asteroid-filterbanks

import torch from asteroid_filterbanks.enc_dec import Encoder, Decoder from asteroid_filterbanks import FreeFB # Define parameters for the filterbank n_filters = 256 kernel_size = 128 stride = 64 sample_rate = 16000 # 1. Instantiate a filterbank (e.g., a fully learnable Free Filterbank) fb = FreeFB(n_filters=n_filters, kernel_size=kernel_size, stride=stride, sample_rate=sample_rate) # 2. Wrap it with an Encoder to get the time-frequency representation encoder = Encoder(fb) # 3. Create a dummy waveform (batch, channels, time) waveform = torch.randn(1, 1, sample_rate * 4) # 4 seconds of audio # 4. Encode the waveform into a spectrogram-like representation spec_like = encoder(waveform) print(f"Input waveform shape: {waveform.shape}") print(f"Encoded spectrogram-like shape: {spec_like.shape}") # 5. Instantiate a Decoder (can use the same filterbank or a different one) decoder = Decoder(fb) # For reconstruction, often use the same filterbank # 6. Decode the spectrogram-like representation back to waveform out_waveform = decoder(spec_like) print(f"Decoded waveform shape: {out_waveform.shape}")