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Update notebooks
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Eren Golge committed
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@@ -4,7 +4,27 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook runs the given model over all the benchmark sentences and show their alignment, stop-token prediction and spectrogram plot in order."
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"This is to test TTS models with benchmark sentences for speech synthesis.\n",
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"\n",
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"Before running this script please DON'T FORGET: \n",
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"- to set file paths.\n",
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"- to download related model files from TTS and WaveRNN.\n",
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"- to checkout right commit versions (given next to the model) of TTS and WaveRNN.\n",
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"- to set the right paths in the cell below.\n",
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"\n",
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"Repositories:\n",
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"- TTS: https://github.com/mozilla/TTS\n",
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"- WaveRNN: https://github.com/erogol/WaveRNN"
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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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"TTS_PATH = \"/home/erogol/projects/\"\n",
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"WAVERNN_PATH =\"/home/erogol/projects/\""
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]
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},
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{
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@@ -28,7 +48,10 @@
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"\n",
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"%pylab inline\n",
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"rcParams[\"figure.figsize\"] = (16,5)\n",
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"# sys.path.append('/home/erogol/projects/') # set this if TTS is not installed globally\n",
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"\n",
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"# add libraries into environment\n",
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"sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n",
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"sys.path.append(WAVERNN_PATH) # set this if TTS is not installed globally\n",
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"\n",
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"import librosa\n",
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"import librosa.display\n",
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@@ -54,7 +77,7 @@
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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 = synthesis(model, text, CONFIG, use_cuda, ap)\n",
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" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, False, CONFIG.enable_eos_bos_chars)\n",
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" if not use_gl:\n",
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" waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=11000, overlap=550)\n",
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"\n",
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@@ -76,8 +99,8 @@
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"outputs": [],
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"source": [
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"# Set constants\n",
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"ROOT_PATH = '/media/erogol/data_ssd/Data/models/ljspeech_models/4241_best_t2_model/'\n",
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"MODEL_PATH = ROOT_PATH + '/best_model_edited.pth.tar'\n",
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"ROOT_PATH = '/media/erogol/data_ssd/Data/models/ljspeech_models/ljspeech-April-08-2019_07+32PM-8a47b46/'\n",
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"MODEL_PATH = ROOT_PATH + '/checkpoint_260000.pth.tar'\n",
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"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
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"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
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"CONFIG = load_config(CONFIG_PATH)\n",
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@@ -85,9 +108,9 @@
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"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/ljspeech/mold_ljspeech_best_model/config.json\"\n",
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"VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
