mirror of https://github.com/coqui-ai/TTS.git
Upgrade and Optimize TTS Code in extractttsspectrogram.ipynb (#3012)
This commit is contained in:
Anupam Maurya
GitHub
parent
155c5fc0bd
commit
f133b9d2d7
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@ -13,15 +13,15 @@
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"metadata": {},
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"outputs": [],
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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 torch\n",
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"import importlib\n",
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"import numpy as np\n",
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"from tqdm import tqdm as tqdm\n",
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"from tqdm import tqdm\n",
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"from torch.utils.data import DataLoader\n",
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"import soundfile as sf\n",
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"import pickle\n",
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"from TTS.tts.datasets.dataset import TTSDataset\n",
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"from TTS.tts.layers.losses import L1LossMasked\n",
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"from TTS.utils.audio import AudioProcessor\n",
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@ -33,8 +33,8 @@
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"\n",
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"%matplotlib inline\n",
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"\n",
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='2'"
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"# Configure CUDA visibility\n",
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"os.environ['CUDA_VISIBLE_DEVICES'] = '2'"
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]
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},
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{
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@ -43,6 +43,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Function to create directories and file names\n",
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"def set_filename(wav_path, out_path):\n",
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" wav_file = os.path.basename(wav_path)\n",
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" file_name = wav_file.split('.')[0]\n",
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@ -61,6 +62,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Paths and configurations\n",
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"OUT_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/specs2/\"\n",
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"DATA_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/\"\n",
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"DATASET = \"ljspeech\"\n",
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@ -73,12 +75,15 @@
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"QUANTIZE_BIT = None\n",
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"DRY_RUN = False # if False, does not generate output files, only computes loss and visuals.\n",
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"\n",
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"# Check CUDA availability\n",
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"use_cuda = torch.cuda.is_available()\n",
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"print(\" > CUDA enabled: \", use_cuda)\n",
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"\n",
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"# Load the configuration\n",
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"C = load_config(CONFIG_PATH)\n",
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"C.audio['do_trim_silence'] = False # IMPORTANT!!!!!!!!!!!!!!! disable to align mel specs with the wav files\n",
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"ap = AudioProcessor(bits=QUANTIZE_BIT, **C.audio)"
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"ap = AudioProcessor(bits=QUANTIZE_BIT, **C.audio)\n",
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"print(C['r'])"
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]
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},
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{
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@ -87,14 +92,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(C['r'])\n",
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"# if the vocabulary was passed, replace the default\n",
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"# If the vocabulary was passed, replace the default\n",
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"if 'characters' in C and C['characters']:\n",
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" symbols, phonemes = make_symbols(**C.characters)\n",
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"\n",
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"# load the model\n",
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"# Load the model\n",
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"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
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"# TODO: multiple speaker\n",
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"# TODO: multiple speakers\n",
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"model = setup_model(C)\n",
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"model.load_checkpoint(C, MODEL_FILE, eval=True)"
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]
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@ -105,11 +109,12 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the preprocessor based on the dataset\n",
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"preprocessor = importlib.import_module(\"TTS.tts.datasets.formatters\")\n",
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"preprocessor = getattr(preprocessor, DATASET.lower())\n",
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"meta_data = preprocessor(DATA_PATH, METADATA_FILE)\n",
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"dataset = TTSDataset(\n",
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" checkpoint[\"config\"][\"r\"],\n",
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" C,\n",
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" C.text_cleaner,\n",
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" False,\n",
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" ap,\n",
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@ -124,6 +129,24 @@
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialize lists for storing results\n",
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"file_idxs = []\n",
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"metadata = []\n",
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"losses = []\n",
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"postnet_losses = []\n",
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"criterion = L1LossMasked(seq_len_norm=C.seq_len_norm)\n",
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"\n",
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"# Create log file\n",
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"log_file_path = os.path.join(OUT_PATH, \"log.txt\")\n",
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"log_file = open(log_file_path, \"w\")"
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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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@ -137,83 +160,85 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"\n",
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"file_idxs = []\n",
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"metadata = []\n",
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"losses = []\n",
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"postnet_losses = []\n",
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"criterion = L1LossMasked(seq_len_norm=C.seq_len_norm)\n",
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"# Start processing with a progress bar\n",
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"with torch.no_grad():\n",
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" for data in tqdm(loader):\n",
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" # setup input data\n",
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" text_input = data[0]\n",
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" text_lengths = data[1]\n",
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" linear_input = data[3]\n",
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" mel_input = data[4]\n",
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" mel_lengths = data[5]\n",
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" stop_targets = data[6]\n",
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" item_idx = data[7]\n",
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" for data in tqdm(loader, desc=\"Processing\"):\n",
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" try:\n",
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" # setup input data\n",
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" text_input, text_lengths, _, linear_input, mel_input, mel_lengths, stop_targets, item_idx = data\n",
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"\n",
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" # dispatch data to GPU\n",
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" if use_cuda:\n",
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" text_input = text_input.cuda()\n",
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" text_lengths = text_lengths.cuda()\n",
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" mel_input = mel_input.cuda()\n",
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" mel_lengths = mel_lengths.cuda()\n",
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" # dispatch data to GPU\n",
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" if use_cuda:\n",
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" text_input = text_input.cuda()\n",
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" text_lengths = text_lengths.cuda()\n",
