def rtf()

in models/framework.py [0:0]

• 18 lines of code
• 6 McCabe index (conditional complexity)

def rtf(model: Vocoder, n_iter: int) -> float:
    """
    Compute RTF for a given vocoder model over n_iter
    """

    mel = datasets.Audio2Mel()
    if torch.cuda.is_available():
        mel.cuda()

    rtfs = []

    # Generate a list of random secs for each sample within [10, 20] range.
    np.random.seed(42)
    secs = np.random.rand(n_iter) * 10 + 10

    progress = tqdm(secs, desc="", total=n_iter)

    for sec in progress:
        waveforms = torch.rand(1, int(AUDIO_SAMPLE_RATE * sec))
        if torch.cuda.is_available():
            waveforms = waveforms.cuda()
        spectrograms = mel(waveforms)

        # Compute RTF for the current sample.
        start = time.time()
        model.generate(spectrograms)
        rtfs.append((time.time() - start) / sec)

        progress.set_description("RTF: %0.2f" % np.mean(rtfs))

    return np.mean(rtfs)