""" Specifies the inference interfaces for Voice Activity Detection (VAD) modules.
Authors:
* Aku Rouhe 2021
* Peter Plantinga 2021
* Loren Lugosch 2020
* Mirco Ravanelli 2020
* Titouan Parcollet 2021
* Abdel Heba 2021
* Andreas Nautsch 2022, 2023
* Pooneh Mousavi 2023
* Sylvain de Langen 2023
* Adel Moumen 2023
* Pradnya Kandarkar 2023
"""
import torch
import torchaudio
from speechbrain.utils.data_utils import split_path
from speechbrain.utils.fetching import fetch
from speechbrain.inference.interfaces import Pretrained
[docs]
class VAD(Pretrained):
"""A ready-to-use class for Voice Activity Detection (VAD) using a
pre-trained model.
Example
-------
>>> import torchaudio
>>> from speechbrain.inference.VAD import VAD
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> VAD = VAD.from_hparams(
... source="speechbrain/vad-crdnn-libriparty",
... savedir=tmpdir,
... )
>>> # Perform VAD
>>> boundaries = VAD.get_speech_segments("tests/samples/single-mic/example1.wav")
"""
HPARAMS_NEEDED = ["sample_rate", "time_resolution", "device"]
MODULES_NEEDED = ["compute_features", "mean_var_norm", "model"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.time_resolution = self.hparams.time_resolution
self.sample_rate = self.hparams.sample_rate
[docs]
def get_speech_prob_file(
self,
audio_file,
large_chunk_size=30,
small_chunk_size=10,
overlap_small_chunk=False,
):
"""Outputs the frame-level speech probability of the input audio file
using the neural model specified in the hparam file. To make this code
both parallelizable and scalable to long sequences, it uses a
double-windowing approach. First, we sequentially read non-overlapping
large chunks of the input signal. We then split the large chunks into
smaller chunks and we process them in parallel.
Arguments
---------
audio_file: path
Path of the audio file containing the recording. The file is read
with torchaudio.
large_chunk_size: float
Size (in seconds) of the large chunks that are read sequentially
from the input audio file.
small_chunk_size:
Size (in seconds) of the small chunks extracted from the large ones.
The audio signal is processed in parallel within the small chunks.
Note that large_chunk_size/small_chunk_size must be an integer.
overlap_small_chunk: bool
True, creates overlapped small chunks. The probabilities of the
overlapped chunks are combined using hamming windows.
Returns
-------
prob_vad: torch.Tensor
Tensor containing the frame-level speech probabilities for the
input audio file.
"""
# Getting the total size of the input file
sample_rate, audio_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
# Computing the length (in samples) of the large and small chunks
long_chunk_len = int(sample_rate * large_chunk_size)
small_chunk_len = int(sample_rate * small_chunk_size)
# Setting the step size of the small chunk (50% overlapping windows are supported)
small_chunk_step = small_chunk_size
if overlap_small_chunk:
small_chunk_step = small_chunk_size / 2
# Computing the length (in sample) of the small_chunk step size
small_chunk_len_step = int(sample_rate * small_chunk_step)
# Loop over big chunks
prob_chunks = []
last_chunk = False
begin_sample = 0
while True:
# Check if the current chunk is the last one
if begin_sample + long_chunk_len >= audio_len:
last_chunk = True
# Reading the big chunk
large_chunk, fs = torchaudio.load(
str(audio_file),
frame_offset=begin_sample,
num_frames=long_chunk_len,
)
large_chunk = large_chunk.to(self.device)
# Manage padding of the last small chunk
if last_chunk or large_chunk.shape[-1] < small_chunk_len:
padding = torch.zeros(
1, small_chunk_len, device=large_chunk.device
)
large_chunk = torch.cat([large_chunk, padding], dim=1)
# Splitting the big chunk into smaller (overlapped) ones
small_chunks = torch.nn.functional.unfold(
large_chunk.unsqueeze(1).unsqueeze(2),
kernel_size=(1, small_chunk_len),
stride=(1, small_chunk_len_step),
)
