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from bark.generation import load_codec_model, generate_text_semantic, grab_best_device
from encodec.utils import convert_audio
import torchaudio
import torch
import os
import gradio
def clone_voice(audio_filepath, text, 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)
progress(0.25, desc="Converting WAV")
# Load and pre-process the audio waveform
device = grab_best_device(use_gpu)
wav, sr = torchaudio.load(audio_filepath)
wav = convert_audio(wav, sr, model.sample_rate, model.channels)
wav = wav.unsqueeze(0).to(device)
progress(0.5, desc="Extracting codes")
# Extract discrete codes from EnCodec
with torch.no_grad():
encoded_frames = model.encode(wav)
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()
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"
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