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https://github.com/wassname/TTS.git
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Update notebooks
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Eren Gölge committed
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@@ -2,14 +2,16 @@
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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 is a notebook to generate mel-spectrograms from a TTS model to be used in a Vocoder training."
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],
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"metadata": {}
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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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"%load_ext autoreload\n",
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"%autoreload 2\n",
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@@ -20,7 +22,7 @@
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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 torch.utils.data import DataLoader\n",
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"from TTS.tts.datasets.TTSDataset import TTSDataset\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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"from TTS.config import load_config\n",
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@@ -33,13 +35,13 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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"def set_filename(wav_path, out_path):\n",
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" wav_file = os.path.basename(wav_path)\n",
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@@ -51,13 +53,13 @@
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" mel_path = os.path.join(out_path, \"mel\", file_name)\n",
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" wav_path = os.path.join(out_path, \"wav_gl\", file_name)\n",
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" return file_name, wavq_path, mel_path, wav_path"
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],
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"outputs": [],
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"metadata": {}
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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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"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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@@ -77,13 +79,13 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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"print(C['r'])\n",
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"# if the vocabulary was passed, replace the default\n",
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@@ -95,13 +97,13 @@
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"# TODO: multiple speaker\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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"outputs": [],
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"metadata": {}
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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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"preprocessor = importlib.import_module(\"TTS.tts.datasets.formatters\")\n",
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"preprocessor = getattr(preprocessor, DATASET.lower())\n",
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@@ -120,20 +122,20 @@
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"loader = DataLoader(\n",
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" dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False\n",
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")\n"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Generate model outputs "
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],
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"metadata": {}
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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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"import pickle\n",
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"\n",
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@@ -212,42 +214,42 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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"# 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\")"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Sanity Check"
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],
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"metadata": {}
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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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"idx = 1\n",
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"ap.melspectrogram(ap.load_wav(item_idx[idx])).shape"
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],
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"outputs": [],
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"metadata": {}
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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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"import soundfile as sf\n",
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"wav, sr = sf.read(item_idx[idx])\n",
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@@ -255,46 +257,46 @@
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"mel_decoder = mel_outputs[idx][:mel_lengths[idx], :].detach().cpu().numpy()\n",
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"mel_truth = ap.melspectrogram(wav)\n",
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"print(mel_truth.shape)"
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],
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"outputs": [],
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"metadata": {}
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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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"# plot posnet output\n",
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"print(mel_postnet[:mel_lengths[idx], :].shape)\n",
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"plot_spectrogram(mel_postnet, ap)"
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],
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"outputs": [],
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"metadata": {}
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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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"# plot decoder output\n",
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"print(mel_decoder.shape)\n",
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"plot_spectrogram(mel_decoder, ap)"
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],
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"outputs": [],
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"metadata": {}
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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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"# plot GT specgrogram\n",
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"print(mel_truth.shape)\n",
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"plot_spectrogram(mel_truth.T, ap)"
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],
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"outputs": [],
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"metadata": {}
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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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"# postnet, decoder diff\n",
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"from matplotlib import pylab as plt\n",
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@@ -303,13 +305,13 @@
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"plt.imshow(abs(mel_diff[:mel_lengths[idx],:]).T,aspect=\"auto\", origin=\"lower\");\n",
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"plt.colorbar()\n",
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"plt.tight_layout()"
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],
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"outputs": [],
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"metadata": {}
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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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"# PLOT GT SPECTROGRAM diff\n",
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"from matplotlib import pylab as plt\n",
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@@ -318,13 +320,13 @@
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"plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n",
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"plt.colorbar()\n",
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"plt.tight_layout()"
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],
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"outputs": [],
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"metadata": {}
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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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"# PLOT GT SPECTROGRAM diff\n",
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"from matplotlib import pylab as plt\n",
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@@ -334,22 +336,23 @@
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"plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n",
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"plt.colorbar()\n",
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"plt.tight_layout()"
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],
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"outputs": [],
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"metadata": {}
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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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"source": [],
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"metadata": {},
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"outputs": [],
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"metadata": {}
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"source": []
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.9.7 64-bit ('base': conda)"
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"display_name": "Python 3.9.7 64-bit ('base': conda)",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@@ -362,11 +365,8 @@
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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},
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"interpreter": {
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"hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}
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