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| import torch | |
| import torch.nn.functional as F | |
| import matplotlib.pyplot as plt | |
| from pesq import pesq | |
| from pystoi import stoi | |
| import mir_eval | |
| REFERENCE_CHANNEL = 0 | |
| def plot_spectrogram(stft, title="Spectrogram", xlim=None): | |
| magnitude = stft.abs() | |
| spectrogram = 20 * torch.log10(magnitude + 1e-8).numpy() | |
| # figure, axis = plt.subplots(1, 1) | |
| # img = axis.imshow(spectrogram, cmap="viridis", vmin=-100, vmax=0, origin="lower", aspect="auto") | |
| # figure.suptitle(title) | |
| # plt.colorbar(img, ax=axis) | |
| # plt.show() | |
| def plot_mask(mask, title="Mask", xlim=None): | |
| mask = mask.numpy() | |
| figure, axis = plt.subplots(1, 1) | |
| img = axis.imshow(mask, cmap="viridis", origin="lower", aspect="auto") | |
| figure.suptitle(title) | |
| plt.colorbar(img, ax=axis) | |
| plt.show() | |
| def si_snr(estimate, reference, epsilon=1e-8): | |
| estimate = estimate - estimate.mean() | |
| reference = reference - reference.mean() | |
| reference_pow = reference.pow(2).mean(axis=1, keepdim=True) | |
| mix_pow = (estimate * reference).mean(axis=1, keepdim=True) | |
| scale = mix_pow / (reference_pow + epsilon) | |
| reference = scale * reference | |
| error = estimate - reference | |
| reference_pow = reference.pow(2) | |
| error_pow = error.pow(2) | |
| reference_pow = reference_pow.mean(axis=1) | |
| error_pow = error_pow.mean(axis=1) | |
| si_snr = 10 * torch.log10(reference_pow) - 10 * torch.log10(error_pow) | |
| return si_snr.item() | |
| # def generate_mixture(waveform_clean, waveform_noise, target_snr): | |
| # power_clean_signal = waveform_clean.pow(2).mean() | |
| # power_noise_signal = waveform_noise.pow(2).mean() | |
| # current_snr = 10 * torch.log10(power_clean_signal / power_noise_signal) | |
| # waveform_noise *= 10 ** (-(target_snr - current_snr) / 20) | |
| # return waveform_clean + waveform_noise | |
| def generate_mixture(waveform_clean, waveform_noise, target_snr): | |
| if waveform_clean.size(1) > waveform_noise.size(1): | |
| waveform_noise = F.pad(waveform_noise, (0, waveform_clean.size(1) - waveform_noise.size(1))) | |
| elif waveform_noise.size(1) > waveform_clean.size(1): | |
| waveform_clean = F.pad(waveform_clean, (0, waveform_noise.size(1) - waveform_clean.size(1))) | |
| power_clean_signal = waveform_clean.pow(2).mean() | |
| power_noise_signal = waveform_noise.pow(2).mean() | |
| current_snr = 10 * torch.log10(power_clean_signal / power_noise_signal) | |
| waveform_noise *= 10 ** (-(target_snr - current_snr) / 20) | |
| return waveform_clean + waveform_noise | |
| def evaluate(estimate, reference): | |
| si_snr_score = si_snr(estimate, reference) | |
| ( | |
| sdr, | |
| _, | |
| _, | |
| _, | |
| ) = mir_eval.separation.bss_eval_sources(reference.numpy(), estimate.numpy(), False) | |
| pesq_mix = pesq(SAMPLE_RATE, estimate[0].numpy(), reference[0].numpy(), "wb") | |
| stoi_mix = stoi(reference[0].numpy(), estimate[0].numpy(), SAMPLE_RATE, extended=False) | |
| print(f"SDR score: {sdr[0]}") | |
| print(f"Si-SNR score: {si_snr_score}") | |
| print(f"PESQ score: {pesq_mix}") | |
| print(f"STOI score: {stoi_mix}") | |
| def get_irms(stft_clean, stft_noise): | |
| mag_clean = stft_clean.abs() ** 2 | |
| mag_noise = stft_noise.abs() ** 2 | |
| irm_speech = mag_clean / (mag_clean + mag_noise) | |
| irm_noise = mag_noise / (mag_clean + mag_noise) | |
| return irm_speech[REFERENCE_CHANNEL], irm_noise[REFERENCE_CHANNEL] | |