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tacotron2
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tacotron2/inference.ipynb

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inference.ipynb: adding notebook for griffin-lim inference
2018-05-03 22:16:32 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tacotron 2 inference code \n",
"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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import libraries and setup matplotlib"
]
},
{
"cell_type": "code",
inference.ipynb: import taco2model to be public
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"execution_count": 1,
inference.ipynb: adding code to remove waveglows bias
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"metadata": {
"collapsed": true
},
inference.ipynb: setting relative model paths
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
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"text": [ ]
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}
],
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"source": [
"import matplotlib\n",
"matplotlib.use(\"Agg\")\n",
"import matplotlib.pylab as plt\n",
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"%matplotlib inline\n",
inference.ipynb: adding notebook for griffin-lim inference
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"import IPython.display as ipd\n",
"\n",
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"import sys\n",
"sys.path.append('waveglow/')\n",
inference.ipynb: adding notebook for griffin-lim inference
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"import numpy as np\n",
"import torch\n",
"\n",
"from hparams import create_hparams\n",
"from model import Tacotron2\n",
inference.ipynb: adding code to remove waveglows bias
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"from layers import TacotronSTFT, STFT\n",
inference.ipynb: adding notebook for griffin-lim inference
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"from audio_processing import griffin_lim\n",
"from train import load_model\n",
inference.ipynb: adding code to remove waveglows bias
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"from text import text_to_sequence\n",
"from denoiser import Denoiser"
inference.ipynb: adding notebook for griffin-lim inference
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]
},
{
"cell_type": "code",
inference.ipynb: import taco2model to be public
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"execution_count": 2,
inference.ipynb: adding notebook for griffin-lim inference
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"metadata": {},
"outputs": [],
"source": [
"def plot_data(data, figsize=(16, 4)):\n",
" fig, axes = plt.subplots(1, len(data), figsize=figsize)\n",
" for i in range(len(data)):\n",
" axes[i].imshow(data[i], aspect='auto', origin='bottom', \n",
" interpolation='none')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setup hparams"
]
},
{
"cell_type": "code",
inference.ipynb: updating inference file with relative paths
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"execution_count": 3,
inference.ipynb: adding notebook for griffin-lim inference
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"metadata": {},
inference.ipynb: cleanup
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"outputs": [],
inference.ipynb: adding notebook for griffin-lim inference
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"source": [
inference.ipynb: cleanup
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"hparams = create_hparams()\n",
"hparams.sampling_rate = 22050"
inference.ipynb: adding notebook for griffin-lim inference
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load model from checkpoint"
]
},
{
"cell_type": "code",
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"execution_count": 4,
inference.ipynb: adding notebook for griffin-lim inference
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"metadata": {},
"outputs": [],
"source": [
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"checkpoint_path = \"tacotron2_statedict\"\n",
inference.ipynb: adding notebook for griffin-lim inference
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"model = load_model(hparams)\n",
inference.ipynb: cleanup
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"model.load_state_dict(torch.load(checkpoint_path)['state_dict'])\n",
inference.ipynb: adding notebook for griffin-lim inference
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"_ = model.eval()"
]
},
inference.ipynb: import taco2model to be public
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{
"cell_type": "markdown",
"metadata": {},
"source": [
inference.ipynb: adding code to remove waveglows bias
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"#### Load WaveGlow for mel2audio synthesis and denoiser"
inference.ipynb: import taco2model to be public
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]
},
{
"cell_type": "code",
inference.ipynb: updating inference file with relative paths
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"execution_count": 5,
inference.ipynb: adding code to remove waveglows bias
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"metadata": {
"collapsed": true
},
inference.ipynb: updating inference file with relative paths
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
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"text": [ ]
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}
],
inference.ipynb: import taco2model to be public
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"source": [
inference.ipynb: setting relative model paths
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"waveglow_path = 'waveglow_old.pt'\n",
inference.ipynb: changing waverglow inference fo fp16
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"waveglow = torch.load(waveglow_path)['model']\n",
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"waveglow.cuda()\n",
"denoiser = Denoiser(waveglow)"
inference.ipynb: import taco2model to be public
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]
},
inference.ipynb: adding notebook for griffin-lim inference
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Prepare text input"
]
},
{
"cell_type": "code",
inference.ipynb: updating inference file with relative paths
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"execution_count": 6,
inference.ipynb: adding notebook for griffin-lim inference
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"metadata": {},
"outputs": [],
"source": [
inference.ipynb: import taco2model to be public
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"text = \"Waveglow is really awesome!\"\n",
inference.ipynb: adding notebook for griffin-lim inference
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"sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]\n",
"sequence = torch.autograd.Variable(\n",
" torch.from_numpy(sequence)).cuda().long()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Decode text input and plot results"
]
},
{
"cell_type": "code",
inference.ipynb: updating inference file with relative paths
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"execution_count": 7,
inference.ipynb: adding notebook for griffin-lim inference
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"metadata": {
"scrolled": true
},
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"outputs": [
{
"data": {
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"image/png": "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
inference.ipynb: updating inference file with relative paths
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"text/plain": [
"<Figure size 1152x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
inference.ipynb: adding notebook for griffin-lim inference
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"source": [
"mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)\n",
"plot_data((mel_outputs.data.cpu().numpy()[0],\n",
" mel_outputs_postnet.data.cpu().numpy()[0],\n",
" alignments.data.cpu().numpy()[0].T))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
inference.ipynb: import taco2model to be public
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"#### Synthesize audio from spectrogram using WaveGlow"
inference.ipynb: adding notebook for griffin-lim inference
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]
},
{
"cell_type": "code",
inference.ipynb: updating inference file with relative paths
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"execution_count": 8,
inference.ipynb: adding notebook for griffin-lim inference
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"metadata": {},
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"outputs": [
{
"data": {
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
inference.ipynb: adding code to remove waveglows bias
2019-03-15 23:54:54 +00:00
" <source src=\"data:audio/wav;base64,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
inference.ipynb: updating inference file with relative paths
2018-11-27 16:04:04 +00:00
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
inference.ipynb: adding notebook for griffin-lim inference
2018-05-03 22:16:32 +00:00
"source": [
inference.ipynb: import taco2model to be public
2018-11-26 06:33:16 +00:00
"with torch.no_grad():\n",
inference.ipynb: reverting fp16 inference for now
2018-12-09 05:26:01 +00:00
" audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)\n",
inference.ipynb: import taco2model to be public
2018-11-26 06:33:16 +00:00
"ipd.Audio(audio[0].data.cpu().numpy(), rate=hparams.sampling_rate)"
inference.ipynb: adding notebook for griffin-lim inference
2018-05-03 22:16:32 +00:00
]
inference.ipynb: adding code to remove waveglows bias
2019-03-15 23:54:54 +00:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (Optional) Remove WaveGlow bias"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
" <source src=\"data:audio/wav;base64,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
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"audio_denoised = denoiser(audio, strength=0.01)[:, 0]\n",
"ipd.Audio(audio_denoised.cpu().numpy(), rate=hparams.sampling_rate) "
]
inference.ipynb: adding notebook for griffin-lim inference
2018-05-03 22:16:32 +00:00
}
],
"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",
inference.ipynb: import taco2model to be public
2018-11-26 06:33:16 +00:00
"version": "3.6.6"
inference.ipynb: adding notebook for griffin-lim inference
2018-05-03 22:16:32 +00:00
}
},
"nbformat": 4,
"nbformat_minor": 2
}