def test_samples_like_scalar():
X = 7
hop_length = 512
samples = librosa.samples_like(X, hop_length=hop_length)
expected_samples = np.arange(7)*hop_length
assert np.allclose(samples, expected_samples)
def test_samples_like_scalar():
X = 7
hop_length = 512
samples = librosa.samples_like(X, hop_length=hop_length)
expected_samples = np.arange(7)*hop_length
assert np.allclose(samples, expected_samples)
def test_samples_like():
X = np.ones((3, 4, 5))
hop_length = 512
for axis in (0, 1, 2, -1):
samples = librosa.samples_like(X, hop_length=hop_length, axis=axis)
expected_samples = np.arange(X.shape[axis]) * hop_length
assert np.allclose(samples, expected_samples)
def test_samples_like():
X = np.ones((3, 4, 5))
hop_length = 512
for axis in (0, 1, 2, -1):
samples = librosa.samples_like(X,
hop_length=hop_length,
axis=axis)
expected_samples = np.arange(X.shape[axis])*hop_length
assert np.allclose(samples, expected_samples)
def main():
audio_full_name = 'huya_zdx_103.16.wav'
y,sr = librosa.load(audio_full_name,sr=None)#y为ndarray类型
print(y)
print('sr:', sr)
print('总帧数=%d,采样率=%d,持续秒数=%f'%(len(y),sr,len(y)/sr))
# return
samples = librosa.samples_like(y,hop_length=1)
print('samples = %s'%samples)
times = librosa.frames_to_time(samples,sr=sr,hop_length=1)
print(len(times))
print('times = %s'%times)
for i in range(len(speech_list)):
a = speech_list[i]
start_ind = getRight(times, a.start_time)
end_ind = getRight(times, a.end_time)
print(start_ind)
b = y[start_ind: end_ind + 1]
write_wav(os.path.join(dir_p, a.name), b, sr=sr)
