mirror of https://github.com/coqui-ai/TTS.git
remove unused notebooks
This commit is contained in:
Edresson
parent
bc5307caa0
commit
5af505ff33
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@ -1,163 +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 is a noteboook used to generate the speaker embeddings with the AngleProto speaker encoder model for multi-speaker training.\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.\n",
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"- download or clone related repos, linked below.\n",
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"- setup the repositories. ```python setup.py install```\n",
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"- to checkout right commit versions (given next to the model) of TTS.\n",
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"- to set the right paths in the cell below.\n",
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"\n",
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"Repository:\n",
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"- TTS: https://github.com/mozilla/TTS"
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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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"import os\n",
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"import importlib\n",
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"import random\n",
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"import librosa\n",
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"import torch\n",
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"\n",
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"import numpy as np\n",
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"from tqdm import tqdm\n",
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"from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
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"\n",
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"# you may need to change this depending on your system\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='0'\n",
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"\n",
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"\n",
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"from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.io import load_config"
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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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"You should also adjust all the path constants to point at the relevant locations for you locally"
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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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"MODEL_RUN_PATH = \"../../Mozilla-TTS/checkpoints/libritts_100+360-angleproto-June-06-2020_04+12PM-9c04d1f/\"\n",
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"MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
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"CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
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"\n",
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"\n",
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"DATASETS_NAME = ['vctk'] # list the datasets\n",
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"DATASETS_PATH = ['../../../datasets/VCTK/']\n",
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"DATASETS_METAFILE = ['']\n",
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"\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Preprocess dataset\n",
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"meta_data = []\n",
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"for i in range(len(DATASETS_NAME)):\n",
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" preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n",
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" preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
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" meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
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" \n",
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"meta_data= list(meta_data)"
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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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"c = load_config(CONFIG_PATH)\n",
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"ap = AudioProcessor(**c['audio'])\n",
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"\n",
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"model = SpeakerEncoder(**c.model)\n",
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"model.load_state_dict(torch.load(MODEL_PATH)['model'])\n",
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"model.eval()\n",
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"if USE_CUDA:\n",
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" model.cuda()\n",
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"\n",
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"embeddings_dict = {}\n",
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"len_meta_data= len(meta_data)\n",
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"\n",
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"for i in tqdm(range(len_meta_data)):\n",
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" _, wav_file, speaker_id = meta_data[i]\n",
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" wav_file_name = os.path.basename(wav_file)\n",
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" mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T\n",
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" mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
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" if USE_CUDA:\n",
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" mel_spec = mel_spec.cuda()\n",
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" embedd = model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
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" embeddings_dict[wav_file_name] = [embedd,speaker_id]\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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"# create and export speakers.json\n",
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"speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0].reshape(-1).tolist()} for i, sample in enumerate(embeddings_dict.keys())}\n",
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"save_speaker_mapping(MODEL_RUN_PATH, speaker_mapping)\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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"#test load integrity\n",
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"speaker_mapping_load = load_speaker_mapping(MODEL_RUN_PATH)\n",
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"assert speaker_mapping == speaker_mapping_load\n",
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"print(\"The file speakers.json has been exported to \",MODEL_RUN_PATH, ' with ', len(embeddings_dict.keys()), ' speakers')"
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]
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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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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.7.6"
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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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@ -1,212 +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 is a noteboook used to generate the speaker embeddings with the CorentinJ GE2E model trained with Angular Prototypical loss for multi-speaker training.\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 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/coqui/TTS\n",
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"- CorentinJ GE2E: https://github.com/Edresson/GE2E-Speaker-Encoder"
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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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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import importlib\n",
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"import random\n",
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"import librosa\n",
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"import torch\n",
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"\n",
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"import numpy as np\n",
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"from TTS.utils.io import load_config\n",
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"from tqdm import tqdm\n",
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"from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
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"\n",
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"# you may need to change this depending on your system\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='0'"
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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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"# Clone encoder \n",
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"!git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git\n",
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"os.chdir('Real-Time-Voice-Cloning/')"
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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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"#Install voxceleb_trainer Requeriments\n",
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"!python -m pip install umap-learn visdom webrtcvad librosa>=0.5.1 matplotlib>=2.0.2 numpy>=1.14.0 scipy>=1.0.0 tqdm sounddevice Unidecode inflect multiprocess numba"
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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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"#Download encoder Checkpoint\n",
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"!wget https://github.com/Edresson/Real-Time-Voice-Cloning/releases/download/checkpoints/pretrained.zip\n",
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"!unzip pretrained.zip"
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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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"from encoder import inference as encoder\n",
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"from encoder.params_model import model_embedding_size as speaker_embedding_size\n",
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"from pathlib import Path"
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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(\"Preparing the encoder, the synthesizer and the vocoder...\")\n",
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"encoder.load_model(Path('encoder/saved_models/pretrained.pt'))\n",