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" mel_input = mel_input.cuda()\n",
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" mel_lengths = mel_lengths.cuda()\n",
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"\n",
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" mask = sequence_mask(text_lengths)\n",
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" mel_outputs, postnet_outputs, alignments, stop_tokens = model.forward(text_input, text_lengths, mel_input)\n",
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" \n",
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" # compute loss\n",
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" loss = criterion(mel_outputs, mel_input, mel_lengths)\n",
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" loss_postnet = criterion(postnet_outputs, mel_input, mel_lengths)\n",
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" losses.append(loss.item())\n",
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" postnet_losses.append(loss_postnet.item())\n",
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" mask = sequence_mask(text_lengths)\n",
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" mel_outputs, postnet_outputs, alignments, stop_tokens = model.forward(text_input, text_lengths, mel_input)\n",
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"\n",
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" # compute mel specs from linear spec if model is Tacotron\n",
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" if C.model == \"Tacotron\":\n",
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" mel_specs = []\n",
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" postnet_outputs = postnet_outputs.data.cpu().numpy()\n",
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" for b in range(postnet_outputs.shape[0]):\n",
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" postnet_output = postnet_outputs[b]\n",
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" mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())\n",
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" postnet_outputs = torch.stack(mel_specs)\n",
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" elif C.model == \"Tacotron2\":\n",
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" postnet_outputs = postnet_outputs.detach().cpu().numpy()\n",
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" alignments = alignments.detach().cpu().numpy()\n",
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" # compute loss\n",
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" loss = criterion(mel_outputs, mel_input, mel_lengths)\n",
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" loss_postnet = criterion(postnet_outputs, mel_input, mel_lengths)\n",
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" losses.append(loss.item())\n",
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" postnet_losses.append(loss_postnet.item())\n",
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"\n",
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" if not DRY_RUN:\n",
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" for idx in range(text_input.shape[0]):\n",
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" wav_file_path = item_idx[idx]\n",
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" wav = ap.load_wav(wav_file_path)\n",
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" file_name, wavq_path, mel_path, wav_path = set_filename(wav_file_path, OUT_PATH)\n",
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" file_idxs.append(file_name)\n",
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" # compute mel specs from linear spec if the model is Tacotron\n",
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" if C.model == \"Tacotron\":\n",
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" mel_specs = []\n",
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" postnet_outputs = postnet_outputs.data.cpu().numpy()\n",
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" for b in range(postnet_outputs.shape[0]):\n",
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" postnet_output = postnet_outputs[b]\n",
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" mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())\n",
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" postnet_outputs = torch.stack(mel_specs)\n",
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" elif C.model == \"Tacotron2\":\n",
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" postnet_outputs = postnet_outputs.detach().cpu().numpy()\n",
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" alignments = alignments.detach().cpu().numpy()\n",
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"\n",
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" # quantize and save wav\n",
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" if QUANTIZED_WAV:\n",
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" wavq = ap.quantize(wav)\n",
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" np.save(wavq_path, wavq)\n",
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" if not DRY_RUN:\n",
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" for idx in range(text_input.shape[0]):\n",
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" wav_file_path = item_idx[idx]\n",
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" wav = ap.load_wav(wav_file_path)\n",
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" file_name, wavq_path, mel_path, wav_path = set_filename(wav_file_path, OUT_PATH)\n",
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" file_idxs.append(file_name)\n",
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"\n",
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" # save TTS mel\n",
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" mel = postnet_outputs[idx]\n",
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" mel_length = mel_lengths[idx]\n",
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" mel = mel[:mel_length, :].T\n",
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" np.save(mel_path, mel)\n",
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" # quantize and save wav\n",
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" if QUANTIZED_WAV:\n",
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" wavq = ap.quantize(wav)\n",
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" np.save(wavq_path, wavq)\n",
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"\n",
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" metadata.append([wav_file_path, mel_path])\n",
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" # save TTS mel\n",
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" mel = postnet_outputs[idx]\n",
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" mel_length = mel_lengths[idx]\n",
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" mel = mel[:mel_length, :].T\n",
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" np.save(mel_path, mel)\n",
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"\n",
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" # for wavernn\n",
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" if not DRY_RUN:\n",
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" pickle.dump(file_idxs, open(OUT_PATH+\"/dataset_ids.pkl\", \"wb\")) \n",
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" \n",
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" # for pwgan\n",
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" with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n",
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" for data in metadata:\n",
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" f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")\n",
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" metadata.append([wav_file_path, mel_path])\n",
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"\n",
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" print(np.mean(losses))\n",
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" print(np.mean(postnet_losses))"
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" except Exception as e:\n",
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" log_file.write(f\"Error processing data: {str(e)}\\n\")\n",
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"\n",
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" # Calculate and log mean losses\n",
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" mean_loss = np.mean(losses)\n",
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" mean_postnet_loss = np.mean(postnet_losses)\n",
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" log_file.write(f\"Mean Loss: {mean_loss}\\n\")\n",
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" log_file.write(f\"Mean Postnet Loss: {mean_postnet_loss}\\n\")\n",
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"\n",
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"# Close the log file\n",
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"log_file.close()\n",
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"\n",
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"# For wavernn\n",
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"if not DRY_RUN:\n",
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" pickle.dump(file_idxs, open(os.path.join(OUT_PATH, \"dataset_ids.pkl\"), \"wb\"))\n",
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"\n",
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"# For pwgan\n",
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"with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n",
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" for data in metadata:\n",
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" f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")\n",
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"\n",
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"# Print mean losses\n",
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"print(f\"Mean Loss: {mean_loss}\")\n",
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"print(f\"Mean Postnet Loss: {mean_postnet_loss}\")"
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]
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},
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{
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