.replace(\" \", \"_\").replace(\".\",\"\") + \".wav\"\n",
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" out_path = os.path.join(OUT_FOLDER, file_name)\n",
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" ap.save_wav(waveform, out_path)\n",
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" return alignment, mel_postnet_spec, stop_tokens, waveform"
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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": null,
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"metadata": {
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"collapsed": true
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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/Data/models/mozilla_models/4845/'\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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"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_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
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"use_cuda = False\n",
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"\n",
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"# Set some config fields manually for testing\n",
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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.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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"batched_wavernn = True # use batched wavernn inference if 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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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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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"# 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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"\n",
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"# load the audio processor\n",
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"ap = AudioProcessor(**CONFIG.audio) \n",
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"\n",
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"\n",
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"# load model state\n",
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"if use_cuda:\n",
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" cp = torch.load(MODEL_PATH)\n",
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"else:\n",
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" cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
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"\n",
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"# load the model\n",
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"model.load_state_dict(cp['model'])\n",
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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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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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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" bits = 10\n",
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"\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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" 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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" ).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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" if use_cuda:\n",
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" wavernn.cuda()\n",
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" wavernn.eval();\n",
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" print(check['step'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Comparision with https://mycroft.ai/blog/available-voices/"
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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": null,
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"metadata": {
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"collapsed": true,
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"scrolled": false
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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": null,
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"metadata": {
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"collapsed": true,
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"The human voice is the most perfect instrument of all.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\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": null,
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"metadata": {
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"collapsed": true,
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"sentence = \"This cake is great. It's so delicious and moist.\"\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": "markdown",
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"metadata": {},
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"source": [
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"### Comparison with https://keithito.github.io/audio-samples/"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Generative adversarial network or variational auto-encoder.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"The buses aren't the problem, they actually provide a solution.\"\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": "markdown",
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"metadata": {},
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"source": [
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"### Comparison with https://google.github.io/tacotron/publications/tacotron/index.html"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Generative adversarial network or variational auto-encoder.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Basilar membrane and otolaryngology are not auto-correlations.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \" He has read the whole thing.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"He reads books.\"\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": null,
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"metadata": {
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"collapsed": true,
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"sentence = \"Thisss isrealy awhsome.\"\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": null,
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"metadata": {
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"collapsed": true,
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"sentence = \"This is your internet browser, Firefox.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"This is your internet browser Firefox.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"The quick brown fox jumps over the lazy dog.\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Does the quick brown fox jump over the lazy dog?\"\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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentence = \"Eren, how are you?\"\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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Hard Sentences"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"collapsed": 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
|
||
},
|
||
"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
|
||
},
|
||
"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": [],
|
||
"source": [
|
||
"# !zip benchmark_samples/samples.zip benchmark_samples/*"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3(mztts)",
|
||
"language": "python",
|
||
"name": "mztts"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.8"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|