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"use_cuda = True\n",
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"windowing = True\n",
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"CONFIG.windowing = True\n",
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"use_gl = False # use GL if True\n",
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"batched_wavernn = False # use batched wavernn inference if True"
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"batched_wavernn = True # use batched wavernn inference if True"
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]
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},
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{
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@@ -104,8 +127,6 @@
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"model = setup_model(num_chars, CONFIG)\n",
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"\n",
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"# load the audio processor\n",
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"# CONFIG.audio[\"power\"] = 1.3\n",
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"CONFIG.audio[\"preemphasis\"] = 0.97\n",
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"ap = AudioProcessor(**CONFIG.audio) \n",
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"\n",
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"\n",
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@@ -205,7 +226,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"sentence = \"I'm sorry Dave. I'm afraid I can't do that .\"\n",
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"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
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]
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},
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@@ -244,7 +265,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"sentence = \" Generative adversarial network or variational auto-encoder.\"\n",
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"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
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"align, spec, stop_tokens, wav = tts(model, sentence, CONFIG, use_cuda, ap, use_gl=use_gl, figures=True)"
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]
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},
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@@ -1,213 +0,0 @@
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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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"This notebook reads a given article by giving each sentence individually to the network without any state passing. You can also compare different checkpoints below."
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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": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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},
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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/erogol/miniconda3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n",
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" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"import os\n",
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"import sys\n",
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"import io\n",
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"import torch \n",
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"import time\n",
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"import numpy as np\n",
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"from collections import OrderedDict\n",
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"from matplotlib import pylab as plt\n",
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"\n",
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"%pylab inline\n",
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"rcParams[\"figure.figsize\"] = (16,5)\n",
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"sys.path.append('/home/erogol/Projects/') # change here if you don't install TTS by setup.py\n",
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"\n",
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"import librosa\n",
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"import librosa.display\n",
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"\n",
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"from TTS.models.tacotron import Tacotron \n",
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"from TTS.layers import *\n",
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"from TTS.utils.data import *\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.generic_utils import load_config\n",
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"from TTS.utils.text import text_to_sequence\n",