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"print(\"Testing your configuration with small inputs.\")\n",
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"# Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's\n",
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"# sampling rate, which may differ.\n",
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"# If you're unfamiliar with digital audio, know that it is encoded as an array of floats \n",
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"# (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.\n",
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"# The sampling rate is the number of values (samples) recorded per second, it is set to\n",
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"# 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond \n",
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"# to an audio of 1 second.\n",
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"print(\"\\tTesting the encoder...\")\n",
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"\n",
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"wav = np.zeros(encoder.sampling_rate) \n",
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"embed = encoder.embed_utterance(wav)\n",
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"print(embed.shape)\n",
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"\n",
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"# Embeddings are L2-normalized (this isn't important here, but if you want to make your own \n",
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"# embeddings it will be).\n",
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"#embed /= np.linalg.norm(embed) # for random embedding\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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"SAVE_PATH = '../'"
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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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"# Set constants\n",
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"DATASETS_NAME = ['vctk'] # list the datasets\n",
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"DATASETS_PATH = ['../../../../../datasets/VCTK-Corpus-removed-silence/']\n",
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"DATASETS_METAFILE = ['']\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Preprocess dataset\n",
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"meta_data = []\n",
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"for i in range(len(DATASETS_NAME)):\n",
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" preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n",
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" preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
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" meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
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" \n",
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"meta_data= list(meta_data)\n",
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"\n",
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"meta_data = meta_data\n",
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"embeddings_dict = {}\n",
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"len_meta_data= len(meta_data)\n",
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"for i in tqdm(range(len_meta_data)):\n",
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" _, wave_file_path, speaker_id = meta_data[i]\n",
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" wav_file_name = os.path.basename(wave_file_path)\n",
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" # Extract Embedding\n",
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" preprocessed_wav = encoder.preprocess_wav(wave_file_path)\n",
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" file_embedding = encoder.embed_utterance(preprocessed_wav)\n",
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" embeddings_dict[wav_file_name] = [file_embedding.reshape(-1).tolist(), speaker_id]\n",
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" del file_embedding"
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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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"# create and export speakers.json and aplly a L2_norm in embedding\n",
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"speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0]} for i, sample in enumerate(embeddings_dict.keys())}\n",
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"save_speaker_mapping(SAVE_PATH, speaker_mapping)\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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"#test load integrity\n",
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"speaker_mapping_load = load_speaker_mapping(SAVE_PATH)\n",
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"assert speaker_mapping == speaker_mapping_load\n",
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"print(\"The file speakers.json has been exported to \",SAVE_PATH, ' with ', len(embeddings_dict.keys()), ' samples')"
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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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}
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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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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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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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@ -1,163 +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 is a noteboook used to generate the speaker embeddings with the GE2E speaker encoder model for multi-speaker training.\n",
|
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"\n",
|
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"Before running this script please DON'T FORGET: \n",
|
||||
"- to set file paths.\n",
|
||||
"- to download related model files from TTS.\n",
|
||||
"- download or clone related repos, linked below.\n",
|
||||
"- setup the repositories. ```python setup.py install```\n",
|
||||
"- to checkout right commit versions (given next to the model) of TTS.\n",
|
||||
"- to set the right paths in the cell below.\n",
|
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"\n",
|
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"Repository:\n",
|
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"- TTS: https://github.com/coqui/TTS"
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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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"import os\n",
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"import importlib\n",
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"import random\n",
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"import librosa\n",
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"import torch\n",
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"\n",
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"import numpy as np\n",
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"from tqdm import tqdm\n",
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"from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
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"\n",
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"# you may need to change this depending on your system\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='0'\n",
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"\n",
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"\n",
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"from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.io import load_config"
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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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"You should also adjust all the path constants to point at the relevant locations for you locally"
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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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"MODEL_RUN_PATH = \"../../Coqui-TTS/checkpoints/libritts_360-half-September-28-2019_10+46AM-8565c50-20200323T115637Z-001/\"\n",
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"MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
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"CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
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"\n",
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"\n",
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"DATASETS_NAME = ['vctk'] # list the datasets\n",
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"DATASETS_PATH = ['../../../datasets/VCTK/']\n",
|
||||
"DATASETS_METAFILE = ['']\n",
|
||||
"\n",
|
||||
"USE_CUDA = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Preprocess dataset\n",
|
||||
"meta_data = []\n",
|
||||
"for i in range(len(DATASETS_NAME)):\n",
|
||||
" preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
|
||||
" preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
|
||||
" meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
|
||||
" \n",
|
||||
"meta_data= list(meta_data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"c = load_config(CONFIG_PATH)\n",
|
||||
"ap = AudioProcessor(**c['audio'])\n",
|
||||
"\n",
|
||||
"model = SpeakerEncoder(**c.model)\n",
|
||||
"model.load_state_dict(torch.load(MODEL_PATH)['model'])\n",
|
||||
"model.eval()\n",
|
||||
"if USE_CUDA:\n",
|
||||
" model.cuda()\n",
|
||||
"\n",
|
||||
"embeddings_dict = {}\n",
|
||||
"len_meta_data= len(meta_data)\n",
|
||||
"\n",
|
||||
"for i in tqdm(range(len_meta_data)):\n",
|
||||
" _, wav_file, speaker_id = meta_data[i]\n",
|
||||
" wav_file_name = os.path.basename(wav_file)\n",
|
||||
" mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T\n",
|
||||
" mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
|
||||
" if USE_CUDA:\n",
|
||||
" mel_spec = mel_spec.cuda()\n",
|
||||
" embedd = model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
|
||||
" embeddings_dict[wav_file_name] = [embedd,speaker_id]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# create and export speakers.json\n",
|
||||
"speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0].reshape(-1).tolist()} for i, sample in enumerate(embeddings_dict.keys())}\n",
|
||||
"save_speaker_mapping(MODEL_RUN_PATH, speaker_mapping)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#test load integrity\n",
|
||||
"speaker_mapping_load = load_speaker_mapping(MODEL_RUN_PATH)\n",
|
||||
"assert speaker_mapping == speaker_mapping_load\n",
|
||||
"print(\"The file speakers.json has been exported to \",MODEL_RUN_PATH, ' with ', len(embeddings_dict.keys()), ' speakers')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue