mirror of https://github.com/coqui-ai/TTS.git
651 lines
20 KiB
Plaintext
651 lines
20 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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"colab_type": "text",
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"id": "yZK6UdwSFnOO"
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},
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"source": [
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"# **Download and install Coqui 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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"colab": {},
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"colab_type": "code",
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"id": "yvb0pX3WY6MN"
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},
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"outputs": [],
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"source": [
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"import os \n",
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"!git clone https://github.com/Edresson/TTS -b dev-gst-embeddings"
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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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"colab": {},
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"colab_type": "code",
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"id": "iB9nl2UEG3SY"
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},
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"outputs": [],
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"source": [
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"!apt-get install espeak\n",
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"os.chdir('TTS')\n",
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"!pip install -r requirements.txt\n",
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"!python setup.py develop\n",
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"os.chdir('..')"
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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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"colab_type": "text",
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"id": "w6Krn8k1inC_"
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},
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"source": [
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"\n",
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"\n",
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"**Download Checkpoint**\n",
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"\n",
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"\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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"colab": {},
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"colab_type": "code",
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"id": "PiYHf3lKhi9z"
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},
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"outputs": [],
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"source": [
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"!wget -c -q --show-progress -O ./TTS-checkpoint.zip https://github.com/Edresson/TTS/releases/download/v1.0.0/Checkpoints-TTS-MultiSpeaker-Jia-et-al-2018.zip\n",
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"!unzip ./TTS-checkpoint.zip\n"
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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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"colab_type": "text",
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"id": "MpYNgqrZcJKn"
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},
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"source": [
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"**Utils Functions**"
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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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"colab": {},
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"colab_type": "code",
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"id": "4KZA4b_CbMqx"
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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 argparse\n",
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"import json\n",
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"# pylint: disable=redefined-outer-name, unused-argument\n",
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"import os\n",
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"import string\n",
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"import time\n",
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"import sys\n",
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"import numpy as np\n",
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"\n",
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"TTS_PATH = \"../content/TTS\"\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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"\n",
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"import torch\n",
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"\n",
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"from TTS.tts.utils.generic_utils import setup_model\n",
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"from TTS.tts.utils.synthesis import synthesis\n",
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"from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.io import load_config\n",
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"from TTS.vocoder.utils.generic_utils import setup_generator\n",
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"\n",
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"\n",
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"def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None):\n",
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" t_1 = time.time()\n",
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" waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, None, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)\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 = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
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" if use_cuda and not use_gl:\n",
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" waveform = waveform.cpu()\n",
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" if not use_gl:\n",
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" waveform = waveform.numpy()\n",
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" waveform = waveform.squeeze()\n",
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" rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
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" tps = (time.time() - t_1) / len(waveform)\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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" print(\" > Time per step: {}\".format(tps))\n",
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" return waveform\n",
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"\n"
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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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"colab_type": "text",
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"id": "ENA2OumIVeMA"
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},
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"source": [
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"# **Vars definitions**\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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"colab": {},
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"colab_type": "code",
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"id": "jPD0d_XpVXmY"
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},
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"outputs": [],
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"source": [
