Spaces:
Running
on
A10G
Running
on
A10G
File size: 3,257 Bytes
96ea36d 88c0b9b 96ea36d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
import os
import json
import numpy as np
import torch
import torchaudio
torchaudio.set_audio_backend("soundfile") # Use 'soundfile' backend
from encodec import EncodecModel
from encodec.utils import convert_audio
from .hubert_manager import HuBERTManager
from .pre_kmeans_hubert import CustomHubert
from .customtokenizer import CustomTokenizer
class VoiceParser():
def __init__(self, device='cpu'):
model = ('quantifier_hubert_base_ls960_14.pth', 'tokenizer.pth')
hubert_model = CustomHubert(HuBERTManager.make_sure_hubert_installed(), device=device)
quant_model = CustomTokenizer.load_from_checkpoint(HuBERTManager.make_sure_tokenizer_installed(model=model[0], local_file=model[1]), device)
encodec_model = EncodecModel.encodec_model_24khz()
encodec_model.set_target_bandwidth(6.0)
self.hubert_model = hubert_model
self.quant_model = quant_model
self.encodec_model = encodec_model.to(device)
self.device = device
print('Loaded VoiceParser models!')
def extract_acoustic_embed(self, wav_path, npz_dir):
wav, sr = torchaudio.load(wav_path)
wav_hubert = wav.to(self.device)
if wav_hubert.shape[0] == 2: # Stereo to mono if needed
wav_hubert = wav_hubert.mean(0, keepdim=True)
semantic_vectors = self.hubert_model.forward(wav_hubert, input_sample_hz=sr)
semantic_tokens = self.quant_model.get_token(semantic_vectors)
wav = convert_audio(wav, sr, self.encodec_model.sample_rate, 1).unsqueeze(0)
wav = wav.to(self.device)
with torch.no_grad():
encoded_frames = self.encodec_model.encode(wav)
codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()
codes = codes.cpu()
semantic_tokens = semantic_tokens.cpu()
wav_name = os.path.split(wav_path)[1]
npz_name = wav_name[:-4] + '.npz'
npz_path = os.path.join(npz_dir, npz_name)
np.savez(
npz_path,
semantic_prompt=semantic_tokens,
fine_prompt=codes,
coarse_prompt=codes[:2, :]
)
return npz_path
def read_json_file(self, json_path):
with open(json_path, 'r') as file:
data = json.load(file)
return data
def parse_voice_json(self, voice_json, output_dir):
"""
Parse a voice json file, generate the corresponding output json and npz files
Params:
voice_json: path of a json file or List of json nodes
output_dir: output dir for new json and npz files
"""
if isinstance(voice_json, list):
voice_json = voice_json
else:
# If voice_json is a file path (str), read the JSON file
voice_json = self.read_json_file(voice_json)
for item in voice_json:
wav_path = item['wav']
npz_path = self.extract_acoustic_embed(wav_path=wav_path, npz_dir=output_dir)
item['npz'] = npz_path
del item['wav']
output_json = os.path.join(output_dir, 'metadata.json')
with open(output_json, 'w') as file:
json.dump(voice_json, file, indent=4)
|