mirror of https://github.com/coqui-ai/TTS.git
Update notebook compat
This commit is contained in:
Eren Gölge
parent
bfc6ceac29
commit
1de010acd4
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@ -2,16 +2,14 @@
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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 for WaveRNN training."
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]
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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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"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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@ -25,22 +23,23 @@
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"from TTS.tts.datasets.TTSDataset 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.utils.io import load_config\n",
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"from TTS.config import load_config\n",
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"from TTS.tts.utils.visual import plot_spectrogram\n",
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"from TTS.tts.utils.generic_utils import setup_model, sequence_mask\n",
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"from TTS.tts.utils.helpers import sequence_mask\n",
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"from TTS.tts.models import setup_model\n",
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"from TTS.tts.utils.text.symbols import make_symbols, symbols, phonemes\n",
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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']='0'"
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]
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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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"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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@ -52,20 +51,20 @@
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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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],
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"outputs": [],
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"metadata": {}
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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/erogol/gdrive/Datasets/non-binary-voice-files/tacotron-DCA\"\n",
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"DATA_PATH = \"/home/erogol/gdrive/Datasets/non-binary-voice-files/\"\n",
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"DATASET = \"sam_accenture\"\n",
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"METADATA_FILE = \"recording_script.xml\"\n",
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"CONFIG_PATH = \"/home/erogol/gdrive/Trainings/sam/ljspeech-dcattn-April-03-2021_05+02-2344379/config.json\"\n",
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"MODEL_FILE = \"/home/erogol/gdrive/Trainings/sam/ljspeech-dcattn-April-03-2021_05+02-2344379/best_model.pth.tar\"\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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"METADATA_FILE = \"metadata.csv\"\n",
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"CONFIG_PATH = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/config.json\"\n",
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"MODEL_FILE = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/model_file.pth.tar\"\n",
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"BATCH_SIZE = 32\n",
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"\n",
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"QUANTIZED_WAV = False\n",
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@ -78,56 +77,63 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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"if 'characters' in C.keys():\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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"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
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"# TODO: multiple speaker\n",
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"model = setup_model(num_chars, num_speakers=0, c=C)\n",
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"checkpoint = torch.load(MODEL_FILE)\n",
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"model.load_state_dict(checkpoint['model'])\n",
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"print(checkpoint['step'])\n",
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"model.eval()\n",
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"model.decoder.set_r(checkpoint['r'])\n",
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"if use_cuda:\n",
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" model = model.cuda()"
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]
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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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"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.preprocess')\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(checkpoint['r'], C.text_cleaner, False, ap, meta_data,characters=c.characters if 'characters' in C.keys() else None, use_phonemes=C.use_phonemes, phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
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"loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)"
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]
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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.text_cleaner,\n",
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" False,\n",
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" ap,\n",
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" meta_data,\n",
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" characters=C.get('characters', None),\n",
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" use_phonemes=C.use_phonemes,\n",
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" phoneme_cache_path=C.phoneme_cache_path,\n",
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" enable_eos_bos=C.enable_eos_bos_chars,\n",
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")\n",
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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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"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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],
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"metadata": {}
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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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@ -206,42 +212,42 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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],
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"outputs": [],
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"metadata": {}
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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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],
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"metadata": {}
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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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],
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"outputs": [],
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"metadata": {}
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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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@ -249,46 +255,46 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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],
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"outputs": [],
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"metadata": {}
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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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],
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"outputs": [],
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"metadata": {}
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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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],
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"outputs": [],
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"metadata": {}
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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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@ -297,13 +303,13 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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@ -312,13 +318,13 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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@ -328,21 +334,22 @@
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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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],
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"outputs": [],
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"metadata": {}
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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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"source": [],
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"outputs": [],
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"source": []
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"metadata": {}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"name": "python3",
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"display_name": "Python 3.9.7 64-bit ('base': conda)"
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},
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"language_info": {
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"codemirror_mode": {
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@ -354,7 +361,10 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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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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