small_chunks = small_chunks.squeeze(0).transpose(0, 1)
# Getting (in parallel) the frame-level speech probabilities
small_chunks_prob = self.get_speech_prob_chunk(small_chunks)
small_chunks_prob = small_chunks_prob[:, :-1, :]
# Manage overlapping chunks
if overlap_small_chunk:
small_chunks_prob = self._manage_overlapped_chunks(
small_chunks_prob
)
# Prepare for folding
small_chunks_prob = small_chunks_prob.permute(2, 1, 0)
# Computing lengths in samples
out_len = int(
large_chunk.shape[-1] / (sample_rate * self.time_resolution)
)
kernel_len = int(small_chunk_size / self.time_resolution)
step_len = int(small_chunk_step / self.time_resolution)
# Folding the frame-level predictions
small_chunks_prob = torch.nn.functional.fold(
small_chunks_prob,
output_size=(1, out_len),
kernel_size=(1, kernel_len),
stride=(1, step_len),
)
# Appending the frame-level speech probabilities of the large chunk
small_chunks_prob = small_chunks_prob.squeeze(1).transpose(-1, -2)
prob_chunks.append(small_chunks_prob)
# Check stop condition
if last_chunk:
break
# Update counter to process the next big chunk
begin_sample = begin_sample + long_chunk_len
# Converting the list to a tensor
prob_vad = torch.cat(prob_chunks, dim=1)
last_elem = int(audio_len / (self.time_resolution * sample_rate))
prob_vad = prob_vad[:, 0:last_elem, :]
return prob_vad
def _manage_overlapped_chunks(self, small_chunks_prob):
"""This support function manages overlapped the case in which the
small chunks have a 50% overlap."""
# Weighting the frame-level probabilities with a hamming window
# reduces uncertainty when overlapping chunks are used.
hamming_window = torch.hamming_window(
small_chunks_prob.shape[1], device=self.device
)
# First and last chunks require special care
half_point = int(small_chunks_prob.shape[1] / 2)
small_chunks_prob[0, half_point:] = small_chunks_prob[
0, half_point:
] * hamming_window[half_point:].unsqueeze(1)
small_chunks_prob[-1, 0:half_point] = small_chunks_prob[
-1, 0:half_point
] * hamming_window[0:half_point].unsqueeze(1)
# Applying the window to all the other probabilities
small_chunks_prob[1:-1] = small_chunks_prob[
1:-1
] * hamming_window.unsqueeze(0).unsqueeze(2)
return small_chunks_prob
[docs]
def get_speech_prob_chunk(self, wavs, wav_lens=None):
"""Outputs the frame-level posterior probability for the input audio chunks
Outputs close to zero refers to time steps with a low probability of speech
activity, while outputs closer to one likely contain speech.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model. Make sure the sample rate is fs=16000 Hz.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
# Manage single waveforms in input
if len(wavs.shape) == 1:
wavs = wavs.unsqueeze(0)
# Assign full length if wav_lens is not assigned
if wav_lens is None:
wav_lens = torch.ones(wavs.shape[0], device=self.device)
# Storing waveform in the specified device
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
wavs = wavs.float()
# Computing features and embeddings
feats = self.mods.compute_features(wavs)
feats = self.mods.mean_var_norm(feats, wav_lens)
outputs = self.mods.cnn(feats)
outputs = outputs.reshape(
outputs.shape[0],
outputs.shape[1],
outputs.shape[2] * outputs.shape[3],
)
outputs, h = self.mods.rnn(outputs)
outputs = self.mods.dnn(outputs)
output_prob = torch.sigmoid(outputs)
return output_prob
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def apply_threshold(
self, vad_prob, activation_th=0.5, deactivation_th=0.25
):
"""Scans the frame-level speech probabilities and applies a threshold
on them. Speech starts when a value larger than activation_th is
detected, while it ends when observing a value lower than
the deactivation_th.
Arguments
---------
vad_prob: torch.Tensor
Frame-level speech probabilities.
activation_th: float
Threshold for starting a speech segment.
deactivation_th: float
Threshold for ending a speech segment.
