mirror of https://github.com/coqui-ai/TTS.git
bug fixes and benchmark notebook update
This commit is contained in:
erogol
parent
ed67cadf98
commit
4a6949632b
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@ -1,585 +0,0 @@
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{
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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 PWGAN.\n",
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"- download or clone related repos, linked below.\n",
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"- setup the repositories. ```python setup.py install```\n",
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"- to checkout right commit versions (given next to the model) of TTS and PWGAN.\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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"- PWGAN: https://github.com/erogol/ParallelWaveGAN"
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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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"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 json\n",
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"import yaml\n",
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"import numpy as np\n",
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"from collections import OrderedDict\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams[\"figure.figsize\"] = (16,5)\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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"\n",
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"# you may need to change this depending on your system\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
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"%matplotlib inline"
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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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"outputs": [],
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"source": [
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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, 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 = np.pad(mel_postnet_spec, pad_width=((2, 2), (0, 0)))\n",
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" print(mel_postnet_spec.shape)\n",
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" print(\"max- \", mel_postnet_spec.max(), \" -- min- \", mel_postnet_spec.min())\n",
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" if not use_gl:\n",
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" waveform = vocoder_model.inference(torch.FloatTensor(ap_vocoder._normalize(mel_postnet_spec).T).unsqueeze(0), hop_size=ap_vocoder.hop_length)\n",
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"# waveform = waveform / abs(waveform).max() * 0.9\n",
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" if use_cuda:\n",
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" waveform = waveform.cpu()\n",
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" waveform = waveform.numpy()\n",
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" rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
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" print(waveform.shape)\n",
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" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
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" print(\" > Real-time factor: {}\".format(rtf))\n",
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" if figures: \n",
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" visualize(alignment, mel_postnet_spec, stop_tokens, text, ap.hop_length, CONFIG, ap._denormalize(mel_spec)) \n",
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" IPython.display.display(Audio(waveform, rate=CONFIG.audio['sample_rate'], normalize=False)) \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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"outputs": [],
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"source": [
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"# Set constants\n",
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"ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-bn-December-23-2019_08+34AM-ffea133/'\n",
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"MODEL_PATH = ROOT_PATH + '/checkpoint_670000.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 = \"/home/erogol/Models/LJSpeech/pwgan-ljspeech/checkpoint-400000steps.pkl\"\n",
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"VOCODER_CONFIG_PATH = \"/home/erogol/Models/LJSpeech/pwgan-ljspeech/config.yml\"\n",
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"\n",
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"# load PWGAN config\n",
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"with open(VOCODER_CONFIG_PATH) as f:\n",
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" VOCODER_CONFIG = yaml.load(f, Loader=yaml.Loader)\n",
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" \n",
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"# Run FLAGs\n",
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"use_cuda = False\n",
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"# Set some config fields manually for testing\n",
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"CONFIG.windowing = True\n",
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"CONFIG.use_forward_attn = True \n",
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"# Set the vocoder\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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{
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"cell_type": "code",
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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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"# LOAD TTS MODEL\n",
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"from TTS.utils.text.symbols import make_symbols, 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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"# if the vocabulary was passed, replace the default\n",
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"if 'characters' in CONFIG.keys():\n",
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" symbols, phonemes = make_symbols(**CONFIG.characters)\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, 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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"\n",
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"\n",
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"# load model state\n",
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"cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))\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'])\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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"cell_type": "code",
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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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"# LOAD WAVERNN\n",
