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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a notebook to generate mel-spectrograms from a TTS model to be used in a Vocoder training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import torch\n",
"import importlib\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
"from torch.utils.data import DataLoader\n",
"import soundfile as sf\n",
"import pickle\n",
"from TTS.tts.datasets.dataset import TTSDataset\n",
"from TTS.tts.layers.losses import L1LossMasked\n",
"from TTS.utils.audio import AudioProcessor\n",
"from TTS.config import load_config\n",
"from TTS.tts.utils.visual import plot_spectrogram\n",
"from TTS.tts.utils.helpers import sequence_mask\n",
"from TTS.tts.models import setup_model\n",
"from TTS.tts.utils.text.symbols import make_symbols, symbols, phonemes\n",
"\n",
"%matplotlib inline\n",
"\n",
"# Configure CUDA visibility\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Function to create directories and file names\n",
"def set_filename(wav_path, out_path):\n",
" wav_file = os.path.basename(wav_path)\n",
" file_name = wav_file.split('.')[0]\n",
" os.makedirs(os.path.join(out_path, \"quant\"), exist_ok=True)\n",
" os.makedirs(os.path.join(out_path, \"mel\"), exist_ok=True)\n",
" os.makedirs(os.path.join(out_path, \"wav_gl\"), exist_ok=True)\n",
" wavq_path = os.path.join(out_path, \"quant\", file_name)\n",
" mel_path = os.path.join(out_path, \"mel\", file_name)\n",
" wav_path = os.path.join(out_path, \"wav_gl\", file_name)\n",
" return file_name, wavq_path, mel_path, wav_path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Paths and configurations\n",
"OUT_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/specs2/\"\n",
"DATA_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/\"\n",
"DATASET = \"ljspeech\"\n",
"METADATA_FILE = \"metadata.csv\"\n",
"CONFIG_PATH = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/config.json\"\n",
"MODEL_FILE = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/model_file.pth\"\n",
"BATCH_SIZE = 32\n",
"\n",
"QUANTIZED_WAV = False\n",
"QUANTIZE_BIT = None\n",
"DRY_RUN = False # if False, does not generate output files, only computes loss and visuals.\n",
"\n",
"# Check CUDA availability\n",
"use_cuda = torch.cuda.is_available()\n",
"print(\" > CUDA enabled: \", use_cuda)\n",
"\n",
"# Load the configuration\n",
"C = load_config(CONFIG_PATH)\n",
"C.audio['do_trim_silence'] = False # IMPORTANT!!!!!!!!!!!!!!! disable to align mel specs with the wav files\n",
"ap = AudioProcessor(bits=QUANTIZE_BIT, **C.audio)\n",
"print(C['r'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# If the vocabulary was passed, replace the default\n",
"if 'characters' in C and C['characters']:\n",
" symbols, phonemes = make_symbols(**C.characters)\n",
"\n",
"# Load the model\n",
"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
"# TODO: multiple speakers\n",
"model = setup_model(C)\n",
"model.load_checkpoint(C, MODEL_FILE, eval=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the preprocessor based on the dataset\n",
"preprocessor = importlib.import_module(\"TTS.tts.datasets.formatters\")\n",
"preprocessor = getattr(preprocessor, DATASET.lower())\n",
"meta_data = preprocessor(DATA_PATH, METADATA_FILE)\n",
"dataset = TTSDataset(\n",
" C,\n",
" C.text_cleaner,\n",
" False,\n",
" ap,\n",
" meta_data,\n",
" characters=C.get('characters', None),\n",
" use_phonemes=C.use_phonemes,\n",
" phoneme_cache_path=C.phoneme_cache_path,\n",
" enable_eos_bos=C.enable_eos_bos_chars,\n",
")\n",
"loader = DataLoader(\n",
" dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Initialize lists for storing results\n",
"file_idxs = []\n",
"metadata = []\n",
"losses = []\n",
"postnet_losses = []\n",
"criterion = L1LossMasked(seq_len_norm=C.seq_len_norm)\n",
"\n",
"# Create log file\n",
"log_file_path = os.path.join(OUT_PATH, \"log.txt\")\n",
"log_file = open(log_file_path, \"w\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate model outputs "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Start processing with a progress bar\n",
"with torch.no_grad():\n",
" for data in tqdm(loader, desc=\"Processing\"):\n",
" try:\n",
" # setup input data\n",
" text_input, text_lengths, _, linear_input, mel_input, mel_lengths, stop_targets, item_idx = data\n",
"\n",
" # dispatch data to GPU\n",
" if use_cuda:\n",
" text_input = text_input.cuda()\n",
" text_lengths = text_lengths.cuda()\n",
" mel_input = mel_input.cuda()\n",
" mel_lengths = mel_lengths.cuda()\n",
"\n",
" mask = sequence_mask(text_lengths)\n",