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"\n",
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"import IPython\n",
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"from IPython.display import Audio\n",
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"from synthesis import *"
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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 tts(model, text, CONFIG, use_cuda, ap, figures=True):\n",
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" waveform, alignment, spectrogram, stop_tokens = create_speech(model, text, CONFIG, use_cuda, ap) \n",
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" return waveform\n",
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"\n",
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"def text2audio(text, model, CONFIG, use_cuda, ap):\n",
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" wavs = []\n",
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" for sen in text.split('.'):\n",
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" if len(sen) < 3:\n",
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" continue\n",
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" sen+='.'\n",
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" sen = sen.strip()\n",
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" print(sen)\n",
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" wav = tts(model, sen, CONFIG, use_cuda, ap)\n",
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" wavs.append(wav)\n",
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" wavs.append(np.zeros(10000))\n",
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"# audio = np.stack(wavs)\n",
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"# IPython.display.display(Audio(audio, rate=CONFIG.sample_rate)) \n",
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" return wavs"
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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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"source": [
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"# Set constants\n",
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"ROOT_PATH = '/home/erogol/Projects/runs/local_runs/September-26-2018_06+55PM-TTS-attn-smoothing-bgs-sigmoid-wd-231607a/'\n",
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"MODEL_PATH_TMP = ROOT_PATH + '/checkpoint_{}.pth.tar'\n",
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"# MODEL_PATH_TMP = ROOT_PATH + '/best_model.pth.tar'\n",
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"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
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"OUT_FOLDER = ROOT_PATH + '/test'\n",
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"CONFIG = load_config(CONFIG_PATH)\n",
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"use_cuda = True"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Try different checkpoints\n",
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"check_idxs = [150000, 200000, 250000, 300000, 350000, 400000]"
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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": 5,
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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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" | > Number of characters : 149\n",
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" > Setting up Audio Processor...\n",
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" | > fft size: 2048, hop length: 275, win length: 1100\n",
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"/home/erogol/Projects/runs/local_runs/September-26-2018_06+55PM-TTS-attn-smoothing-bgs-sigmoid-wd-231607a//best_model.pth.tar\n",
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"Voice is natural, voice is human.\n",
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"That’s why we are fascinated with creating usable voice technology for our machines.\n",
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"But to create voice systems, an extremely large amount of voice data is required.\n",
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"Most of the data used by large companies isn’t available to the majority of people.\n",
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"We think that stifles innovation.\n",
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"So we’ve launched Project Common Voice, a project to help make voice recognition open to everyone.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"\n",
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" <audio controls=\"controls\" >\n",
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" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# load the model\n",
|
||||
"model = Tacotron(CONFIG.embedding_size, CONFIG.num_freq, CONFIG.num_mels, CONFIG.r)\n",
|
||||
"\n",
|
||||
"# load the audio processor\n",
|
||||
"\n",
|
||||
"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
