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fix(preprocess): pass device

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magic-akari 2023-07-23 23:27:58 +08:00
parent b306a0ecfa
commit 2eb19ae4e5
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1 changed files with 2 additions and 3 deletions
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preprocess_hubert_f0.py
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@ -28,11 +28,10 @@ hop_length = hps.data.hop_length
speech_encoder = hps["model"]["speech_encoder"] speech_encoder = hps["model"]["speech_encoder"]
def process_one(filename, hmodel,f0p,rank,diff=False,mel_extractor=None): def process_one(filename, hmodel, f0p, device, diff=False, mel_extractor=None):
wav, sr = librosa.load(filename, sr=sampling_rate) wav, sr = librosa.load(filename, sr=sampling_rate)
audio_norm = torch.FloatTensor(wav) audio_norm = torch.FloatTensor(wav)
audio_norm = audio_norm.unsqueeze(0) audio_norm = audio_norm.unsqueeze(0)
device = torch.device(f"cuda:{rank}")
soft_path = filename + ".soft.pt" soft_path = filename + ".soft.pt"
if not os.path.exists(soft_path): if not os.path.exists(soft_path):
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000) wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
@ -115,7 +114,7 @@ def process_batch(file_chunk, f0p, diff=False, mel_extractor=None, device="cpu")
hmodel = utils.get_speech_encoder(speech_encoder, device=device) hmodel = utils.get_speech_encoder(speech_encoder, device=device)
logger.info(f"Loaded speech encoder for rank {rank}") logger.info(f"Loaded speech encoder for rank {rank}")
for filename in tqdm(file_chunk): for filename in tqdm(file_chunk):
process_one(filename, hmodel, f0p, gpu_id, diff, mel_extractor) process_one(filename, hmodel, f0p, device, diff, mel_extractor)
def parallel_process(filenames, num_processes, f0p, diff, mel_extractor, device): def parallel_process(filenames, num_processes, f0p, diff, mel_extractor, device):
with ProcessPoolExecutor(max_workers=num_processes) as executor: with ProcessPoolExecutor(max_workers=num_processes) as executor: