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206 lines
4.9 KiB
206 lines
4.9 KiB
{
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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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"## Tacotron 2 inference code \n",
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"Edit the variables **checkpoint_path** and **text** to match yours and run the entire code to generate plots of mel outputs, alignments and audio synthesis from the generated mel-spectrogram using Griffin-Lim."
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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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"#### Import libraries and setup matplotlib"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/dcg-adlr-rafaelvalle-source.cosmos597/repos/nvidia/tacotron2/plotting_utils.py:2: UserWarning: matplotlib.pyplot as already been imported, this call will have no effect.\n",
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" matplotlib.use(\"Agg\")\n"
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]
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}
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],
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"source": [
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"import matplotlib\n",
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"matplotlib.use(\"Agg\")\n",
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"import matplotlib.pylab as plt\n",
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"%matplotlib inline\n",
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"import IPython.display as ipd\n",
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"\n",
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"import sys\n",
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"sys.path.append('waveglow/')\n",
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"import numpy as np\n",
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"import torch\n",
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"\n",
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"from hparams import create_hparams\n",
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"from model import Tacotron2\n",
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"from layers import TacotronSTFT\n",
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"from audio_processing import griffin_lim\n",
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"from train import load_model\n",
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"from text import text_to_sequence\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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_data(data, figsize=(16, 4)):\n",
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" fig, axes = plt.subplots(1, len(data), figsize=figsize)\n",
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" for i in range(len(data)):\n",
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" axes[i].imshow(data[i], aspect='auto', origin='bottom', \n",
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" interpolation='none')"
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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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"#### Setup hparams"
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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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"hparams = create_hparams(\"distributed_run=False,mask_padding=False\")\n",
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"hparams.sampling_rate = 22050\n",
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"hparams.filter_length = 1024\n",
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"hparams.hop_length = 256\n",
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"hparams.win_length = 1024"
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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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"#### Load model from checkpoint"
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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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"checkpoint_path = \"tacotron2_statedict\"\n",
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"\n",
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"model = load_model(hparams)\n",
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"try:\n",
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" model = model.module\n",
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"except:\n",
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" pass\n",
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"model.load_state_dict({k.replace('module.',''):v for k,v in torch.load(checkpoint_path)['state_dict'].items()})\n",
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"_ = 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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"#### Load WaveGlow for mel2audio synthesis"
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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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"waveglow_path = 'waveglow_old.pt'\n",
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"waveglow = torch.load(waveglow_path)['model']"
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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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"#### Prepare text input"
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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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"text = \"Waveglow is really awesome!\"\n",
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"sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]\n",
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"sequence = torch.autograd.Variable(\n",
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" torch.from_numpy(sequence)).cuda().long()"
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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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"#### Decode text input and plot results"
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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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"mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)\n",
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"plot_data((mel_outputs.data.cpu().numpy()[0],\n",
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" mel_outputs_postnet.data.cpu().numpy()[0],\n",
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" alignments.data.cpu().numpy()[0].T))"
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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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"#### Synthesize audio from spectrogram using WaveGlow"
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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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"with torch.no_grad():\n",
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" audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)\n",
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"ipd.Audio(audio[0].data.cpu().numpy(), rate=hparams.sampling_rate)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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