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"TEXT = ''\n",
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"OUT_PATH = 'tests-audios/'\n",
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"# create output path\n",
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"os.makedirs(OUT_PATH, exist_ok=True)\n",
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"\n",
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"SPEAKER_FILEID = None # if None use the first embedding from speakers.json\n",
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"\n",
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"# model vars \n",
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"MODEL_PATH = 'best_model.pth.tar'\n",
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"CONFIG_PATH = 'config.json'\n",
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"SPEAKER_JSON = 'speakers.json'\n",
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"\n",
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"# vocoder vars\n",
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"VOCODER_PATH = ''\n",
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"VOCODER_CONFIG_PATH = ''\n",
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"\n",
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"USE_CUDA = 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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"colab_type": "text",
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"id": "dV6cXXlfi72r"
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},
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"source": [
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"# **Restore TTS Model**"
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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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"colab": {},
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"colab_type": "code",
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"id": "x1WgLFauWUPe"
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},
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"outputs": [],
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"source": [
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"# load the config\n",
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"C = load_config(CONFIG_PATH)\n",
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"C.forward_attn_mask = True\n",
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"\n",
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"# load the audio processor\n",
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"ap = AudioProcessor(**C.audio)\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 C.keys():\n",
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" symbols, phonemes = make_symbols(**C.characters)\n",
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"\n",
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"speaker_embedding = None\n",
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"speaker_embedding_dim = None\n",
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"num_speakers = 0\n",
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"# load speakers\n",
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"if SPEAKER_JSON != '':\n",
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" speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
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" num_speakers = len(speaker_mapping)\n",
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" if C.use_external_speaker_embedding_file:\n",
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" if SPEAKER_FILEID is not None:\n",
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" speaker_embedding = speaker_mapping[SPEAKER_FILEID]['embedding']\n",
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" else: # if speaker_fileid is not specificated use the first sample in speakers.json\n",
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" choise_speaker = list(speaker_mapping.keys())[0]\n",
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" print(\" Speaker: \",choise_speaker.split('_')[0],'was chosen automatically', \"(this speaker seen in training)\")\n",
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" speaker_embedding = speaker_mapping[choise_speaker]['embedding']\n",
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" speaker_embedding_dim = len(speaker_embedding)\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
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"model = setup_model(num_chars, num_speakers, C, speaker_embedding_dim)\n",
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"cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))\n",
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"model.load_state_dict(cp['model'])\n",
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"model.eval()\n",
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"\n",
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"if USE_CUDA:\n",
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" model.cuda()\n",
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"\n",
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"model.decoder.set_r(cp['r'])\n",
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"\n",
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"# load vocoder model\n",
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"if VOCODER_PATH!= \"\":\n",
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" VC = load_config(VOCODER_CONFIG_PATH)\n",
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" vocoder_model = setup_generator(VC)\n",
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" vocoder_model.load_state_dict(torch.load(VOCODER_PATH, map_location=\"cpu\")[\"model\"])\n",
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" vocoder_model.remove_weight_norm()\n",
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" if USE_CUDA:\n",
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" vocoder_model.cuda()\n",
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" vocoder_model.eval()\n",
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"else:\n",
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" vocoder_model = None\n",
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" VC = None\n",
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"\n",
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"# synthesize voice\n",
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"use_griffin_lim = VOCODER_PATH== \"\"\n",
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"\n",
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"if not C.use_external_speaker_embedding_file:\n",
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" if SPEAKER_FILEID.isdigit():\n",
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" SPEAKER_FILEID = int(SPEAKER_FILEID)\n",
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" else:\n",
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" SPEAKER_FILEID = None\n",
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"else:\n",
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" SPEAKER_FILEID = None\n"
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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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"colab_type": "text",
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"id": "tNvVEoE30qY6"
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},
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"source": [
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"Synthesize sentence with Speaker\n",
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"\n",
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"> Stop running the cell to leave!\n",
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"\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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"colab": {},
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"colab_type": "code",
