{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "DDC-TTS_and_MultiBand-MelGAN_TF_Example.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "6LWsNd3_M3MP",
"colab_type": "text"
},
"source": [
"# Mozilla TTS on CPU Real-Time Speech Synthesis with Tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FAqrSIWgLyP0",
"colab_type": "text"
},
"source": [
"**These models are converted from released [PyTorch models](https://colab.research.google.com/drive/1u_16ZzHjKYFn1HNVuA4Qf_i2MMFB9olY?usp=sharing) using our TF utilities provided in Mozilla TTS.**\n",
"\n",
"These TF models support TF 2.2 and for different versions you might need to\n",
"regenerate them. \n",
"\n",
"We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n",
"\n",
"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",
"\n",
"MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n",
"\n",
"Note that both model performances can be improved with more training.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ku-dA4DKoeXk",
"colab_type": "text"
},
"source": [
"### Download Models"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jGIgnWhGsxU1",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 162
},
"outputId": "08b0dddd-4edf-48c9-e8e5-a419b36a5c3d",
"tags": []
},
"source": [
"!gdown --id 1p7OSEEW_Z7ORxNgfZwhMy7IiLE1s0aH7 -O data/tts_model.pkl\n",
"!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4dnpE0-kvTsu",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"outputId": "2fe836eb-c7e7-4f1e-9352-0142126bb19f",
"tags": []
},
"source": [
"!gdown --id 1rHmj7CqD3Sfa716Y3ub_vpIBrQg_b1yF -O data/vocoder_model.pkl\n",
"!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n",
"!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zlgi8fPdpRF0",
"colab_type": "text"
},
"source": [
"### Define TTS function"
]
},
{
"cell_type": "code",
"metadata": {
"id": "f-Yc42nQZG5A",
"colab_type": "code",
"colab": {}
},
"source": [
"def tts(model, text, CONFIG, p):\n",
" t_1 = time.time()\n",
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = 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",
" backend='tf')\n",
" waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
" waveform = waveform.numpy()[0, 0]\n",
" rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
" tps = (time.time() - t_1) / len(waveform)\n",
" print(waveform.shape)\n",
" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
" print(\" > Real-time factor: {}\".format(rtf))\n",
" print(\" > Time per step: {}\".format(tps))\n",
" IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n",
" return alignment, mel_postnet_spec, stop_tokens, waveform"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZksegYQepkFg",
"colab_type": "text"
},
"source": [
"### Load Models"
]
},
{
"cell_type": "code",
"metadata": {
"id": "oVa0kOamprgj",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"import torch\n",
"import time\n",
"import IPython\n",
"\n",
"from TTS.tts.tf.utils.generic_utils import setup_model\n",
"from TTS.tts.tf.utils.io import load_checkpoint\n",
"from TTS.utils.io import load_config\n",
"from TTS.tts.utils.text.symbols import symbols, phonemes\n",
"from TTS.utils.audio import AudioProcessor\n",
"from TTS.tts.utils.synthesis import synthesis"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EY-sHVO8IFSH",
"colab_type": "code",
"colab": {}
},
"source": [
"# runtime settings\n",
"use_cuda = False"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_1aIUp2FpxOQ",
"colab_type": "code",
"colab": {}
},
"source": [
"# model paths\n",
"TTS_MODEL = \"data/tts_model.pkl\"\n",
"TTS_CONFIG = \"data/config.json\"\n",
"VOCODER_MODEL = \"data/vocoder_model.pkl\"\n",
"VOCODER_CONFIG = \"data/config_vocoder.json\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CpgmdBVQplbv",
"colab_type": "code",
"colab": {}
},
"source": [
"# load configs\n",
"TTS_CONFIG = load_config(TTS_CONFIG)\n",
"VOCODER_CONFIG = load_config(VOCODER_CONFIG)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zmrQxiozIUVE",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 471
},
"outputId": "fa71bd05-401f-4e5b-a6f7-60ae765966db",
"tags": []
},
"source": [
"# load the audio processor\n",
"TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n",
"ap = AudioProcessor(**TTS_CONFIG.audio) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8fLoI4ipqMeS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"outputId": "595d990f-930d-4698-ee14-77796b5eed7d",
"tags": []
},
"source": [
"# LOAD TTS MODEL\n",
"# multi speaker \n",
"speaker_id = None\n",
"speakers = []\n",
"\n",
"# load the model\n",
"num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
"model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
"model.build_inference()\n",
"model = load_checkpoint(model, TTS_MODEL)\n",
"model.decoder.set_max_decoder_steps(1000)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zKoq0GgzqzhQ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"outputId": "2cc3deae-144f-4465-da3b-98628d948506"
},
"source": [
"from TTS.vocoder.tf.utils.generic_utils import setup_generator\n",
"from TTS.vocoder.tf.utils.io import load_checkpoint\n",
"\n",
"# LOAD VOCODER MODEL\n",
"vocoder_model = setup_generator(VOCODER_CONFIG)\n",
"vocoder_model.build_inference()\n",
"vocoder_model = load_checkpoint(vocoder_model, VOCODER_MODEL)\n",
"vocoder_model.inference_padding = 0\n",
"\n",
"ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ws_YkPKsLgo-",
"colab_type": "text"
},
"source": [
"## Run Inference"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FuWxZ9Ey5Puj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 134
},
"outputId": "07ede6e5-06e6-4612-f687-7984d20e5254"
},
"source": [
"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, TTS_CONFIG, ap)"
],
"execution_count": null,
"outputs": []
}
]
}