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November 12, 2020 14:00
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# waveform = torch.from_numpy(waveform) ----> numpy array를 torch tensor로 변환하는 것은 속도에 영향이 거의 없다. | |
# sr = 22050이면 --> torchaudio가 많이 느리다. | |
# sr = 16000 ---> origin sample_rate과 일치하면 모두 다 빠르다. | |
# sr = 8000 | |
n_samples = 100 | |
sr = 22050 | |
s_time=time.time() | |
for i in range(n_samples): | |
waveform,sr = librosa.load(A[i],sr=sr,res_type='kaiser_best') | |
print('librosa(kaiser_best): ',time.time()-s_time) | |
s_time=time.time() | |
for i in range(n_samples): | |
waveform,sr = librosa.load(A[i],sr=sr,res_type='kaiser_fast') | |
print('librosa(kaiser_fast): ',time.time()-s_time) | |
s_time=time.time() | |
for i in range(n_samples): | |
waveform,sr = librosa.load(A[i],sr=sr,res_type='polyphase') | |
print('librosa(polyphase): ',time.time()-s_time) | |
transform = torchaudio.transforms.Resample(orig_freq=16000, new_freq=sr) | |
s_time=time.time() | |
for i in range(n_samples): | |
waveform,sr = torchaudio.load(A[i]) | |
waveform = transform(waveform) | |
print('torchaudio: ',time.time()-s_time) |
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