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Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| import julius | |
| import soundfile as sf | |
| class MultibandEnergyExtractor(nn.Module): | |
| def __init__(self, hop_size: int = 512, window_size: int = 1024, | |
| padding: str = 'reflect', min_db: float = -60, | |
| norm: bool = True, quantize_levels: int = None, | |
| n_bands: int = 8, control_bands: int = 4, | |
| sample_rate: int = 24000,): | |
| super().__init__() | |
| self.hop_size = hop_size | |
| self.window_size = window_size | |
| self.padding = padding | |
| self.min_db = min_db | |
| self.norm = norm | |
| self.quantize_levels = quantize_levels | |
| self.n_bands = n_bands | |
| self.control_bands = control_bands | |
| self.sample_rate = sample_rate | |
| def forward(self, audio: torch.Tensor) -> torch.Tensor: | |
| # Split the audio into frequency bands | |
| audio = julius.split_bands(audio, n_bands=self.n_bands, | |
| sample_rate=self.sample_rate)[:self.control_bands].transpose(0, 1) | |
| B, C, _ = audio.shape | |
| for i in range(C): | |
| sf.write(f'output_{i}.wav', audio[0][i], self.sample_rate) | |
| # Compute number of frames | |
| n_frames = int(audio.size(-1) // self.hop_size) | |
| # Pad the audio signal | |
| pad_amount = (self.window_size - self.hop_size) // 2 | |
| audio_padded = F.pad(audio, (pad_amount, pad_amount), mode=self.padding) | |
| # Square the padded audio signal | |
| audio_squared = audio_padded ** 2 | |
| # Compute the mean energy for each frame using unfold and mean | |
| energy = audio_squared.unfold(dimension=-1, size=self.window_size, step=self.hop_size) | |
| energy = energy[:, :, :n_frames] | |
| print(energy.shape) | |
| energy = energy.mean(dim=-1) | |
| print(energy.shape) | |
| # Compute the square root of the mean energy to get the RMS energy | |
| # energy = torch.sqrt(energy) | |
| # Normalize the energy using the min_db value | |
| gain = torch.maximum(energy, torch.tensor(np.power(10, self.min_db / 10), device=audio.device)) | |
| gain_db = 10 * torch.log10(gain) | |
| if self.norm: | |
| # Find the min and max of gain_db | |
| # min_gain_db = torch.min(gain_db) | |
| min_gain_db = self.min_db | |
| max_gain_db = torch.amax(gain_db, dim=(-1, -2), keepdim=True) | |
| # Avoid numerical error by adding a small epsilon to the denominator | |
| epsilon = 1e-8 | |
| gain_db = (gain_db - min_gain_db) / (max_gain_db - min_gain_db + epsilon) | |
| if self.quantize_levels is not None: | |
| # Quantize the result to the given number of levels | |
| gain_db = torch.round(gain_db * (self.quantize_levels - 1)) / (self.quantize_levels - 1) | |
| return gain_db.transpose(-1, -2) | |
| if __name__ == "__main__": | |
| energy_extractor = MultibandEnergyExtractor(hop_size=320, window_size=1280, | |
| padding='reflect', | |
| min_db=-60, norm=True) | |
| audio = torch.rand(4, 24000) | |
| energy = energy_extractor(audio) | |
| print(energy.shape) | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| a1, _ = librosa.load('eg2.wav', sr=24000) | |
| audio = torch.tensor(a1[:5*16000]).unsqueeze(0) | |
| energy = energy_extractor(audio) | |
| print(energy.shape) | |
| # Plot the energy for each audio sample | |
| plt.figure(figsize=(12, 6)) | |
| for i in range(energy.shape[-1]): | |
| plt.plot(energy[0, :, i].cpu().numpy(), label=f'Band {i+1}') | |
| plt.xlabel('Frame') | |
| plt.ylabel('Energy (dB)') | |
| plt.title('Energy over Time') | |
| plt.legend() | |
| plt.savefig('debug.png') | |