I use same code in https://github.com/KentoNishi/torch-time-stretch/blob/master/example.py but get below error

(librosa) ➜  torch-time-stretch git:(master) ✗ python example.py 
Traceback (most recent call last):
  File "/home/jackie/code/github/torch-time-stretch/example.py", line 48, in <module>
    test_time_stretch_2_up()
  File "/home/jackie/code/github/torch-time-stretch/example.py", line 20, in test_time_stretch_2_up
    up = time_stretch(sample, Fraction(1, 2), SAMPLE_RATE)
  File "/home/jackie/code/github/torch-time-stretch/torch_time_stretch/main.py", line 116, in time_stretch
    output = stretcher(output)
  File "/home/jackie/anaconda3/envs/librosa/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/jackie/anaconda3/envs/librosa/lib/python3.9/site-packages/torchaudio/transforms/_transforms.py", line 1059, in forward
    return F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
  File "/home/jackie/anaconda3/envs/librosa/lib/python3.9/site-packages/torchaudio/functional/functional.py", line 743, in phase_vocoder
    phase = angle_1 - angle_0 - phase_advance
RuntimeError: The size of tensor a (40264) must match the size of tensor b (173) at non-singleton dimension 1

Love it, thanks for making this! Tiny thing: In the example test_time_stretch_2_up should use 1/2 as a ratio, not 2/1. test_time_stretch_2_down should use that 2/1 (it's stretching the clip length by 2x).

my audio clip is in mono 16khz audio, [ 0 0 0 ... 63 100 127], so it will throw

---> 15 down = time_stretch(sample, Fraction(2, 1), SAMPLE_RATE)
     16 wavfile.write(
     17     "./stretched_down_2.wav",
     18     SAMPLE_RATE,
     19     np.swapaxes(down.cpu()[0].numpy(), 0, 0).astype(dtype),
     20 )

File /opt/conda/envs/classify-audio/lib/python3.9/site-packages/torch_time_stretch/main.py:108, in time_stretch(input, stretch, sample_rate, n_fft, hop_length)
    106 if not hop_length:
    107     hop_length = n_fft // 32
--> 108 batch_size, channels, samples = input.shape
    109 # resampler = T.Resample(sample_rate, int(sample_rate / stretch)).to(input.device)
    110 output = input

ValueError: not enough values to unpack (expected 3, got 2)