|
||||
" CONFIG.frame_shift_ms, CONFIG.frame_length_ms,\n",
|
||||
" CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, CONFIG.preemphasis,\n",
|
||||
" 60) \n",
|
||||
"\n",
|
||||
"for idx in check_idxs:\n",
|
||||
" MODEL_PATH = MODEL_PATH_TMP.format(idx)\n",
|
||||
" print(MODEL_PATH)\n",
|
||||
" \n",
|
||||
" # load model state\n",
|
||||
" if use_cuda:\n",
|
||||
" cp = torch.load(MODEL_PATH)\n",
|
||||
" else:\n",
|
||||
" cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
|
||||
"\n",
|
||||
" # load the model\n",
|
||||
" model.load_state_dict(cp['model'])\n",
|
||||
" if use_cuda:\n",
|
||||
" model.cuda()\n",
|
||||
" model.eval()\n",
|
||||
"\n",
|
||||
" model.decoder.max_decoder_steps = 400\n",
|
||||
" text = \"Voice is natural, voice is human. That’s why we are fascinated with creating usable voice technology for our machines. But to create voice systems, an extremely large amount of voice data is required. Most of the data used by large companies isn’t available to the majority of people. We think that stifles innovation. So we’ve launched Project Common Voice, a project to help make voice recognition open to everyone.\"\n",
|
||||
"# text = \"Does the quick brown fox jump over the lazy dog?\"\n",
|
||||
" wavs = text2audio(text, model, CONFIG, use_cuda, ap)\n",
|
||||
"\n",
|
||||
" audio = np.concatenate(wavs)\n",
|
||||
" IPython.display.display(Audio(audio, rate=CONFIG.sample_rate)) \n",
|
||||
" ap.save_wav(audio, 'benchmark_samples/CommonVoice.wav')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,783 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/miniconda3/envs/pytorch4/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||||
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"import sys\n",
|
||||
"import io\n",
|
||||
"import torch \n",
|
||||
"import time\n",
|
||||
"import numpy as np\n",
|
||||
"from collections import OrderedDict\n",
|
||||
"from matplotlib import pylab as plt\n",
|
||||
"\n",
|
||||
"%pylab inline\n",
|
||||
"rcParams[\"figure.figsize\"] = (16,5)\n",
|
||||
"sys.path.append('/home/erogol/projects/')\n",
|
||||
"\n",
|
||||
"import librosa\n",
|
||||
"import librosa.display\n",
|
||||
"\n",
|
||||
"from TTS.models.tacotron import Tacotron \n",
|
||||
"from TTS.layers import *\n",
|
||||
"from TTS.utils.data import *\n",
|
||||
"from TTS.utils.audio import AudioProcessor\n",
|
||||
"from TTS.utils.generic_utils import load_config\n",
|
||||
"from TTS.utils.text import text_to_sequence\n",
|
||||
"\n",
|
||||
"import IPython\n",
|
||||
"from IPython.display import Audio\n",
|
||||
"from utils import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[0m\u001b[01;31mcheckpoint_33464.pth.tar\u001b[0m \u001b[01;31mcheckpoint_36096.pth.tar\u001b[0m\r\n",
|
||||
"\u001b[01;31mcheckpoint_33840.pth.tar\u001b[0m \u001b[01;31mcheckpoint_36472.pth.tar\u001b[0m\r\n",
|
||||
"\u001b[01;31mcheckpoint_34216.pth.tar\u001b[0m \u001b[01;31mcheckpoint_36848.pth.tar\u001b[0m\r\n",
|
||||
"\u001b[01;31mcheckpoint_34592.pth.tar\u001b[0m \u001b[01;31mcheckpoint_37224.pth.tar\u001b[0m\r\n",
|
||||
"\u001b[01;31mcheckpoint_34968.pth.tar\u001b[0m \u001b[01;34mcheckpoints\u001b[0m/\r\n",
|
||||
"\u001b[01;31mcheckpoint_35344.pth.tar\u001b[0m config.json\r\n",
|
||||
"\u001b[01;31mcheckpoint_35720.pth.tar\u001b[0m events.out.tfevents.1526635608.mlc1\r\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ls /data/shared/erogol_models/May-18-2018_02:26AM-los_sen_attn-debug "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def tts(model, text, CONFIG, use_cuda, ap, figures=True):\n",
|
||||
" t_1 = time.time()\n",
|
||||
" waveform, alignment, spectrogram, stop_tokens = create_speech(model, text, CONFIG, use_cuda, ap) \n",
|
||||
" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
|
||||
" if figures: \n",
|
||||
" visualize(alignment, spectrogram, stop_tokens, CONFIG) \n",
|
||||
" IPython.display.display(Audio(waveform, rate=CONFIG.sample_rate)) \n",
|
||||
" return alignment, spectrogram, stop_tokens"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set constants\n",
|
||||
"ROOT_PATH = '/data/shared/erogol_models/May-18-2018_02:26AM-los_sen_attn-debug/'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/checkpoint_37224.pth.tar'\n",
|
||||
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
|
||||
"OUT_FOLDER = ROOT_PATH + '/test/'\n",
|
||||
"CONFIG = load_config(CONFIG_PATH)\n",
|
||||
"use_cuda = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" | > Number of characters : 149\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Tacotron(\n",
|
||||
" (embedding): Embedding(149, 256)\n",
|
||||
" (encoder): Encoder(\n",
|
||||
" (prenet): Prenet(\n",
|
||||
" (layers): ModuleList(\n",
|
||||
" (0): Linear(in_features=256, out_features=256, bias=True)\n",
|
||||
" (1): Linear(in_features=256, out_features=128, bias=True)\n",
|
||||
" )\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (dropout): Dropout(p=0.5)\n",
|
||||
" )\n",
|
||||
