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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Bark text-to-speech voice cloning.\n",
"Clone voices to create speaker history prompt files (.npz) for [bark text-to-speech](https://github.com/suno-ai/bark)."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Install packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%pip install -r requirements.txt\n",
"%pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Load models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-05-26 21:27:49 | INFO | fairseq.tasks.text_to_speech | Please install tensorboardX: pip install tensorboardX\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading HuBERT...\n",
"Loading Quantizer...\n",
"Loading Encodec...\n",
"Downloaded and loaded models!\n"
]
}
],
"source": [
"large_quant_model = False # Use the larger pretrained model\n",
"device = 'cuda' # 'cuda', 'cpu', 'cuda:0', 0, -1, torch.device('cuda')\n",
"\n",
"import numpy as np\n",
"import torch\n",
"import torchaudio\n",
"from encodec import EncodecModel\n",
"from encodec.utils import convert_audio\n",
"from bark_hubert_quantizer.hubert_manager import HuBERTManager\n",
"from bark_hubert_quantizer.pre_kmeans_hubert import CustomHubert\n",
"from bark_hubert_quantizer.customtokenizer import CustomTokenizer\n",
"\n",
"model = ('quantifier_V1_hubert_base_ls960_23.pth', 'tokenizer_large.pth') if large_quant_model else ('quantifier_hubert_base_ls960_14.pth', 'tokenizer.pth')\n",
"\n",
"print('Loading HuBERT...')\n",
"hubert_model = CustomHubert(HuBERTManager.make_sure_hubert_installed(), device=device)\n",
"print('Loading Quantizer...')\n",
"quant_model = CustomTokenizer.load_from_checkpoint(HuBERTManager.make_sure_tokenizer_installed(model=model[0], local_file=model[1]), device)\n",
"print('Loading Encodec...')\n",
"encodec_model = EncodecModel.encodec_model_24khz()\n",
"encodec_model.set_target_bandwidth(6.0)\n",
"encodec_model.to(device)\n",
"\n",
"print('Downloaded and loaded models!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Load wav and create speaker history prompt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting semantics...\n",
"Tokenizing semantics...\n",
"Creating coarse and fine prompts...\n",
"Done!\n"
]
}
],
"source": [
"wav_file = 'speaker.wav' # Put the path of the speaker you want to use here.\n",
"out_file = 'speaker.npz' # Put the path to save the cloned speaker to here.\n",
"\n",
"wav, sr = torchaudio.load(wav_file)\n",
"\n",
"wav_hubert = wav.to(device)\n",
"\n",
"if wav_hubert.shape[0] == 2: # Stereo to mono if needed\n",
" wav_hubert = wav_hubert.mean(0, keepdim=True)\n",
"\n",
"print('Extracting semantics...')\n",
"semantic_vectors = hubert_model.forward(wav_hubert, input_sample_hz=sr)\n",
"print('Tokenizing semantics...')\n",
"semantic_tokens = quant_model.get_token(semantic_vectors)\n",
"print('Creating coarse and fine prompts...')\n",
"wav = convert_audio(wav, sr, encodec_model.sample_rate, 1).unsqueeze(0)\n",
"\n",
"wav = wav.to(device)\n",
"\n",
"with torch.no_grad():\n",
" encoded_frames = encodec_model.encode(wav)\n",
"codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()\n",
"\n",
"codes = codes.cpu()\n",
"semantic_tokens = semantic_tokens.cpu()\n",
"\n",
"np.savez(out_file,\n",
" semantic_prompt=semantic_tokens,\n",
" fine_prompt=codes,\n",
" coarse_prompt=codes[:2, :]\n",
" )\n",
"\n",
"print('Done!')"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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"nbformat_minor": 0
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