Returns
-------
vad_th: torch.Tensor
Tensor containing 1 for speech regions and 0 for non-speech regions.
"""
vad_activation = (vad_prob >= activation_th).int()
vad_deactivation = (vad_prob >= deactivation_th).int()
vad_th = vad_activation + vad_deactivation
# Loop over batches and time steps
for batch in range(vad_th.shape[0]):
for time_step in range(vad_th.shape[1] - 1):
if (
vad_th[batch, time_step] == 2
and vad_th[batch, time_step + 1] == 1
):
vad_th[batch, time_step + 1] = 2
vad_th[vad_th == 1] = 0
vad_th[vad_th == 2] = 1
return vad_th
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def get_boundaries(self, prob_th, output_value="seconds"):
"""Computes the time boundaries where speech activity is detected.
It takes in input frame-level binary decisions
(1 for speech, 0 for non-speech) and outputs the begin/end second
(or sample) of each detected speech region.
Arguments
---------
prob_th: torch.Tensor
Frame-level binary decisions (1 for speech frame, 0 for a
non-speech one). The tensor can be obtained from apply_threshold.
output_value: 'seconds' or 'samples'
When the option 'seconds' is set, the returned boundaries are in
seconds, otherwise, it reports them in samples.
Returns
-------
boundaries: torch.Tensor
Tensor containing the start second (or sample) of speech segments
in even positions and their corresponding end in odd positions
(e.g, [1.0, 1.5, 5,.0 6.0] means that we have two speech segment;
one from 1.0 to 1.5 seconds and another from 5.0 to 6.0 seconds).
"""
# Shifting frame-levels binary decision by 1
# This allows detecting changes in speech/non-speech activities
prob_th_shifted = torch.roll(prob_th, dims=1, shifts=1)
prob_th_shifted[:, 0, :] = 0
prob_th = prob_th + prob_th_shifted
# Needed to first and last time step
prob_th[:, 0, :] = (prob_th[:, 0, :] >= 1).int()
prob_th[:, -1, :] = (prob_th[:, -1, :] >= 1).int()
# Fix edge cases (when a speech starts in the last frames)
if (prob_th == 1).nonzero().shape[0] % 2 == 1:
prob_th = torch.cat(
(
prob_th,
torch.Tensor([1.0])
.unsqueeze(0)
.unsqueeze(2)
.to(self.device),
),
dim=1,
)
# Where prob_th is 1 there is a change
indexes = (prob_th == 1).nonzero()[:, 1].reshape(-1, 2)
# Remove 1 from end samples
indexes[:, -1] = indexes[:, -1] - 1
# From indexes to samples
seconds = (indexes * self.time_resolution).float()
samples = (self.sample_rate * seconds).round().int()
if output_value == "seconds":
boundaries = seconds
else:
boundaries = samples
return boundaries
[docs]
def merge_close_segments(self, boundaries, close_th=0.250):
"""Merges segments that are shorter than the given threshold.
Arguments
---------
boundaries : str
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
close_th: float
If the distance between boundaries is smaller than close_th, the
segments will be merged.
Returns
-------
new_boundaries
The new boundaries with the merged segments.
"""
new_boundaries = []
# Single segment case
if boundaries.shape[0] == 0:
return boundaries
# Getting beg and end of previous segment
prev_beg_seg = boundaries[0, 0].float()
prev_end_seg = boundaries[0, 1].float()
# Process all the segments
for i in range(1, boundaries.shape[0]):
beg_seg = boundaries[i, 0]
segment_distance = beg_seg - prev_end_seg
# Merging close segments
if segment_distance <= close_th:
prev_end_seg = boundaries[i, 1]
else:
# Appending new segments
new_boundaries.append([prev_beg_seg, prev_end_seg])
prev_beg_seg = beg_seg
prev_end_seg = boundaries[i, 1]
new_boundaries.append([prev_beg_seg, prev_end_seg])
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
[docs]
def remove_short_segments(self, boundaries, len_th=0.250):
"""Removes segments that are too short.