" (cbhg): CBHG(\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (conv1d_banks): ModuleList(\n",
|
||||
" (0): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (1): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (2): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (3): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (4): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (5): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (6): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (7): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (8): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (9): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(10,), stride=(1,), padding=(5,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (10): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (11): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(12,), stride=(1,), padding=(6,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (12): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(13,), stride=(1,), padding=(6,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (13): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(14,), stride=(1,), padding=(7,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (14): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(15,), stride=(1,), padding=(7,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (15): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(16,), stride=(1,), padding=(8,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
|
||||
" (conv1d_projections): ModuleList(\n",
|
||||
" (0): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(2048, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (1): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (pre_highway): Linear(in_features=128, out_features=128, bias=False)\n",
|
||||
" (highways): ModuleList(\n",
|
||||
" (0): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (1): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (2): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (3): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (gru): GRU(128, 128, batch_first=True, bidirectional=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (decoder): Decoder(\n",
|
||||
" (prenet): Prenet(\n",
|
||||
" (layers): ModuleList(\n",
|
||||
" (0): Linear(in_features=400, out_features=256, bias=True)\n",
|
||||
" (1): Linear(in_features=256, out_features=128, bias=True)\n",
|
||||
" )\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (dropout): Dropout(p=0.5)\n",
|
||||
" )\n",
|
||||
" (attention_rnn): AttentionRNN(\n",
|
||||
" (rnn_cell): GRUCell(384, 256)\n",
|
||||
" (alignment_model): LocationSensitiveAttention(\n",
|
||||
" (loc_conv): Conv1d(2, 32, kernel_size=(31,), stride=(1,), padding=(15,), bias=False)\n",
|
||||
" (loc_linear): Linear(in_features=32, out_features=256, bias=True)\n",
|
||||
" (query_layer): Linear(in_features=256, out_features=256, bias=True)\n",
|
||||
" (annot_layer): Linear(in_features=256, out_features=256, bias=True)\n",
|
||||
" (v): Linear(in_features=256, out_features=1, bias=False)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (project_to_decoder_in): Linear(in_features=512, out_features=256, bias=True)\n",
|
||||
" (decoder_rnns): ModuleList(\n",
|
||||
" (0): GRUCell(256, 256)\n",
|
||||
" (1): GRUCell(256, 256)\n",
|
||||
" )\n",
|
||||
" (proj_to_mel): Linear(in_features=256, out_features=400, bias=True)\n",
|
||||
" (stopnet): StopNet(\n",
|
||||
" (rnn): GRUCell(400, 400)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (linear): Linear(in_features=400, out_features=1, bias=True)\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (postnet): CBHG(\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (conv1d_banks): ModuleList(\n",
|
||||
" (0): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(1,), stride=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (1): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (2): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (3): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (4): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (5): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (6): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (7): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(80, 80, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
|
||||
" (conv1d_projections): ModuleList(\n",
|
||||
" (0): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(640, 256, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(256, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" (activation): ReLU()\n",
|
||||
" )\n",
|
||||
" (1): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d(256, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (pre_highway): Linear(in_features=80, out_features=80, bias=False)\n",
|
||||
" (highways): ModuleList(\n",
|
||||
" (0): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (1): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (2): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (3): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (gru): GRU(80, 80, batch_first=True, bidirectional=True)\n",
