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import torch
def get_model():
import whisper
model = whisper.load_model("small", device="cpu")
return model.encoder
def get_content(model=None, wav_16k_tensor=None):
from whisper import log_mel_spectrogram, pad_or_trim
dev = next(model.parameters()).device
mel = log_mel_spectrogram(wav_16k_tensor).to(dev)[:, :3000]
# if torch.cuda.is_available():
# mel = mel.to(torch.float16)
feature_len = mel.shape[-1] // 2
assert mel.shape[-1] < 3000, "输入音频过长，只允许输入30以内音频"
with torch.no_grad():
feature = model(pad_or_trim(mel, 3000).unsqueeze(0))[
:1, :feature_len, :
].transpose(1, 2)
return feature
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