Arguments
---------
boundaries : torch.Tensor
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
len_th: float
If the length of the segment is smaller than close_th, the segments
will be merged.
Returns
-------
new_boundaries
The new boundaries without the short segments.
"""
new_boundaries = []
# Process the segments
for i in range(boundaries.shape[0]):
# Computing segment length
seg_len = boundaries[i, 1] - boundaries[i, 0]
# Accept segment only if longer than len_th
if seg_len > len_th:
new_boundaries.append([boundaries[i, 0], boundaries[i, 1]])
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
[docs]
def save_boundaries(
self, boundaries, save_path=None, print_boundaries=True, audio_file=None
):
"""Saves the boundaries on a file (and/or prints them) in a readable format.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
save_path: path
When to store the text file containing the speech/non-speech intervals.
print_boundaries: Bool
Prints the speech/non-speech intervals in the standard outputs.
audio_file: path
Path of the audio file containing the recording. The file is read
with torchaudio. It is used here to detect the length of the
signal.
"""
# Create a new file if needed
if save_path is not None:
f = open(save_path, mode="w", encoding="utf-8")
# Getting the total size of the input file
if audio_file is not None:
sample_rate, audio_len = self._get_audio_info(audio_file)
audio_len = audio_len / sample_rate
# Setting the rights format for second- or sample-based boundaries
if boundaries.dtype == torch.int:
value_format = "% i"
else:
value_format = "% .2f "
# Printing speech and non-speech intervals
last_end = 0
cnt_seg = 0
for i in range(boundaries.shape[0]):
begin_value = boundaries[i, 0]
end_value = boundaries[i, 1]
if last_end != begin_value:
cnt_seg = cnt_seg + 1
print_str = (
"segment_%03d " + value_format + value_format + "NON_SPEECH"
)
if print_boundaries:
print(print_str % (cnt_seg, last_end, begin_value))
if save_path is not None:
f.write(print_str % (cnt_seg, last_end, begin_value) + "\n")
cnt_seg = cnt_seg + 1
print_str = "segment_%03d " + value_format + value_format + "SPEECH"
if print_boundaries:
print(print_str % (cnt_seg, begin_value, end_value))
if save_path is not None:
f.write(print_str % (cnt_seg, begin_value, end_value) + "\n")
last_end = end_value
# Managing last segment
if audio_file is not None:
if last_end < audio_len:
cnt_seg = cnt_seg + 1
print_str = (
"segment_%03d " + value_format + value_format + "NON_SPEECH"
)
if print_boundaries:
print(print_str % (cnt_seg, end_value, audio_len))
if save_path is not None:
f.write(print_str % (cnt_seg, end_value, audio_len) + "\n")
if save_path is not None:
f.close()
[docs]
def energy_VAD(
self,
audio_file,
boundaries,
activation_th=0.5,
deactivation_th=0.0,
eps=1e-6,
):
"""Applies energy-based VAD within the detected speech segments.The neural
network VAD often creates longer segments and tends to merge segments that
are close with each other.
The energy VAD post-processes can be useful for having a fine-grained voice
activity detection.
The energy VAD computes the energy within the small chunks. The energy is
normalized within the segment to have mean 0.5 and +-0.5 of std.
This helps to set the energy threshold.
Arguments
---------
audio_file: path
Path of the audio file containing the recording. The file is read
with torchaudio.
boundaries : torch.Tensor
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
activation_th: float
A new speech segment is started it the energy is above activation_th.
deactivation_th: float
The segment is considered ended when the energy is <= deactivation_th.
eps: float
Small constant for numerical stability.
Returns
-------
new_boundaries
The new boundaries that are post-processed by the energy VAD.
