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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| class EnergyExtractor(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): | |
| 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 | |
| def forward(self, audio: torch.Tensor) -> torch.Tensor: | |
| # 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 | |
| audio_squared = audio_squared[:, None, None, :] | |
| energy = F.unfold(audio_squared, (1, self.window_size), stride=self.hop_size)[:, :, :n_frames] | |
| energy = energy.mean(dim=1) | |
| # 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.max(gain_db, dim=-1, keepdim=True)[0] | |
| # 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.unsqueeze(-1) | |
| if __name__ == "__main__": | |
| energy_extractor = EnergyExtractor(hop_size=512, window_size=1024, padding='reflect', | |
| min_db=-60, norm=True) | |
| audio = torch.rand(1, 16000) | |
| energy = energy_extractor(audio) | |
| print(energy.shape) | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| # a1, _ = librosa.load('eg1.wav', sr=16000) | |
| # a2, _ = librosa.load('eg2.wav', sr=16000) | |
| # audio = torch.tensor([a1[:5*16000], a2[:5*16000]]) | |
| 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[0]): | |
| plt.plot(energy[i, :, 0].cpu().numpy(), label=f'Audio {i+1}') | |
| plt.xlabel('Frame') | |
| plt.ylabel('Energy (dB)') | |
| plt.title('Energy over Time') | |
| plt.legend() | |
| plt.savefig('debug.png') |