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343 lines
269 KiB
343 lines
269 KiB
3 years ago
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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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"source": [
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"## TTS with Tacotron2 + Waveflow"
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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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"source": [
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"import numpy as np\n",
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"import paddle\n",
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"from matplotlib import pyplot as plt\n",
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"from IPython import display as ipd\n",
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"%matplotlib inline\n",
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"\n",
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"from parakeet.utils import display\n",
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"from parakeet.utils import layer_tools\n",
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"paddle.set_device(\"gpu:0\")\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"../..\")\n",
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"import examples"
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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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"## Tacotron2: synthesizer model\n",
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"\n",
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"Tacotron2 is used here as a phonemes to spectrogram model. Here we will use an alternative config. In this config, the tacotron2 model does not have a binary classifier to predict whether the generation should stop.\n",
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"\n",
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"Instead, the peak position is used as the criterion. When the peak position of the attention reaches the end of the encoder outputs, it implies that the content is exhausted. So we stop the generated after 10 frames."
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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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"from parakeet.models.tacotron2 import Tacotron2\n",
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"from parakeet.frontend import EnglishCharacter"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"data:\n",
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" batch_size: 32\n",
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" fmax: 8000\n",
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" fmin: 0\n",
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" hop_length: 256\n",
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" n_fft: 1024\n",
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" n_mels: 80\n",
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" padding_idx: 0\n",
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" sample_rate: 22050\n",
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" valid_size: 64\n",
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" win_length: 1024\n",
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"model:\n",
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" attention_filters: 32\n",
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" attention_kernel_size: 31\n",
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" d_attention: 128\n",
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" d_attention_rnn: 1024\n",
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" d_decoder_rnn: 1024\n",
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" d_encoder: 512\n",
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" d_global_condition: None\n",
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" d_postnet: 512\n",
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" d_prenet: 256\n",
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" encoder_conv_layers: 3\n",
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" encoder_kernel_size: 5\n",
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" guided_attention_loss_sigma: 0.2\n",
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" n_tones: None\n",
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" p_attention_dropout: 0.1\n",
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" p_decoder_dropout: 0.1\n",
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" p_encoder_dropout: 0.5\n",
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" p_postnet_dropout: 0.5\n",
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" p_prenet_dropout: 0.5\n",
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" postnet_conv_layers: 5\n",
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" postnet_kernel_size: 5\n",
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" reduction_factor: 1\n",
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" use_guided_attention_loss: True\n",
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" use_stop_token: False\n",
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" vocab_size: 37\n",
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"training:\n",
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" grad_clip_thresh: 1.0\n",
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" lr: 0.001\n",
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" max_iteration: 500000\n",
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" plot_interval: 1000\n",
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" save_interval: 1000\n",
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" valid_interval: 1000\n",
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" weight_decay: 1e-06\n"
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]
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}
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],
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"source": [
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"from examples.tacotron2 import config as tacotron2_config\n",
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"synthesizer_config = tacotron2_config.get_cfg_defaults()\n",
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"synthesizer_config.merge_from_file(\"configs/alternative.yaml\")\n",
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"print(synthesizer_config)"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[checkpoint] Rank 0: loaded model from ../../pretrained/tacotron2/tacotron2_ljspeech_ckpt_0.3_alternative/step-50000.pdparams\n"
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]
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}
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],
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"source": [
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"frontend = EnglishCharacter()\n",
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"model = Tacotron2.from_pretrained(\n",
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" synthesizer_config, \"../../pretrained/tacotron2/tacotron2_ljspeech_ckpt_0.3_alternative/step-50000\")\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": "code",
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"execution_count": 7,
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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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" 36%|███▋ | 365/1000 [00:01<00:02, 256.89it/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"content exhausted!\n"
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]
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}
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],
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"source": [
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"sentence = \"Life was like a box of chocolates, you never know what you're gonna get.\" \n",
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"sentence = paddle.to_tensor(frontend(sentence)).unsqueeze(0)\n",
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"\n",
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"with paddle.no_grad():\n",
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" outputs = model.infer(sentence)\n",
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"mel_output = outputs[\"mel_outputs_postnet\"][0].numpy().T\n",
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"alignment = outputs[\"alignments\"][0].numpy().T"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig = display.plot_alignment(alignment)"
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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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"## WaveFlow: vocoder model\n",
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"Generated spectrogram is converted to raw audio using a pretrained waveflow 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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"from parakeet.models.waveflow import ConditionalWaveFlow"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"data:\n",
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" batch_size: 8\n",
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" clip_frames: 65\n",
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" fmax: 8000\n",
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" fmin: 0\n",
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" hop_length: 256\n",
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" n_fft: 1024\n",
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" n_mels: 80\n",
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" sample_rate: 22050\n",
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" valid_size: 16\n",
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" win_length: 1024\n",
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"model:\n",
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" channels: 128\n",
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" kernel_size: [3, 3]\n",
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" n_flows: 8\n",
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" n_group: 16\n",
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" n_layers: 8\n",
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" sigma: 1.0\n",
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" upsample_factors: [16, 16]\n",
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"training:\n",
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" lr: 0.0002\n",
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" max_iteration: 3000000\n",
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" save_interval: 10000\n",
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" valid_interval: 1000\n"
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]
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|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from examples.waveflow import config as waveflow_config\n",
|
||
|
|
"vocoder_config = waveflow_config.get_cfg_defaults()\n",
|
||
|
|
"print(vocoder_config)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[checkpoint] Rank 0: loaded model from ../../pretrained/waveflow/waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"vocoder = ConditionalWaveFlow.from_pretrained(\n",
|
||
|
|
" vocoder_config, \n",
|
||
|
|
" \"../../pretrained/waveflow/waveflow_ljspeech_ckpt_0.3/step-2000000\")\n",
|
||
|
|
"layer_tools.recursively_remove_weight_norm(vocoder)\n",
|
||
|
|
"vocoder.eval()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"time: 9.412613868713379s\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"audio = vocoder.infer(paddle.transpose(outputs[\"mel_outputs_postnet\"], [0, 2, 1]))\n",
|
||
|
|
"wav = audio[0].numpy()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"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": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"ipd.Audio(wav, rate=22050)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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",
|
||
|
|
"version": "3.7.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|