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"if use_gl == False:\n",
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" from parallel_wavegan.models import ParallelWaveGANGenerator\n",
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" from parallel_wavegan.utils.audio import AudioProcessor as AudioProcessorVocoder\n",
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" \n",
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" vocoder_model = ParallelWaveGANGenerator(**VOCODER_CONFIG[\"generator_params\"])\n",
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" vocoder_model.load_state_dict(torch.load(VOCODER_MODEL_PATH, map_location=\"cpu\")[\"model\"][\"generator\"])\n",
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" vocoder_model.remove_weight_norm()\n",
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" ap_vocoder = AudioProcessorVocoder(**VOCODER_CONFIG['audio']) \n",
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" if use_cuda:\n",
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" vocoder_model.cuda()\n",
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" vocoder_model.eval();"
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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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"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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"model.decoder.prenet.eval()\n",
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"speaker_id = None\n",
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"sentence = '''A breeding jennet, lusty, young, and proud,'''\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, 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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"outputs": [],
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"source": [
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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, 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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"### https://espnet.github.io/icassp2020-tts/"
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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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"outputs": [],
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"source": [
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"sentence = \"The Commission also recommends\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, 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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"outputs": [],
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"source": [
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"sentence = \"As a result of these studies, the planning document submitted by the Secretary of the Treasury to the Bureau of the Budget on August thirty-one.\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, 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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"outputs": [],
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"source": [
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"sentence = \"The FBI now transmits information on all defectors, a category which would, of course, have included Oswald.\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, 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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"outputs": [],
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"source": [
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"sentence = \"they seem unduly restrictive in continuing to require some manifestation of animus against a Government official.\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, 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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"outputs": [],
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"source": [
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"sentence = \"and each agency given clear understanding of the assistance which the Secret Service expects.\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, 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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"### Other examples"
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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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"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, 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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"outputs": [],
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"source": [
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"sentence = \" He has read the whole thing.\"\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 = \"He reads books.\"\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 = \"Thisss isrealy awhsome.\"\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 is your internet browser, Firefox.\"\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 is your internet browser Firefox.\"\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 quick brown fox jumps over the lazy dog.\"\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 = \"Does the quick brown fox jump over the lazy dog?\"\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 = \"Eren, how are you?\"\n",
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Hard Sentences"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
|
@ -1,546 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is to test TTS models with benchmark sentences for speech synthesis.\n",
|
||||
"\n",
|
||||
"Before running this script please DON'T FORGET: \n",
|
||||
"- to set file paths.\n",
|
||||
"- to download related model files from TTS and WaveRNN.\n",
|
||||
"- to checkout right commit versions (given next to the model) of TTS and WaveRNN.\n",
|
||||
"- to set the right paths in the cell below.\n",
|
||||
"\n",
|
||||
"Repositories:\n",
|
||||
"- TTS: https://github.com/mozilla/TTS\n",
|
||||
"- WaveRNN: https://github.com/erogol/WaveRNN"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"TTS_PATH = \"/home/erogol/projects/\"\n",
|
||||
"WAVERNN_PATH =\"/home/erogol/projects/\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"import sys\n",
|
||||
"import io\n",
|
||||
"import torch \n",
|
||||
"import time\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"from collections import OrderedDict\n",
|
||||
"from matplotlib import pylab as plt\n",
|
||||
"\n",
|
||||
"%pylab inline\n",
|
||||
"rcParams[\"figure.figsize\"] = (16,5)\n",
|
||||
"\n",
|
||||
"# add libraries into environment\n",
|
||||
"sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n",
|
||||
"sys.path.append(WAVERNN_PATH) # set this if TTS is not installed globally\n",
|
||||
"\n",
|
||||
"import librosa\n",
|
||||
"import librosa.display\n",
|
||||
"\n",
|
||||
"from TTS.models.tacotron import Tacotron \n",
|
||||
"from TTS.layers import *\n",
|
||||
"from TTS.utils.data import *\n",
|
||||
"from TTS.utils.audio import AudioProcessor\n",
|
||||
"from TTS.utils.generic_utils import load_config, setup_model\n",
|
||||
"from TTS.utils.text import text_to_sequence\n",
|
||||
"from TTS.utils.synthesis import synthesis\n",
|
||||
"from TTS.utils.visual import visualize\n",
|
||||
"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
|
||||
"\n",
|
||||
"import IPython\n",
|
||||
"from IPython.display import Audio\n",
|
||||
"\n",
|
||||
"import os\n",
|
||||
"os.environ['CUDA_VISIBLE_DEVICES']='1'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
|
||||
" t_1 = time.time()\n",
|
||||
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None, \n",
|
||||
" truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars,\n",
|
||||
" use_griffin_lim=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",
|
||||
" if not use_gl:\n",
|
||||
" mel_postnet_spec = ap._denormalize(mel_postnet_spec)\n",
|
||||
" mel_postnet_spec = ap_vocoder._normalize(mel_postnet_spec)\n",
|
||||
" waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=8000, overlap=400)\n",
|
||||
"\n",
|
||||
" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
|
||||
" if figures: \n",
|
||||
" visualize(alignment, mel_postnet_spec, stop_tokens, text, ap.hop_length, CONFIG, mel_spec) \n",
|
||||
" IPython.display.display(Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n",
|
||||
" os.makedirs(OUT_FOLDER, exist_ok=True)\n",
|
||||
" file_name = text.replace(\" \", \"_\").replace(\".\",\"\") + \".wav\"\n",
|
||||
" out_path = os.path.join(OUT_FOLDER, file_name)\n",
|
||||
" ap.save_wav(waveform, out_path)\n",
|
||||
" return alignment, mel_postnet_spec, stop_tokens, waveform"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set constants\n",
|
||||
"ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-bn-December-23-2019_08+34AM-ffea133/'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
|
||||
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
|
||||
"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
|
||||
"CONFIG = load_config(CONFIG_PATH)\n",
|
||||
"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Models/wavernn/ljspeech/mold_ljspeech_best_model/checkpoint_433000.pth.tar\"\n",
|
||||
"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Models/wavernn/ljspeech/mold_ljspeech_best_model/config.json\"\n",
|
||||
"VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
|
||||
"use_cuda = True\n",
|
||||
"\n",
|
||||
"# Set some config fields manually for testing\n",
|
||||
"# CONFIG.windowing = False\n",
|
||||
"# CONFIG.prenet_dropout = False\n",
|
||||
"# CONFIG.separate_stopnet = True\n",
|
||||
"CONFIG.use_forward_attn = True\n",
|
||||
"# CONFIG.forward_attn_mask = True\n",
|
||||
"# CONFIG.stopnet = True\n",
|
||||
"\n",
|
||||
"# Set the vocoder\n",
|
||||
"use_gl = True # use GL if True\n",
|
||||
"batched_wavernn = True # use batched wavernn inference if True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# LOAD TTS MODEL\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",
|
||||
"# if the vocabulary was passed, replace the default\n",
|
||||
"if 'characters' in CONFIG.keys():\n",
|
||||
" symbols, phonemes = make_symbols(**CONFIG.characters)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
|
||||
"model = setup_model(num_chars, len(speakers), CONFIG)\n",
|
||||
"\n",
|
||||
"# load the audio processor\n",
|
||||
"ap = AudioProcessor(**CONFIG.audio) \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# load model state\n",
|
||||
"if use_cuda:\n",
|
||||
" cp = torch.load(MODEL_PATH)\n",
|
||||
"else:\n",
|
||||
" cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"model.load_state_dict(cp['model'])\n",
|
||||
"if use_cuda:\n",
|
||||
" model.cuda()\n",
|
||||
"model.eval()\n",
|
||||
"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",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# LOAD WAVERNN - Make sure you downloaded the model and installed the module\n",
|
||||
"if use_gl == False:\n",
|
||||
" from WaveRNN.models.wavernn import Model\n",
|
||||
" from WaveRNN.utils.audio import AudioProcessor as AudioProcessorVocoder\n",
|
||||
" bits = 10\n",
|
||||
" ap_vocoder = AudioProcessorVocoder(**VOCODER_CONFIG.audio) \n",
|
||||
" wavernn = Model(\n",
|
||||
" rnn_dims=512,\n",
|
||||
" fc_dims=512,\n",
|
||||
" mode=VOCODER_CONFIG.mode,\n",
|
||||
" mulaw=VOCODER_CONFIG.mulaw,\n",
|
||||
" pad=VOCODER_CONFIG.pad,\n",
|
||||
" upsample_factors=VOCODER_CONFIG.upsample_factors,\n",
|
||||
" feat_dims=VOCODER_CONFIG.audio[\"num_mels\"],\n",
|
||||
" compute_dims=128,\n",
|
||||
" res_out_dims=128,\n",
|
||||
" res_blocks=10,\n",
|
||||
" hop_length=ap_vocoder.hop_length,\n",
|
||||
" sample_rate=ap_vocoder.sample_rate,\n",
|
||||
" use_upsample_net = True,\n",
|
||||
" use_aux_net = True\n",
|
||||
" ).cuda()\n",
|
||||
"\n",
|
||||
" check = torch.load(VOCODER_MODEL_PATH)\n",
|
||||
" wavernn.load_state_dict(check['model'], strict=False)\n",
|
||||
" if use_cuda:\n",
|
||||
" wavernn.cuda()\n",
|
||||
" wavernn.eval();\n",
|
||||
" print(check['step'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 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 = 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": {},
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Comparison with https://keithito.github.io/audio-samples/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Comparison with https://google.github.io/tacotron/publications/tacotron/index.html"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \" He has read the whole thing.\"\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 = \"He reads books.\"\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 = \"Thisss isrealy awhsome.\"\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 is your internet browser, Firefox.\"\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 is your internet browser Firefox.\"\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 quick brown fox jumps over the lazy dog.\"\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 = \"Does the quick brown fox jump over the lazy dog?\"\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 = \"Eren, how are you?\"\n",
|
||||
"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Hard Sentences"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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, use_gl=use_gl, figures=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
|
@ -4,7 +4,7 @@ import torch as T
|
|||
|
||||
from TTS.speaker_encoder.model import SpeakerEncoder
|
||||
from TTS.speaker_encoder.loss import GE2ELoss
|
||||
from TTS.utils.generic_utils import load_config
|
||||
from TTS.utils.io import load_config
|
||||
|
||||
|
||||
file_path = os.path.dirname(os.path.realpath(__file__)) + "/../tests/"
|
||||
|
|
|
|||
|
|
@ -124,10 +124,10 @@
|
|||
"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
|
||||
"CONFIG = load_config(CONFIG_PATH)\n",
|
||||
"# Run FLAGs\n",
|
||||
"use_cuda = True\n",
|
||||
"use_cuda = True # use the available GPU (only for torch)\n",
|
||||
"# Set the vocoder\n",
|
||||
"use_gl = True # use GL if True\n",
|
||||
"BACKEND = 'tf'"
|
||||
"use_gl = True # use GL if True\n",
|
||||
"BACKEND = 'tf' # set the backend for inference "
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Reference in New Issue