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"id": "2o8fXkVSyXOa"
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},
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"outputs": [],
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"source": [
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"import IPython\n",
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"from IPython.display import Audio\n",
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"print(\"Synthesize sentence with Speaker: \",choise_speaker.split('_')[0], \"(this speaker seen in training)\")\n",
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"while True:\n",
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" TEXT = input(\"Enter sentence: \")\n",
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" print(\" > Text: {}\".format(TEXT))\n",
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" wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
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" IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
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" # save the results\n",
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" file_name = TEXT.replace(\" \", \"_\")\n",
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" file_name = file_name.translate(\n",
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" str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
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" out_path = os.path.join(OUT_PATH, file_name)\n",
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" print(\" > Saving output to {}\".format(out_path))\n",
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" ap.save_wav(wav, out_path)"
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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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"colab_type": "text",
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"id": "vnV-FigfvsS2"
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},
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"source": [
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"# **Select Speaker**\n",
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"\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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"colab": {},
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"colab_type": "code",
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"id": "RuCGOnJ_fgDV"
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},
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"outputs": [],
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"source": [
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"\n",
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"# VCTK speakers not seen in training (new speakers)\n",
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"VCTK_test_Speakers = [\"p225\", \"p234\", \"p238\", \"p245\", \"p248\", \"p261\", \"p294\", \"p302\", \"p326\", \"p335\", \"p347\"]\n",
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"\n",
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"# VCTK speakers seen in training\n",
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"VCTK_train_Speakers = ['p244', 'p300', 'p303', 'p273', 'p292', 'p252', 'p254', 'p269', 'p345', 'p274', 'p363', 'p285', 'p351', 'p361', 'p295', 'p266', 'p307', 'p230', 'p339', 'p253', 'p310', 'p241', 'p256', 'p323', 'p237', 'p229', 'p298', 'p336', 'p276', 'p305', 'p255', 'p278', 'p299', 'p265', 'p267', 'p280', 'p260', 'p272', 'p262', 'p334', 'p283', 'p247', 'p246', 'p374', 'p297', 'p249', 'p250', 'p304', 'p240', 'p236', 'p312', 'p286', 'p263', 'p258', 'p313', 'p376', 'p279', 'p340', 'p362', 'p284', 'p231', 'p308', 'p277', 'p275', 'p333', 'p314', 'p330', 'p264', 'p226', 'p288', 'p343', 'p239', 'p232', 'p268', 'p270', 'p329', 'p227', 'p271', 'p228', 'p311', 'p301', 'p293', 'p364', 'p251', 'p317', 'p360', 'p281', 'p243', 'p287', 'p233', 'p259', 'p316', 'p257', 'p282', 'p306', 'p341', 'p318']\n",
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"\n",
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"\n",
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"num_samples_speaker = 2 # In theory the more samples of the speaker the more similar to the real voice it will be!\n"
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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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"colab_type": "text",
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"id": "hkvv7gRcx4WV"
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},
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"source": [
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"## **Example select a VCTK seen speaker in training**"
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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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"colab": {},
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"colab_type": "code",
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"id": "BviNMI9UyCYz"
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},
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"outputs": [],
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"source": [
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"# get embedding\n",
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"Speaker_choise = VCTK_train_Speakers[0] # choise one of training speakers\n",
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"# load speakers\n",
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"if SPEAKER_JSON != '':\n",
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" speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
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" if C.use_external_speaker_embedding_file:\n",
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" speaker_embeddings = []\n",
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" for key in list(speaker_mapping.keys()):\n",
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" if Speaker_choise in key:\n",
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" if len(speaker_embeddings) < num_samples_speaker:\n",
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" speaker_embeddings.append(speaker_mapping[key]['embedding'])\n",
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" # takes the average of the embedings samples of the announcers\n",
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" speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()\n",
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" "
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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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"colab": {},
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"colab_type": "code",
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"id": "5e5_XnLsx3jg"
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},
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"outputs": [],
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"source": [
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"import IPython\n",
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"from IPython.display import Audio\n",
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"print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker seen in training)\")\n",
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"while True:\n",
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" TEXT = input(\"Enter sentence: \")\n",
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" print(\" > Text: {}\".format(TEXT))\n",
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" wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
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" IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
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" # save the results\n",