|
||||
" )\n",
|
||||
" (last_linear): Linear(in_features=160, out_features=1025, bias=True)\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# load the model\n",
|
||||
"model = Tacotron(CONFIG.embedding_size, CONFIG.num_freq, CONFIG.num_mels, CONFIG.r)\n",
|
||||
"\n",
|
||||
"# load the audio processor\n",
|
||||
"\n",
|
||||
"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
|
||||
" CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis,\n",
|
||||
" CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, griffin_lim_iters=30) \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# load model state\n",
|
||||
"if use_cuda:\n",
|
||||
" cp = torch.load(MODEL_PATH)\n",
|
||||
"else:\n",
|
||||
" cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"model.load_state_dict(cp['model'])\n",
|
||||
"if use_cuda:\n",
|
||||
" model.cuda()\n",
|
||||
"model.eval()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### EXAMPLES FROM TRAINING SET"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"df = pd.read_csv('/data/shared/KeithIto/LJSpeech-1.0/metadata_val.csv', delimiter='|')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Hosty took the necessary steps to have the Dallas office of the FBI, rather than the New Orleans office, reestablished as the office with principal responsibility.\n",
|
||||
" > Run-time: 9.547307014465332\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,UklGRmT7BQBXQVZFZm10IBAAAAABAAEAIE4AAECcAAACABAAZGF0YUD7BQD7//f/+/8AAP7/7//h/+T/8f/2/+z/5v/y/wYAEwATAAwACAAOABYADgD4/+f/6f/1//b/6//p//j/BwAHAP///f8FABAADgADAPv/+P/8/wIABwABAPP/7v/7/wwACgD2/+f/8v8JAA4A+//r//r/FgAgAA8A/v///wkACgD7/+r/6v/7/wYABQD//wAAEQAfABYA+f/n//D/AQABAO7/3v/h//L/BAAMAAkABwAQABwAHwAZABUAFwANAPn/5//l//P//v/1/93/z//i/wQAFAAGAPr/BAAYACEAHgAVAAwACAAJAAcA+v/n/+v//v8FAPL/2f/e//n/EQAZABIACQAQACcANQAlAAAA6f/0/wUAAADl/9D/2//5/wwAAQDw//P/CAAbABIA/P/w//j/CQAQAAYA8P/n//z/HQAhAAIA7v/8/xQAFAD4/9z/4/8EABIA8//C/7f/5P8VAA4A0v+v/9//OgByAFQADgAAAD4AcwBRAPT/vP/a/wwA/P+t/3r/n//1/xwA/v/a//X/QwB1AFcAFwAAAB8AQwAxAPH/zP/f//z/9P/O/7f/x//1/xsAFQD1//X/KABXADsA8P/Q//7/KwAVANP/q//H/wUAIwD5/7z/z/8jAFIALAD6/wIAJwAdAPD/1//i//P/7//U/77/zP8AACsAGwDt/+//JwBRAEUAJAAdACYALQAuABUA5f/D/8D/wf+m/4L/h/+4//H/GwA7AFoAdwCQAKUAoAByADgADQDf/5n/XP9U/3f/gv9b/0v/j/8MAHAAiAB9AI8AywD0ANMAdgAQANL/oP9f/zb/Tf+H/6D/l/+g/+D/LQBGABwA7v8OAHQAugCpAHEAXQBxAGQA/f9s/yb/XP+3/8b/kP+I//b/gACeAEQA1P+i/7r/9/8nAC4ALwBhAK0AsABKAMr/d/9a/1r/av91/4n/2v9wAAUBJQGrAAcAuv/J/9z/tP9t/2X/zP9WAKoArgCCAEYA9v+d/0X/+f7V/gT/kf8UADAASADjAMYBHAKPAb4ARQAoAPX/Yf+r/kf+YP68/h//h//c/wsAJABYAKwA7gD7AMIAYgA2AIcA3wCcANH/Hv/9/jD/Q/8F/7T+wf5m/1sA+QAaAT0BvgEwAvMBDwE4ANf/h//u/ir+w/3//Xr+zv4e/7z/iQADAQoBDQEwATsB/gByAMn/Yv+E/+n/8/+T/3v//v+RAKEATwAXACgAPADz/23/Jf9Z/6//x/+8/9P/HwB+AMYAwgCFAEkACwCy/2P/UP9T/y//EP9w/z4A5QD7AKkAkgAIAYsBXgFmAGn/Hf9a/3j/HP+B/kT+uv55/+v/AQBMAOsAOAH3ANAAKwF9ASUBQQB6/z3/fP/G/4z/0f5G/oL+Vf8QAE8AOgAqAFQAuwAYAQoBpwBtAIMAewAWAKb/gP+I/3P/Yf9+/5T/ev96/9L/TQB7AEIAFQAuAG4AjABVAAMA0f+7/8H/0P/k/9b/u//1/3EAwQCoAHQAcACBAHwAMACL/+X+wf4V/zD/vP5n/vv+HQDLAKsAcwDwAOEBSAKzAdYAiQC1AIUAkf+X/nT+8/4K/27+8f04/ij/BwBTAEMASQDHAI8BBwLMAQoBiQCOAJIAKgB8//n+wP7V/hT/Nv86/07/qf8eAGkAiQCkAOsAGwHfAFEA7//f/9L/h/8A/8f+HP+R/9b//v9WAOkATAE8ARYBHQEHAYQAw/8r/9j+uv7G/tj+zP7h/lT/FQDKABwBJwEaAQgB7wDJAIkAGQCQ/zj/If8a/xf/I/9N/5P/zf/4/zwAogD5AAMB0gCpAJUAZgAEAI7/QP8l/wr/0v61/uv+TP+e/+X/SQCyAOsAAAEMAT4BiAGMARMBUgDX/9D/1/9f/3D+tf3C/XL+DP9a/6X/PgApAdIB7AHGAcEBygFVATIAKP/2/kT/PP+f/iD+gf6U/6AABAHRAJ0A8gCHAYgBzgD7/43/V/8b/83+rP6y/sv+Kv+a/+T/DwBmAPIAUwFFAeUAjgBaAEMALQDb/0z/2/7o/jv/V/8r/xj/aP+y/6//lv+9/yYAYABQADkAOQBoAKUArgBnAPH/xP/v/xcA5v+a/8H/GwBnAI0AkgCkANoAIAEnAcgAQwASABsA0f9O/wb/Jf+M/9H/7f/8/xUAiQAbAU0B4QBRAG0ADwFLAdEATAAzAEwANwDX/17/5v6V/ov+f/5o/n/+wf4s/5L/1v/m/9j/BwBAABcAtf+b/7v/mv9Q/zj/Xf9i/zT/K/9U/4P/nf+Y/3D/bf/r/4MAlABaAJAASAG0AXEBEAH4AMgANwCW/1f/Tf8g/wD/Pv8JAPoAlwH9AWwC6QILA7wCPwLSAUABWACj/0L/CP/z/g//cf/K//7/WwAJAbgB5QGBAR8BLwFPAeAA+v8x/8r+Y/7N/RT9jPyk/AH9O/0y/VH9Hv7z/k//Zv+d//P/5P99/zj/Hf/q/ov+LP75/e79Ef5z/u/+Qf9f/4f/5/+TAC8BTgEgARQBfAENAjUC5gGRAaMB/QEmArwB+QCSALkABAHbAGMAYAAKAcMBAgI7AtwC0AONBLcErASgBKEEoAQ9BD4D4wHNADYA0/8d/wj+P/3+/CX9X/1o/W/9a/1n/W79cP0v/XT8wvtn+0P7DPuH+k/6vvqa+3j83/wh/af9cf40/2f/Cv++/uL+IP/1/mv+Mv56/tL++P4l/7X/lgB9ATsC2QJMA6gD8APdA18DrAL/AXMB/ADKAPsAWwHfAXcCUQNuBJcFugaIBxUIaAjSCGoJnwlrCcMICQhtB44GbAX4A08CdwCV/uD8LPtZ+Yb3HvYg9Ur00fMM9BT1MPYV9zz49fkd/Mn9sf75/tD+pf5g/t/9uvxQ+3n6W/pg+tn5iPkp+mv7X/yc/PP8AP6D/28AWAAFAFkAQwGlAUMB4ABFAS8CnQKXAroChAOrBFgFWAUiBZEFxAbgBzEIIwjZCJcKeQyuDXsOrA8tESkSGRI3ETMQCA8LDdcJtgWTARr+7fpP91Pzv+947ZfsQuwG7CHsRO3Q79byU/Vr99H5tPwE/9n/qP9k/yn/Mv5g/FD6yfi294T2qfVO9aX1hfZ098X4RfoK/Ar+gv89AIAA3wAhAWYA4v6g/Wf9lv1v/aX9Bf94AfID0gWzB+cJ+gsbDU4NlQ2uDmgQuBGXEgQU6RbEGr0dTB/9H4EgeyCKHnQapBTiDYkGaP7V9WXtF+YR4Dzb79eG1qDXv9oW3yzk1+mA8M73b/6kA54HyAraDMUMuwrtB9gE+ADr+3f20PGJ7lXs6eoO6r3puuqR7a7xp/W8+E/7//1HAEkBUAGSAG//xv3N+9H69fpP/D3+YABLA+0GaQuuD9wSJhVMF9oZGRy0HdUehCDtIoElFigCKjkreiuKKkco/yN5HfsUBQvx/9vzlOc73EHS2clwwwnAPMCBw4LJItLS3IHogPR0AMoLbxV8HK8gziEZICscfxb7DrAF0/ub8rrqEuSV3sXa69hY2ZTb7N5e4yLp7e/r9er5kPz8/kcB5wFzADL+VfzQ+zj8df3s/1QDIQiBDSgT5hjhHYMi6iWOKKkq9it7LZwu/y82MXIxdTHmMDQwAC55KBEg3RVKCyP/LfCT3yDQMMRauuCxA6xHq/qwRLpOxSjS9+GH9I0GyxX1Ifgr2jPNN6A2DDGuKNkeoxPFBjL55etP4EPXntBLzInJ3sgfyw7QhtYQ3ZnjXeoL8Yr2kvqH/VL/NQBXAKIA1wHUA5wGIgrDDnYU1xoaIYYmmisJMHQzkzVrNnY3TjhsOOI32TYjNsw0STIOLkIn6R3QEQgEGfWZ5CDTksF1stCnZ6HEnqiftaXFsQDCG9QC54f7yhAKJC8zGz7xRU9KxEmXQ8Y4TSvVHPANEP5r7XHdLdDCxjjAobs7ufy5573Iw4vKHtIt20jkBuzo8X/2n/sRABEDygRCBrsJrA4jFBQZIx7oJCAt5jR4Od47RT6VQRtEEEPdP+M8UTvWOSM2tjDaKo0kfRwBEbsCnPNV5JLUs8MJszuleJwQmbqZ8p0spuKzCscu3RT0NQpdH1IzYER6USFZu1oQV/1O5EPRNBciIg4b+wXq2tgsyOm6zLJMrzGtLazrrYCzjLwzxiXPedcP4H7p8fFg+Gz9hQKeCAoOjBK5F9oezCeNLwE2MDywQsRIVEuOS75Kp0mvR8RCFz0gOOczyi5LJwwfgha0DAMAFvGx4TfSsMLgskOlP5uslW2VPZmmoRiunr9g1rjuqAbeHLgyW0dAV0NhR2VLZYRg/VXsRn00IyGjDKb3BeOYz5e/cLLRqCKibZ7bnrKhMKetrga4KcPezVLYXuI87Bb2+f5VB90OARYOHjInxzDyOLo/8UbATuxU3lZtVY1T5lFBTklHzT5+N2MxRSq6IakYlQ/DBfv5WOx33QTOLL/hsZKlt5p5k02SuZfWoLesNr0i0+vslgaCHkg1UkonXDtoE253blhqKmLEVE1CmyzGFigCk+0l2LPD3LJtpnqdxJZek1uTuZbanMikZq80uwrIh9QC4HHsEPkdBvUQ6hmzI4Yu7TmHQl5JPVC6V9Zdil//XWZaaFcwU8BLHUJBNyQveSf4HdUS7AY6/bHyeOVT1njHSLu+r3Ck35pSlZuVXJo+o16wmsEe1xrvfgitIQ45sU2JXjFreHIpdB9wR2dbWiRJBzVnHoQHQfGW25XHf7R/pDKYEJAAjOiJ/Yo/j0+XoaKyrua7vMlu2NfnOPY5BKoRmB7SKpg16z9VSdtR7lhSXotiGmRLYwpgvFp5VExM6UJYOPwsMCJPFx8MKQAA9FXoIdw5z//BYLbjrF2k+J2EmsmbIaKqq6a5OcvY3zL3rg4WJzcLine truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1728 with 5 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = df.iloc[175, 1]\n",
|
||||
"print(sentence)\n",
|
||||
"model.decoder.max_decoder_steps = 250\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Comparision with https://mycroft.ai/blog/available-voices/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 5.965982913970947\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1728 with 5 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.\"\n",
|
||||
"model.decoder.max_decoder_steps = 250\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap, figures=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 1.3593213558197021\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1728 with 5 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"Be a voice,not an echo.\" # 'echo' is not in training set. \n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 3.3218443393707275\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,UklGRkwXAgBXQVZFZm10IBAAAAABAAEAIE4AAECcAAACABAAZGF0YSgXAgAuAB8ACwAEAAgAPwAZAPL/CwAhAC4ABADh/97/CwAhAO//9v///xMAEwABAPv/8P9BACUACQArAPr/SgB2ADMAHAACACUAIwD5/xQABgAGAAsAzP/B/+L/4//Y/8j/q//R/+r/0//F/77/CwAAAM//7f/w/xcADwDo/+v/+/8YABQACgABAAcAGwD4/wAALQAjADIAKQAeADwAPAA2ACkAJAA7AEkATQBkAGIAWAA7AD8AiQB0AFwAYQBKAGkAaQBDADkAMQArAO//x/+//77/wP+K/3L/aP9L/0D/Jv/6/iD/IP8w/0n/Ev82/zL/Rv9z/17/gf+4/9L/7v8bAPj/6P8LADAAZQBZAI8AfAB0AK8AaQCIAI8AhgCfAHIAowDJALwAswBwAEwAgAC+ALIAqwC7AJ0AjACAAIkAogCWAJYAcABeAJMAiACKAIUAMgAyADkAJwAhABMA9/+C/6T/iv9C/6//TP8R/w//y/7w/sX+4/7Z/oH+kv5p/o7+pv6s/t7+bv6J/n/+XP4c/yX/Mv89/yP/MP8m/5j/5v8DABMAHgA1AGAA0QDlANwAFgEGATwBvgH9AQUCzAHPAdcBoQG/AQUCEwLjAZsBFAHZABEBCgEAAckApgC9ALAAvQDRALQAmgCZAIoApwC7ANQA2gCgAIAARgApAFcAgABXAOv/b//+/u/+BP/O/rP+h/4V/qL9GP3b/Ov84fzC/Kz8Y/xA/G38efy2/ND89Pxn/bT9Kf7M/l7/uP8AAIUA4gA2AbUB4QHEAdwBNgJaAjYCDALIASIB1gC8AGIAdgBhABoAp/8K/1f/SP87/8H/rP88AFcAfAAPAeoA2QGJAtgCLwNbAy0EhASZBLwE3gR0BbsFvwWvBZcFrQVMBRIFxASLBC4EYAMfA2YC6wEWAcT/L//j/ST9gvxR+w773Pmr+Pj37vYW95j2vvW39ez0ufTB9HX0gvXL9cD1LvZI9kT3Dvih+Fv5TvqV+xj9Pf4k/3AABAEVAj8DhgTLBUMGBgd5B0oILAglCJcIlwgsCXMIwQhLCRAJDwm6B+YHfwiVCAoJpAg/CeQJ3AmxCV4J0gk6ClQKFApPCgoLXwuLCq8IZAf5BuEGbQUXAzUBsP9T/mn7Yvgu9qPzQvHg7UfrBupy6MnmPOSf4qvi/OIJ44DiFOMU5XznfOmt6wPvo/Iu9vX4LvwkANgDZAfvCeMMWBD6EsgU3RSDFaQW3RbdFgoVwRMPEiUP4Qw8CicJ2wdMBc8CnP+s/Xb8k/sH/GX8tP1J/+cANAOTBLEF8QYFCZwMJBBPEyMWAhgDGQIZmxh6GOEXXxazFOsSfRGlDx8MlQe/Aez7x/aT8avsxOdx4+Xf8Nuz17DTrNCkzozMGstgy7fN5dDZ0/DW7Nrn373kxum976v2gP4zBc4KpRBuFi8ctB+3IVYkoyZqKPknTyapJMMhCx6tGCkTuA4BCpgFZQDe+yH5F/aD89fwaO/q7/Dwb/PM9gL7yv9kBPUIJw2+Eb0Wvhs1ILAj8SbtKMMpAip5KS8p4ycPJdQg8RqTFfsQzAuZBWX+l/dw8R/qH+K+2nTUuc8Gy4bGPcO0wAm/X70PvP6877+aw/7GXsoL0ALYCeD+5vPsavNe+9wDtgxlFZ4cXyILJtsoKywTL4oxJjKsMOQt+SkbJjciwR2oF0sQIQnNA2UASfx49+/ypO837qns3OsS7cDvLvRR+Lf8PgJWCPQOEhR6GDgd4yKRKa4ukDEXMkox+zBnMJQvzy36KdMkmR3iFeoOyAilA2/80PL/5yvf29mO1M/NXsX3vW25GrbWtIy0orR5tLey7bLYtnu+pcc9zgTTDdjp323q9fSI/mEG2g2dFBsbsiIOKoMwyjOdMxczxzKXM2c0azJkLgAowyDPGoEVWBFBDdcHBAKs/Pv32PQC8zXyLvO28+D0Z/dM+hv/FQRPCfUOlRNMGR8f/iMXKMQqmS2+L6EwhTCUL9stGit8J6IiTR1iFx8RigqMAm/6svLu6ofjL9xs1dnOH8eFv6O5m7XVs7CyQbFtsK6vg7COsk21Y7uIw4bL5dGv1vfd4ef18RP7YgIECo0S8Ro2Ilwn0SsVME8zczSCNNI1Ojf1Na4wmSpzJzglcSEkG+cUpRAEDFoHZQJp/2T+3Pwo/Ov68fu4/uz/zAGNA2wI2A92FBYYKhrMHFgh8yO3Jl8pRiqJKtknHyUuJLghch7OGJsSyw3qBxUC//q+8hjrvuMe3m3YLdKpzDXGEcDKuiu3BbbjtEG05bPIsz21hLj2vGTB1sXWyp3Rl9kz4qDqbfHs93r/0wfvD1sWdRxXIoInpyvTLYUwwTK/Mwsz4zB1MLQvOSxBJwYjCSHdHg4aXBQGEFYNEQtqCFoGyQVWBYMERgNPA2wFMwitCvML1A3KEAYUuBaxGAUaeBsXHcAdzx37HIUcEhwyGc8UKBGUDnELWAZ8/zb6u/XQ8MnrwuQJ37PZoNSh0AfMRMmDxjfDvcCUvu++8L9QwCLCpMPfxsHKTM7J01vYw93243