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import os
import sys
import torch
import numpy as np
import traceback
from scipy.io import wavfile
import librosa
from pathlib import Path
from time import time as ttime
import shutil
from tools.my_utils import load_audio, clean_path
from feature_extractor import cnhubert
def my_save(fea, path, i_part):
"""Fix issue: torch.save doesn't support chinese path"""
dir = os.path.dirname(path)
name = os.path.basename(path)
tmp_path = f"{ttime()}{i_part}.pth"
torch.save(fea, tmp_path)
shutil.move(tmp_path, f"{dir}/{name}")
def extract_hubert_features(data_dir="data8", exp_dir="logs/s2"):
"""Extract Hubert features for stage 2 training"""
# Get project root directory (parent of GPT_SoVITS)
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Convert relative paths to absolute
data_dir = os.path.join(root_dir, data_dir)
exp_dir = os.path.join(root_dir, exp_dir)
# Set environment variables for Hubert extraction
inp_text = os.path.join(exp_dir, "2-name2text.txt")
inp_wav_dir = os.path.join(exp_dir, "5-wav32k")
exp_name = "s2"
i_part = "0"
all_parts = "1"
opt_dir = exp_dir
cnhubert.cnhubert_base_path = os.path.join(root_dir, "pretrained_models", "chinese-hubert-base")
is_half = torch.cuda.is_available()
print("Starting Hubert feature extraction...")
print(f"Input text file: {inp_text}")
print(f"Input wav directory: {inp_wav_dir}")
print(f"Output directory: {opt_dir}")
hubert_dir = f"{opt_dir}/4-cnhubert"
wav32dir = f"{opt_dir}/5-wav32k"
os.makedirs(opt_dir, exist_ok=True)
os.makedirs(hubert_dir, exist_ok=True)
os.makedirs(wav32dir, exist_ok=True)
maxx = 0.95
alpha = 0.5
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
print(f"Loading Hubert model from: {cnhubert.cnhubert_base_path}")
model = cnhubert.get_model()
if is_half:
model = model.half().to(device)
else:
model = model.to(device)
nan_fails = []
def name2go(wav_name, wav_path):
print(f"Processing: {wav_name} from {wav_path}")
hubert_path = f"{hubert_dir}/{wav_name}.pt"
if os.path.exists(hubert_path):
print(f"Skipping {wav_name} - already processed")
return
if not os.path.exists(wav_path):
print(f"Error: WAV file not found: {wav_path}")
return
tmp_audio = load_audio(wav_path, 32000)
if tmp_audio is None:
print(f"Error: Failed to load audio: {wav_path}")
return
tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.2:
print(f"{wav_name}-filtered,{tmp_max}")
return
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000)
tensor_wav16 = torch.from_numpy(tmp_audio)
if is_half:
tensor_wav16 = tensor_wav16.half().to(device)
else:
tensor_wav16 = tensor_wav16.to(device)
ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu()
if np.isnan(ssl.detach().numpy()).sum() != 0:
nan_fails.append((wav_name, wav_path))
print(f"nan filtered:{wav_name}")
return
wavfile.write(
f"{wav32dir}/{wav_name}",
32000,
tmp_audio32.astype("int16"),
)
my_save(ssl, hubert_path, i_part)
print(f"Successfully processed {wav_name}")
print(f"Reading text file: {inp_text}")
with open(inp_text, "r", encoding="utf8") as f:
lines = f.read().strip("\n").split("\n")
print(f"Found {len(lines)} lines in text file")
for line in lines[int(i_part)::int(all_parts)]:
try:
print(f"Processing line: {line}")
wav_name, text, _, _ = line.split("\t")
wav_name = clean_path(wav_name)
if inp_wav_dir:
wav_name = os.path.basename(wav_name)
wav_path = f"{inp_wav_dir}/{wav_name}"
else:
wav_path = wav_name
wav_name = os.path.basename(wav_name)
name2go(wav_name, wav_path)
except Exception as e:
print(f"Error processing line: {line}")
print(traceback.format_exc())
if len(nan_fails) > 0 and is_half:
print("Retrying failed files in float32 mode...")
is_half = False
model = model.float()
for wav in nan_fails:
try:
name2go(wav[0], wav[1])
except:
print(f"Error retrying {wav_name}")
print(traceback.format_exc())
print("Hubert feature extraction complete.")
if __name__ == "__main__":
extract_hubert_features() |