mirror of https://github.com/coqui-ai/TTS.git
ParallelWaveGAN notebook
erogol
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d4aacd5f6d
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d75d6ae523
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "DDC-TTS_and_MultiBand-MelGAN_Example.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"toc_visible": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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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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"id": "6LWsNd3_M3MP",
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"colab_type": "text"
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},
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"source": [
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"# Mozilla TTS on CPU Real-Time Speech Synthesis "
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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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"id": "FAqrSIWgLyP0",
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"colab_type": "text"
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},
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"source": [
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"We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n",
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"\n",
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"Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n",
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"\n",
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"MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n",
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"\n",
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"Note that both model performances can be improved with more training."
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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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"id": "Ku-dA4DKoeXk",
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"colab_type": "text"
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},
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"source": [
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"### Download Models"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "jGIgnWhGsxU1",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 162
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},
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"outputId": "88725e41-a8dc-4885-b3bf-cac939f38abe",
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"tags": []
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},
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"source": [
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"!gdown --id 1dntzjWFg7ufWaTaFy80nRz-Tu02xWZos -O data/tts_model.pth.tar\n",
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"!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "4dnpE0-kvTsu",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 235
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},
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"outputId": "76377c6d-789c-4995-ba00-a21a6e1c401e",
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"tags": []
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},
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"source": [
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"!gdown --id 1X09hHAyAJOnrplCUMAdW_t341Kor4YR4 -O data/vocoder_model.pth.tar\n",
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"!gdown --id \"1qN7vQRIYkzvOX_DtiZtTajzoZ1eW1-Eg\" -O data/config_vocoder.json\n",
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"!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Zlgi8fPdpRF0",
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"colab_type": "text"
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},
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"source": [
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"### Define TTS function"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "f-Yc42nQZG5A",
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"colab_type": "code",
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"colab": {}
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},
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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, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n",
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" truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n",
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" # mel_postnet_spec = ap._denormalize(mel_postnet_spec.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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" waveform = waveform.flatten()\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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" tps = (time.time() - t_1) / len(waveform)\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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" print(\" > Time per step: {}\".format(tps))\n",
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" IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n",
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" return alignment, mel_postnet_spec, stop_tokens, waveform"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ZksegYQepkFg",
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"colab_type": "text"
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},
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"source": [
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"### Load Models"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "oVa0kOamprgj",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import os\n",
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"import torch\n",
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"import time\n",
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"import IPython\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.utils.io import load_config\n",
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"from TTS.tts.utils.text.symbols import symbols, phonemes\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.tts.utils.synthesis import synthesis"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "EY-sHVO8IFSH",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# runtime settings\n",
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"use_cuda = False"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "_1aIUp2FpxOQ",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# model paths\n",
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"TTS_MODEL = \"data/tts_model.pth.tar\"\n",
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"TTS_CONFIG = \"data/config.json\"\n",
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"VOCODER_MODEL = \"data/vocoder_model.pth.tar\"\n",
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"VOCODER_CONFIG = \"data/config_vocoder.json\""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "CpgmdBVQplbv",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# load configs\n",
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"TTS_CONFIG = load_config(TTS_CONFIG)\n",
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"VOCODER_CONFIG = load_config(VOCODER_CONFIG)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zmrQxiozIUVE",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 471
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},
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"outputId": "60c4daa0-4c5b-4a2e-fe0d-be437d003a49",
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"tags": []
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},
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"source": [
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"# load the audio processor\n",
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"TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n",
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"ap = AudioProcessor(**TTS_CONFIG.audio) "
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "8fLoI4ipqMeS",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 35
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},
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"outputId": "b789066e-e305-42ad-b3ca-eba8d9267382",
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"tags": []
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},
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"source": [
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"# LOAD TTS MODEL\n",
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"# multi speaker \n",
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"speaker_id = None\n",
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"speakers = []\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
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"model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
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"\n",
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"# load model state\n",
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"cp = torch.load(TTS_MODEL, 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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"\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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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zKoq0GgzqzhQ",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"outputId": "234efc61-f37a-40bc-95a3-b51896018ccb",
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"tags": []
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},
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"source": [
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"from TTS.vocoder.utils.generic_utils import setup_generator\n",
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"\n",
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"# LOAD VOCODER MODEL\n",
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"vocoder_model = setup_generator(VOCODER_CONFIG)\n",
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"vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location=\"cpu\")[\"model\"])\n",
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"vocoder_model.remove_weight_norm()\n",
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"vocoder_model.inference_padding = 0\n",
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"\n",
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"ap_vocoder = AudioProcessor(**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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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Ws_YkPKsLgo-",
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"colab_type": "text"
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},
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"source": [
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"## Run Inference"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "FuWxZ9Ey5Puj",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 134
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},
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"outputId": "9c06adad-5451-4393-89a1-a2e7dc39ab91",
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"tags": []
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},
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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, TTS_CONFIG, use_cuda, ap, use_gl=False, figures=True)"
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],
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"execution_count": null,
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"outputs": []
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}
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]
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}
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