rpVfBp9mH8EgOfCDMOPxPGF4Ic6yBbJBYnMyg/KCsptimQKlkq2Ci8JqcjRCGFHxIeWBvNFzMViBNbEgUQuw38DD0MeAtACgUKFws8C60LTgvrCwwOwA+kEVYRoRAJEbQQ9hBdEVYRFxGoDm0LlQgnBrwEYgKW/uT64PYg8qXtYOpp6EflCeFr3UXagdfu1ODS+NFz0dHQStC0zzDQONIj1EHWLtiG2s7e/+EV5XHoD+x18Wz26fpn/toAnQQvCacNghFiFJwWnxjbGj8csx3sHkAgHyHmH/4eZh6ZHlUeYhxhGsgYFhjWFhkVRRMDEmURwA9DDlMNQQ36DIELyAqgCowKqApACqAJWAl+CcwJUwkACKIHmweRBocFegQzA3oBBACp/hH93vov+Bn35vQ18n/wsu1A7F/rnemW57DkNOO641XjdeLy4YThAeJv4ofizuN/5VDnD+mQ6brqNu1V8C/0R/aH9kb4k/pi/XUAuAKJBesGMAcYCcwKOgx9DlkQqBAkEDsQlBGfEw8TMhPEE9USixMYE7sSqRI6EYgRZxEdEDAQ1g+QDuYMhQvMCyIMxwoeCTgI2Ae9B/0GigUABT8FMAU5BEsCPwGZAUACswHw/yb+C/0V/X78NPvK+vz5pPjs9g/14/QE9Rr0JvMb8hDx0/C28EbwaPDV7zjv9u808BjxD/Jp8d3xSPJR86r1pfV59u/3E/iJ+WT6sfu0/TP+rP7K/74AjAHJAj0DFAQ8BQEGgQZ9BUEG+wfbBygIYQgsCFwIAwnnCY4J5wgICbEJmAl4CdMJbQnGCcAJqQjhB0kIlwkVCVwH4gbcBqUGgQYHBg8FjwSlBCcElgK/AfkBegEFAVkA+f8H/8D9kf2g/BX89PwR/Rr7XflX+Yz6hPrI+Dz56Plx+N33L/jJ+Eb5R/j096P4tPhC+WT5y/gc+eL5+vor+5b6p/r9+9X9Bv0V/Hz9wf5Y/7r+r/5UAFIAAADkAFcBSAE2AekBXQIWAhgCkAIeAyEC3AGcAzYDAwPtAmAC0AOiAnID5gRkAqsCoQMoBIUEvwOUA94CAwPhBFkF1QNFAhID/ANNA4ADiAMyA88CqwJIAt0BFwLzAZUBEQFRAV4BaQBY/3//uQDs/yv++v19/rr+4P2B/aP90fyf/CT9xPy/+xP9gP3J+477eftp/MH9Rf2G/G/8xvv5+4P9Ev60/Yf9LP2c/cn9zf1P/zT/xP7q/fX+9QCOAIoAEv+x/ngBRAJaAUIBcQBIATABvwCbAgMCoAHXAPn/QAJJAkwAQQE0AZgALgLrAIUAtAExAQ8B2/8rAaoC5AFvAVwA5QBCAdcBmQJOAgwCFAFAAJ8A1AKJA/kB1f/J//UBOAH2/1MAtwHlANv9of6nAEcA3f9I/yD+Yf7k/pMArQD6/eb9F/9+/2H/M/6E/34AvvwF/r//T/4VAHb9OP3O/1T9cP7G/p3+bAAW/Kz8XQCI/0AAI/4e/kP/7/01AMoBIwAk/5b/Jv+k/wgBqAHLABT/xP/bAQUB+f96AQkBP//y/74BWQLcAEr/GwAOAQwDeAIU/7r+t/9DAtsCpf8BACIAVf9JARICugFn/c79swMQA8z+rf5KAYIAvQCJAWUByABz/9QBzgD6/UABqAIPAvb/uP0x/04B1gCZ/5QAcP/N/h3/Uf8q/0z/jP8zAFL+6/xYAeH+PP3g/kIAIQCj/ZL9n/5rAZn/m/42//f+D/+C/qD/8wAuAAYAqP9C/KH/BgOSAKf+p/0+ALsCY//k/S8BHwFd/8v+Zv9WAUoCPQAz/7j/Ef8+AscB/P8jAAT/cwIgAoUA+v4nAOwCKgGRALn/VwGLAQ7/nf72AOwCLwDJ/jv/p/7BACwBBQEhADz+jP57AH4C3f/r/8n+4v+GA3sAOv6W/k4BhAIOAfz+Sf+NAJb/QAIPAN38/f/LAS4Bq/65/vT9CADBAOr+hP+m/qr/mP0T/koDd/9f/Ab/Ov/X/9j+DgHY/x77AwD0AvYA8vxZ/fwAQABeApX/Lf5B//MAcgM0/M/9BQQMAwv/Av1HALQAwgJaAvD7J/1nBQsFW/5J/XwAWwEKAYUD6AEN/gv+6QKLA8z+Q/9CAoICpf7o+44BIASd/pn92/7PAKgBb/9S+y38NwNKAv3/Qvzc+eMC2gKR/QIAlgCzAET+ZPuw/9QFzgNE/Vv9Zv4cAhcCKgBoAvT8If4RBOoBfP0g/6kAUf+VABcBwQFs/In7QAKwA5QBAvvR+/wBygEoAPn+a/+T/Qr/TwLxAAD/of68/kMAwwKhAbj+TfxWAjEFRwGS/439jQBSAY4FZQRG/Vn/t/6BA/QLine truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1728 with 5 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"The human voice is the most perfect instrument of all.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 3.3077054023742676\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
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|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1728 with 5 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 3.859889507293701\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"This cake is great. It's so delicious and moist.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Comparison with https://keithito.github.io/audio-samples/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
|
||||
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,52 +0,0 @@
|
||||
import io
|
||||
import librosa
|
||||
import torch
|
||||
import numpy as np
|
||||
from TTS.utils.text import text_to_sequence
|
||||
from matplotlib import pylab as plt
|
||||
|
||||
hop_length = 250
|
||||
|
||||
|
||||
def create_speech(m, s, CONFIG, use_cuda, ap):
|
||||
text_cleaner = [CONFIG.text_cleaner]
|
||||
seq = np.array(text_to_sequence(s, text_cleaner))
|
||||
chars_var = torch.from_numpy(seq).unsqueeze(0)
|
||||
if use_cuda:
|
||||
chars_var = chars_var.cuda()
|
||||
mel_out, linear_out, alignments, stop_tokens = m.forward(chars_var.long())
|
||||
linear_out = linear_out[0].data.cpu().numpy()
|
||||
alignment = alignments[0].cpu().data.numpy()
|
||||
spec = ap._denormalize(linear_out)
|
||||
wav = ap.inv_spectrogram(linear_out.T)
|
||||
wav = wav[:ap.find_endpoint(wav)]
|
||||
out = io.BytesIO()
|
||||
ap.save_wav(wav, out)
|
||||
return wav, alignment, spec, stop_tokens
|
||||
|
||||
|
||||
def visualize(alignment, spectrogram, stop_tokens, CONFIG):
|
||||
label_fontsize = 16
|
||||
plt.figure(figsize=(16, 24))
|
||||
|
||||
plt.subplot(3, 1, 1)
|
||||
plt.imshow(alignment.T, aspect="auto", origin="lower", interpolation=None)
|
||||
plt.xlabel("Decoder timestamp", fontsize=label_fontsize)
|
||||
plt.ylabel("Encoder timestamp", fontsize=label_fontsize)
|
||||
plt.colorbar()
|
||||
|
||||
stop_tokens = stop_tokens.squeeze().detach().to('cpu').numpy()
|
||||
plt.subplot(3, 1, 2)
|
||||
plt.plot(range(len(stop_tokens)), list(stop_tokens))
|
||||
|
||||
plt.subplot(3, 1, 3)
|
||||
librosa.display.specshow(
|
||||
spectrogram.T,
|
||||
sr=CONFIG.sample_rate,
|
||||
hop_length=hop_length,
|
||||
x_axis="time",
|
||||
y_axis="linear")
|
||||
plt.xlabel("Time", fontsize=label_fontsize)
|
||||
plt.ylabel("Hz", fontsize=label_fontsize)
|
||||
plt.tight_layout()
|
||||
plt.colorbar()
|
||||
Reference in new issue
Block a user