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Running
on
Zero
File size: 3,173 Bytes
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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') |