Output audio duration does not exactly match input audio.

Running through your pre-trained models, I found that generated audio does not exactly match the input in duration length. For example,

wav, sr = load_wav(os.path.join(a.input_wavs_dir, filname))
wav = wav / MAX_WAV_VALUE
wav = torch.FloatTensor(wav).to(device)  # wav shape is torch.Size([71334])
x = get_mel(wav.unsqueeze(0))  # x shape is torch.Size([1, 80, 278])
y_g_hat = generator(x)  # y_g_hat shape is torch.Size([1, 1, 71168])

As you can see, there is a mismatch of 71334 and 71168. What is happening, and why is this the case? Is there a way that I can change it so that the input and output shapes match?

Thank you.

Edit: So I was checking training, and if the target segment_size is a multiple of 256 (hop_size), then y_g_hat = generator(x) will also have the exact number.

[Miralan]

This mismatch is cause padding and transposed convolution, you should set segment_size % hop_size == 0(segment_size + (nfft - hop_size can get segmentsize % hop_size frames melspectrum). In other words, one frame represent hop_size sampling points.

[jik876]

Thank you. It is correct to adjust chunk_size to be divided by hop_size. I don’t know what sample rate you’re using, but 10ms chunk seems too short to generate high quality audio considering the receptive field of the generator.