"""
# Getting the total size of the input file
sample_rate, audio_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
# Computing the chunk length of the energy window
chunk_len = int(self.time_resolution * sample_rate)
new_boundaries = []
# Processing speech segments
for i in range(boundaries.shape[0]):
begin_sample = int(boundaries[i, 0] * sample_rate)
end_sample = int(boundaries[i, 1] * sample_rate)
seg_len = end_sample - begin_sample
# Reading the speech segment
segment, _ = torchaudio.load(
audio_file, frame_offset=begin_sample, num_frames=seg_len
)
# Create chunks
segment_chunks = self.create_chunks(
segment, chunk_size=chunk_len, chunk_stride=chunk_len
)
# Energy computation within each chunk
energy_chunks = segment_chunks.abs().sum(-1) + eps
energy_chunks = energy_chunks.log()
# Energy normalization
energy_chunks = (
(energy_chunks - energy_chunks.mean())
/ (2 * energy_chunks.std())
) + 0.5
energy_chunks = energy_chunks.unsqueeze(0).unsqueeze(2)
# Apply threshold based on the energy value
energy_vad = self.apply_threshold(
energy_chunks,
activation_th=activation_th,
deactivation_th=deactivation_th,
)
# Get the boundaries
energy_boundaries = self.get_boundaries(
energy_vad, output_value="seconds"
)
# Get the final boundaries in the original signal
for j in range(energy_boundaries.shape[0]):
start_en = boundaries[i, 0] + energy_boundaries[j, 0]
end_end = boundaries[i, 0] + energy_boundaries[j, 1]
new_boundaries.append([start_en, end_end])
# Convert boundaries to tensor
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
[docs]
def create_chunks(self, x, chunk_size=16384, chunk_stride=16384):
"""Splits the input into smaller chunks of size chunk_size with
an overlap chunk_stride. The chunks are concatenated over
the batch axis.
Arguments
---------
x: torch.Tensor
Signal to split into chunks.
chunk_size : str
The size of each chunk.
chunk_stride:
The stride (hop) of each chunk.
Returns
-------
x: torch.Tensor
A new tensors with the chunks derived from the input signal.
"""
x = x.unfold(1, chunk_size, chunk_stride)
x = x.reshape(x.shape[0] * x.shape[1], -1)
return x
def _get_audio_info(self, audio_file):
"""Returns the sample rate and the length of the input audio file"""
# Getting the total size of the input file
metadata = torchaudio.info(str(audio_file))
sample_rate = metadata.sample_rate
audio_len = metadata.num_frames
return sample_rate, audio_len
[docs]
def upsample_VAD(self, vad_out, audio_file, time_resolution=0.01):
"""Upsamples the output of the vad to help visualization. It creates a
signal that is 1 when there is speech and 0 when there is no speech.
The vad signal has the same resolution as the input one and can be
opened with it (e.g, using audacity) to visually figure out VAD regions.
Arguments
---------
vad_out: torch.Tensor
Tensor containing 1 for each frame of speech and 0 for each non-speech
frame.
audio_file: path
The original audio file used to compute vad_out
time_resolution : float
Time resolution of the vad_out signal.
Returns
-------
vad_signal
The upsampled version of the vad_out tensor.
"""
# Getting the total size of the input file
sample_rate, sig_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
beg_samp = 0
step_size = int(time_resolution * sample_rate)
end_samp = step_size
index = 0
# Initialize upsampled signal
vad_signal = torch.zeros(1, sig_len, device=vad_out.device)
# Upsample signal
while end_samp < sig_len:
vad_signal[0, beg_samp:end_samp] = vad_out[0, index, 0]
index = index + 1
beg_samp = beg_samp + step_size
end_samp = beg_samp + step_size
return vad_signal
[docs]
def upsample_boundaries(self, boundaries, audio_file):
"""Based on the input boundaries, this method creates a signal that is 1
when there is speech and 0 when there is no speech.
The vad signal has the same resolution as the input one and can be
opened with it (e.g, using audacity) to visually figure out VAD regions.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the boundaries of the speech segments.
audio_file: path
The original audio file used to compute vad_out
Returns
-------
vad_signal
The output vad signal with the same resolution of the input one.
