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fix memory overflow issue in get-hubert-wav

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Keming 2024-11-22 00:46:18 -08:00
parent a70e1ad30c
commit 434ca2e82c
1 changed files with 29 additions and 25 deletions
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54
GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py
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@ -17,6 +17,7 @@ is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
import pdb,traceback,numpy as np,logging import pdb,traceback,numpy as np,logging
from scipy.io import wavfile from scipy.io import wavfile
import librosa import librosa
import gc
now_dir = os.getcwd() now_dir = os.getcwd()
sys.path.append(now_dir) sys.path.append(now_dir)
from tools.my_utils import load_audio,clean_path from tools.my_utils import load_audio,clean_path
@ -64,35 +65,38 @@ else:
model = model.to(device) model = model.to(device)
nan_fails=[] nan_fails=[]
def name2go(wav_name,wav_path): def name2go(wav_name, wav_path):
hubert_path="%s/%s.pt"%(hubert_dir,wav_name) hubert_path = f"{hubert_dir}/{wav_name}.pt"
if(os.path.exists(hubert_path)):return if os.path.exists(hubert_path):
return
tmp_audio = load_audio(wav_path, 32000) tmp_audio = load_audio(wav_path, 32000)
tmp_max = np.abs(tmp_audio).max() tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.2: if tmp_max > 2.2:
print("%s-filtered,%s" % (wav_name, tmp_max)) print(f"{wav_name}-filtered, {tmp_max}")
return return
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio 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_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
tmp_audio = librosa.resample( tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000)
tmp_audio32b, orig_sr=32000, target_sr=16000
)#不是重采样问题 tensor_wav16 = torch.from_numpy(tmp_audio).to(device)
tensor_wav16 = torch.from_numpy(tmp_audio) if is_half:
if (is_half == True): tensor_wav16 = tensor_wav16.half()
tensor_wav16=tensor_wav16.half().to(device)
else: try:
tensor_wav16 = tensor_wav16.to(device) with torch.no_grad():
ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215]) ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu()
if np.isnan(ssl.detach().numpy()).sum()!= 0: if torch.isnan(ssl).any():
nan_fails.append((wav_name,wav_path)) nan_fails.append((wav_name, wav_path))
print("nan filtered:%s"%wav_name) print(f"nan filtered: {wav_name}")
return return
wavfile.write( wavfile.write(f"{wav32dir}/{wav_name}", 32000, tmp_audio32.astype("int16"))
"%s/%s"%(wav32dir,wav_name), my_save(ssl, hubert_path)
32000, except Exception as e:
tmp_audio32.astype("int16"), print(f"Error processing {wav_name}: {e}")
) finally:
my_save(ssl,hubert_path) del tensor_wav16, ssl
torch.cuda.empty_cache()
gc.collect()
with open(inp_text,"r",encoding="utf8")as f: with open(inp_text,"r",encoding="utf8")as f:
lines=f.read().strip("\n").split("\n") lines=f.read().strip("\n").split("\n")