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import torch
import math
import decimal
from typing import List
import torch.nn.functional as F
from torchaudio.transforms import MFCC
from src.simulation.component import Component
################################################################################
# Voice Activity Detection (VAD)
################################################################################
class KaldiStyleVAD(Component):
"""
Kaldi-style Voice Activity Detection (VAD) module. Adapted from
https://github.com/fsepteixeira/FoolHD/blob/main/code/utils/vad_cmvn.py
"""
def __init__(self,
compute_grad: bool = True,
threshold: float = -15.0,
proportion_threshold: float = 0.12,
frame_len: float = 0.025,
hop_len: float = 0.010,
mean_scale: float = 0.5,
context: int = 2):
super().__init__(compute_grad)
self.threshold = threshold
self.proportion_threshold = proportion_threshold
self.mean_scale = mean_scale
self.context = context
self.diff_zero = mean_scale != 0
self.unfold_size = 2 * context + 1
self.frame_len = int(frame_len * self.sample_rate)
self.hop_len = int(hop_len * self.sample_rate)
# prepare to compute MFCC
self.mfcc = MFCC(
sample_rate=self.sample_rate,
n_mfcc=30,
dct_type=2,
norm='ortho',
log_mels=True,
melkwargs={
'n_fft': self.frame_len,
'hop_length': self.hop_len,
'n_mels': 30,
'f_min': 20,
'f_max': self.sample_rate // 2,
'power': 2.0,
'center': True
}
)
def forward(self, x: torch.Tensor):
if x.shape[-1] < self.frame_len + self.hop_len:
return x
# require batch dimension
assert x.ndim >= 2
# require mono audio, discard channel dimension
n_batch, slen = x.shape[0], x.shape[-1]
x = x.reshape(n_batch, slen)
# compute MFCC
x_mfcc = self.mfcc(x).permute(0, 2, 1) # (n_batch, n_frames, n_mfcc)
# set device for energy threshold
energy_threshold = torch.tensor([self.threshold]).to(x_mfcc.device)
# first MFCC coefficient represents log energy
log_energy = x_mfcc[:, :, 0]
if self.diff_zero:
energy_threshold = energy_threshold + self.mean_scale * log_energy.mean(dim=1)
# prepare frame-wise mask
mask = torch.ones_like(log_energy)
# pad borders with symmetric context before striding
mask = F.pad(mask, pad=(self.context, self.context), value=1.0)
# get all (overlapping) context "windows"
mask = mask.unfold(dimension=1, size=self.unfold_size, step=1)
# number of values included in each context window
den_count = mask.sum(dim=-1)
# pad borders with symmetric context
log_energy = F.pad(log_energy, pad=(self.context, self.context))
# get all (overlapping) context "windows"
log_energy = log_energy.unfold(
dimension=1,
size=self.unfold_size,
step=1
)
# number of values in each context window above threshold
num_count = log_energy.gt(
energy_threshold.unsqueeze(-1).unsqueeze(-1)
).sum(dim=-1)
# frame-by-frame mask
mask = num_count.ge(den_count*self.proportion_threshold)
# "fold" to obtain waveform mask
mask_wav = mask.unsqueeze(-1).repeat_interleave(
repeats=self.frame_len, dim=-1
)
mask_wav = torch.cat(
[
mask_wav[:, 0],
mask_wav[:, 1:][:, :, self.frame_len - self.hop_len:].reshape(
n_batch, -1
)
], dim=-1
)
left_trim = self.frame_len // 2
right_trim = mask_wav.shape[-1] - left_trim - x.shape[-1]
mask_wav = mask_wav[..., left_trim: -right_trim]
# compute number of accepted samples per input waveform
samples_per_row: List[int] = []
for e in torch.sum(mask_wav, dim=-1):
samples_per_row.append(e.item())
# split resulting tensor to keep trimmed inputs separate
split = torch.split(x[mask_wav], samples_per_row)
# placeholder for outputs: (n_batch, 1, padded_length)
final = torch.zeros_like(x).unsqueeze(1) # pad to preserve length
# concatenate and pad split views
for i, tensor in enumerate(split):
length = tensor.shape[-1]
final[i, :, :length] = tensor
return final[..., :slen]
class VAD(Component):
"""
Apply Voice Activity Detection (VAD) while allowing for straight-through
gradient estimation. For now, only supports simple energy-based method,
and should be placed after normalization to avoid scale-dependence.
"""
def __init__(self,
compute_grad: bool = True,
frame_len: float = 0.05,
threshold: float = -72
):
super().__init__(compute_grad)
self.threshold = threshold
self.frame_len = int(
decimal.Decimal(
frame_len * self.sample_rate
).quantize(
decimal.Decimal('1'), rounding=decimal.ROUND_HALF_UP
)
) # convert seconds to samples, round up
def forward(self, x: torch.Tensor):
# require batch dimension
assert x.ndim >= 2
# require mono audio, discard channel dimension
n_batch, slen = x.shape[0], x.shape[-1]
audio = x.reshape(n_batch, slen)
eps = 1e-12 # numerical stability
# determine number of frames
if slen <= self.frame_len:
n_frames = 1
else:
n_frames = 1 + int(
math.ceil(
(1.0 * slen - self.frame_len) / self.frame_len)
)
# pad to integer frame length
padlen = int(n_frames * self.frame_len)
zeros = torch.zeros((x.shape[0], padlen - slen,)).to(x)
padded = torch.cat((audio, zeros), dim=-1)
# obtain strided (frame-wise) view of audio
shape = (padded.shape[0], n_frames, self.frame_len)
frames = torch.as_strided(
padded,
size=shape,
stride=(padded.shape[-1], self.frame_len, 1)
)
# create frame-by-frame mask based on energy threshold
mask = 20 * torch.log10(
((frames * self.scale).norm(dim=-1) / self.frame_len) + eps
) > self.threshold
# turn frame-by-frame mask into sample-by-sample mask
mask_wav = torch.repeat_interleave(mask, self.frame_len, dim=-1)
samples_per_row = torch.sum(mask, dim=-1) * self.frame_len
split = torch.split(padded[mask_wav], tuple(samples_per_row))
# placeholder for outputs: (n_batch, 1, padded_length)
final = torch.zeros_like(padded).unsqueeze(1) # pad to preserve length
# concatenate and pad split views
for i, tensor in enumerate(split):
length = tensor.shape[-1]
final[i, :, :length] = tensor
return final[..., :slen]
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