from bark.generation import load_codec_model, generate_text_semantic, grab_best_device from encodec.utils import convert_audio from bark.hubert.hubert_manager import HuBERTManager from bark.hubert.pre_kmeans_hubert import CustomHubert from bark.hubert.customtokenizer import CustomTokenizer import torchaudio import torch import os import gradio def clone_voice(audio_filepath, dest_filename, progress=gradio.Progress(track_tqdm=True)): # if len(text) < 1: # raise gradio.Error('No transcription text entered!') use_gpu = not os.environ.get("BARK_FORCE_CPU", False) progress(0, desc="Loading Codec") model = load_codec_model(use_gpu=use_gpu) # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer hubert_manager = HuBERTManager() hubert_manager.make_sure_hubert_installed() hubert_manager.make_sure_tokenizer_installed() # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer # Load HuBERT for semantic tokens # Load the HuBERT model device = grab_best_device(use_gpu) hubert_model = CustomHubert(checkpoint_path='./models/hubert/hubert.pt').to(device) # Load the CustomTokenizer model tokenizer = CustomTokenizer.load_from_checkpoint('./models/hubert/en_tokenizer.pth').to(device) # change to the correct path progress(0.25, desc="Converting WAV") # Load and pre-process the audio waveform wav, sr = torchaudio.load(audio_filepath) if wav.shape[0] == 2: # Stereo to mono if needed wav = wav.mean(0, keepdim=True) wav = convert_audio(wav, sr, model.sample_rate, model.channels) wav = wav.to(device) progress(0.5, desc="Extracting codes") semantic_vectors = hubert_model.forward(wav, input_sample_hz=model.sample_rate) semantic_tokens = tokenizer.get_token(semantic_vectors) # Extract discrete codes from EnCodec with torch.no_grad(): encoded_frames = model.encode(wav.unsqueeze(0)) codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze() # [n_q, T] # get seconds of audio # seconds = wav.shape[-1] / model.sample_rate # generate semantic tokens # semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds, top_k=50, top_p=.95, temp=0.7) # move codes to cpu codes = codes.cpu().numpy() # move semantic tokens to cpu semantic_tokens = semantic_tokens.cpu().numpy() import numpy as np output_path = dest_filename + '.npz' np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens) return ["Finished", output_path]