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" file_name = TEXT.replace(\" \", \"_\")\n",
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" file_name = file_name.translate(\n",
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" str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
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" out_path = os.path.join(OUT_PATH, file_name)\n",
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" print(\" > Saving output to {}\".format(out_path))\n",
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" ap.save_wav(wav, out_path)"
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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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"colab_type": "text",
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"id": "QJ6VgT2a4vHW"
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},
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"source": [
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"## **Example select a VCTK not seen speaker in training (new Speakers)**\n",
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"\n",
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"\n",
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"> Fitting new Speakers :)\n",
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"\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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"colab": {},
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"colab_type": "code",
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"id": "SZS57ZK-4vHa"
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},
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"outputs": [],
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"source": [
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"# get embedding\n",
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"Speaker_choise = VCTK_test_Speakers[0] # choise one of training speakers\n",
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"# load speakers\n",
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"if SPEAKER_JSON != '':\n",
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" speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
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" if C.use_external_speaker_embedding_file:\n",
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" speaker_embeddings = []\n",
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" for key in list(speaker_mapping.keys()):\n",
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" if Speaker_choise in key:\n",
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" if len(speaker_embeddings) < num_samples_speaker:\n",
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" speaker_embeddings.append(speaker_mapping[key]['embedding'])\n",
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" # takes the average of the embedings samples of the announcers\n",
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" speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()\n",
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" "
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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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"colab": {},
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"colab_type": "code",
|
|
"id": "bbs85vzz4vHo"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import IPython\n",
|
|
"from IPython.display import Audio\n",
|
|
"print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker not seen in training (new speaker))\")\n",
|
|
"while True:\n",
|
|
" TEXT = input(\"Enter sentence: \")\n",
|
|
" print(\" > Text: {}\".format(TEXT))\n",
|
|
" wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
|
|
" IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
|
|
" # save the results\n",
|
|
" file_name = TEXT.replace(\" \", \"_\")\n",
|
|
" file_name = file_name.translate(\n",
|
|
" str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
|
|
" out_path = os.path.join(OUT_PATH, file_name)\n",
|
|
" print(\" > Saving output to {}\".format(out_path))\n",
|
|
" ap.save_wav(wav, out_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "LEE6mQLh5Who"
|
|
},
|
|
"source": [
|
|
"# **Example Synthesizing with your own voice :)**\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "La70gSB65nrs"
|
|
},
|
|
"source": [
|
|
" Download and load GE2E Speaker Encoder "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "r0IEFZ0B5vQg"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"!wget -c -q --show-progress -O ./SpeakerEncoder-checkpoint.zip https://github.com/Edresson/TTS/releases/download/v1.0.0/GE2E-SpeakerEncoder-iter25k.zip\n",
|
|
"!unzip ./SpeakerEncoder-checkpoint.zip"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "jEH8HCTh5mF6"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"SE_MODEL_RUN_PATH = \"GE2E-SpeakerEncoder/\"\n",
|
|
"SE_MODEL_PATH = os.path.join(SE_MODEL_RUN_PATH, \"best_model.pth.tar\")\n",
|
|
"SE_CONFIG_PATH =os.path.join(SE_MODEL_RUN_PATH, \"config.json\")\n",
|
|
"USE_CUDA = True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "tOwkfQqT6-Qo"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from TTS.utils.audio import AudioProcessor\n",
|
|
"from TTS.speaker_encoder.model import SpeakerEncoder\n",
|
|
"se_config = load_config(SE_CONFIG_PATH)\n",
|
|
"se_ap = AudioProcessor(**se_config['audio'])\n",
|
|
"\n",
|
|
"se_model = SpeakerEncoder(**se_config.model)\n",
|
|
"se_model.load_state_dict(torch.load(SE_MODEL_PATH)['model'])\n",
|
|
"se_model.eval()\n",
|
|
"if USE_CUDA:\n",
|
|
" se_model.cuda()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "0TLlbUFG8O36"
|
|
},
|
|
"source": [
|
|
"Upload a wav audio file in your voice.\n",
|
|
"\n",
|
|
"\n",
|
|
"> We recommend files longer than 3 seconds, the bigger the file the closer to your voice :)\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "_FWwHPjJ8NXl"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from google.colab import files\n",
|
|
"file_list = files.upload()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "WWOf6sgbBbGY"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# extract embedding from wav files\n",
|
|
"speaker_embeddings = []\n",
|
|
"for name in file_list.keys():\n",
|
|
" if '.wav' in name:\n",
|
|
" mel_spec = se_ap.melspectrogram(se_ap.load_wav(name, sr=se_ap.sample_rate)).T\n",
|
|
" mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
|
|
" if USE_CUDA:\n",
|
|
" mel_spec = mel_spec.cuda()\n",
|
|
" embedd = se_model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
|
|
" speaker_embeddings.append(embedd)\n",
|
|
" else:\n",
|
|
" print(\" You need upload Wav files, others files is not supported !!\")\n",
|
|
"\n",
|
|
"# takes the average of the embedings samples of the announcers\n",
|
|
"speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "xmItcGac5WiG"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import IPython\n",
|
|
"from IPython.display import Audio\n",
|
|
"print(\"Synthesize sentence with New Speaker using files: \",file_list.keys(), \"(this speaker not seen in training (new speaker))\")\n",
|
|
"while True:\n",
|
|
" TEXT = input(\"Enter sentence: \")\n",
|
|
" print(\" > Text: {}\".format(TEXT))\n",
|
|
" wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
|
|
" IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
|
|
" # save the results\n",
|
|
" file_name = TEXT.replace(\" \", \"_\")\n",
|
|
" file_name = file_name.translate(\n",
|
|
" str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
|
|
" out_path = os.path.join(OUT_PATH, file_name)\n",
|
|
" print(\" > Saving output to {}\".format(out_path))\n",
|
|
" ap.save_wav(wav, out_path)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "GPU",
|
|
"colab": {
|
|
"collapsed_sections": [
|
|
"vnV-FigfvsS2",
|
|
"hkvv7gRcx4WV",
|
|
"QJ6VgT2a4vHW"
|
|
],
|
|
"name": "Demo-Mozilla-TTS-MultiSpeaker-jia-et-al-2018.ipynb",
|
|
"provenance": []
|
|
},
|
|
"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": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|