" mel_outputs, postnet_outputs, alignments, stop_tokens = model.forward(text_input, text_lengths, mel_input)\n",
"\n",
" # compute loss\n",
" loss = criterion(mel_outputs, mel_input, mel_lengths)\n",
" loss_postnet = criterion(postnet_outputs, mel_input, mel_lengths)\n",
" losses.append(loss.item())\n",
" postnet_losses.append(loss_postnet.item())\n",
"\n",
" # compute mel specs from linear spec if the model is Tacotron\n",
" if C.model == \"Tacotron\":\n",
" mel_specs = []\n",
" postnet_outputs = postnet_outputs.data.cpu().numpy()\n",
" for b in range(postnet_outputs.shape[0]):\n",
" postnet_output = postnet_outputs[b]\n",
" mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())\n",
" postnet_outputs = torch.stack(mel_specs)\n",
" elif C.model == \"Tacotron2\":\n",
" postnet_outputs = postnet_outputs.detach().cpu().numpy()\n",
" alignments = alignments.detach().cpu().numpy()\n",
"\n",
" if not DRY_RUN:\n",
" for idx in range(text_input.shape[0]):\n",
" wav_file_path = item_idx[idx]\n",
" wav = ap.load_wav(wav_file_path)\n",
" file_name, wavq_path, mel_path, wav_path = set_filename(wav_file_path, OUT_PATH)\n",
" file_idxs.append(file_name)\n",
"\n",
" # quantize and save wav\n",
" if QUANTIZED_WAV:\n",
" wavq = ap.quantize(wav)\n",
" np.save(wavq_path, wavq)\n",
"\n",
" # save TTS mel\n",
" mel = postnet_outputs[idx]\n",
" mel_length = mel_lengths[idx]\n",
" mel = mel[:mel_length, :].T\n",
" np.save(mel_path, mel)\n",
"\n",
" metadata.append([wav_file_path, mel_path])\n",
"\n",
" except Exception as e:\n",
" log_file.write(f\"Error processing data: {str(e)}\\n\")\n",
"\n",
" # Calculate and log mean losses\n",
" mean_loss = np.mean(losses)\n",
" mean_postnet_loss = np.mean(postnet_losses)\n",
" log_file.write(f\"Mean Loss: {mean_loss}\\n\")\n",
" log_file.write(f\"Mean Postnet Loss: {mean_postnet_loss}\\n\")\n",
"\n",
"# Close the log file\n",
"log_file.close()\n",
"\n",
"# For wavernn\n",
"if not DRY_RUN:\n",
" pickle.dump(file_idxs, open(os.path.join(OUT_PATH, \"dataset_ids.pkl\"), \"wb\"))\n",
"\n",
"# For pwgan\n",
"with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n",
" for data in metadata:\n",
" f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")\n",
"\n",
"# Print mean losses\n",
"print(f\"Mean Loss: {mean_loss}\")\n",
"print(f\"Mean Postnet Loss: {mean_postnet_loss}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# for pwgan\n",
"with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n",
" for data in metadata:\n",
" f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sanity Check"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"idx = 1\n",
"ap.melspectrogram(ap.load_wav(item_idx[idx])).shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import soundfile as sf\n",
"wav, sr = sf.read(item_idx[idx])\n",
"mel_postnet = postnet_outputs[idx][:mel_lengths[idx], :]\n",
"mel_decoder = mel_outputs[idx][:mel_lengths[idx], :].detach().cpu().numpy()\n",
"mel_truth = ap.melspectrogram(wav)\n",
"print(mel_truth.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot posnet output\n",
"print(mel_postnet[:mel_lengths[idx], :].shape)\n",
"plot_spectrogram(mel_postnet, ap)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot decoder output\n",
"print(mel_decoder.shape)\n",
"plot_spectrogram(mel_decoder, ap)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot GT specgrogram\n",
"print(mel_truth.shape)\n",
"plot_spectrogram(mel_truth.T, ap)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# postnet, decoder diff\n",
"from matplotlib import pylab as plt\n",
"mel_diff = mel_decoder - mel_postnet\n",
"plt.figure(figsize=(16, 10))\n",
"plt.imshow(abs(mel_diff[:mel_lengths[idx],:]).T,aspect=\"auto\", origin=\"lower\");\n",
"plt.colorbar()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# PLOT GT SPECTROGRAM diff\n",
"from matplotlib import pylab as plt\n",
"mel_diff2 = mel_truth.T - mel_decoder\n",
"plt.figure(figsize=(16, 10))\n",
"plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n",
"plt.colorbar()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# PLOT GT SPECTROGRAM diff\n",
"from matplotlib import pylab as plt\n",
"mel = postnet_outputs[idx]\n",
"mel_diff2 = mel_truth.T - mel[:mel_truth.shape[1]]\n",
"plt.figure(figsize=(16, 10))\n",
"plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n",
"plt.colorbar()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56"
},
"kernelspec": {
"display_name": "Python 3.9.7 64-bit ('base': conda)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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