"""
# Getting the total size of the input file
sample_rate, sig_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
# Initialization of the output signal
vad_signal = torch.zeros(1, sig_len, device=boundaries.device)
# Composing the vad signal from boundaries
for i in range(boundaries.shape[0]):
beg_sample = int(boundaries[i, 0] * sample_rate)
end_sample = int(boundaries[i, 1] * sample_rate)
vad_signal[0, beg_sample:end_sample] = 1.0
return vad_signal
[docs]
def double_check_speech_segments(
self, boundaries, audio_file, speech_th=0.5
):
"""Takes in input the boundaries of the detected speech segments and
double checks (using the neural VAD) that they actually contain speech.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the boundaries of the speech segments.
audio_file: path
The original audio file used to compute vad_out.
speech_th: float
Threshold on the mean posterior probability over which speech is
confirmed. Below that threshold, the segment is re-assigned to a
non-speech region.
Returns
-------
new_boundaries
The boundaries of the segments where speech activity is confirmed.
"""
# Getting the total size of the input file
sample_rate, sig_len = self._get_audio_info(audio_file)
# Double check the segments
new_boundaries = []
for i in range(boundaries.shape[0]):
beg_sample = int(boundaries[i, 0] * sample_rate)
end_sample = int(boundaries[i, 1] * sample_rate)
len_seg = end_sample - beg_sample
# Read the candidate speech segment
segment, fs = torchaudio.load(
str(audio_file), frame_offset=beg_sample, num_frames=len_seg
)
speech_prob = self.get_speech_prob_chunk(segment)
if speech_prob.mean() > speech_th:
# Accept this as a speech segment
new_boundaries.append([boundaries[i, 0], boundaries[i, 1]])
# Convert boundaries from list to tensor
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
[docs]
def get_segments(
self, boundaries, audio_file, before_margin=0.1, after_margin=0.1
):
"""Returns a list containing all the detected speech segments.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the boundaries of the speech segments.
audio_file: path
The original audio file used to compute vad_out.
before_margin: float
Used to cut the segments samples a bit before the detected margin.
after_margin: float
Use to cut the segments samples a bit after the detected margin.
Returns
-------
segments: list
List containing the detected speech segments
"""
sample_rate, sig_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
segments = []
for i in range(boundaries.shape[0]):
beg_sample = boundaries[i, 0] * sample_rate
end_sample = boundaries[i, 1] * sample_rate
beg_sample = int(max(0, beg_sample - before_margin * sample_rate))
end_sample = int(
min(sig_len, end_sample + after_margin * sample_rate)
)
len_seg = end_sample - beg_sample
vad_segment, fs = torchaudio.load(
audio_file, frame_offset=beg_sample, num_frames=len_seg
)
segments.append(vad_segment)
return segments
[docs]
def get_speech_segments(
self,
audio_file,
large_chunk_size=30,
small_chunk_size=10,
overlap_small_chunk=False,
apply_energy_VAD=False,
double_check=True,
close_th=0.250,
len_th=0.250,
activation_th=0.5,
deactivation_th=0.25,
en_activation_th=0.5,
en_deactivation_th=0.0,
speech_th=0.50,
):
"""Detects speech segments within the input file. The input signal can
be both a short or a long recording. The function computes the
posterior probabilities on large chunks (e.g, 30 sec), that are read
sequentially (to avoid storing big signals in memory).
Each large chunk is, in turn, split into smaller chunks (e.g, 10 seconds)
that are processed in parallel. The pipeline for detecting the speech
segments is the following:
1- Compute posteriors probabilities at the frame level.
2- Apply a threshold on the posterior probability.
3- Derive candidate speech segments on top of that.
4- Apply energy VAD within each candidate segment (optional).
5- Merge segments that are too close.
6- Remove segments that are too short.
7- Double check speech segments (optional).
Arguments
---------
audio_file : str
Path to audio file.
large_chunk_size: float
Size (in seconds) of the large chunks that are read sequentially
from the input audio file.
small_chunk_size: float
Size (in seconds) of the small chunks extracted from the large ones.
The audio signal is processed in parallel within the small chunks.
Note that large_chunk_size/small_chunk_size must be an integer.
overlap_small_chunk: bool
If True, it creates overlapped small chunks (with 50% overlap).
The probabilities of the overlapped chunks are combined using
hamming windows.
apply_energy_VAD: bool
If True, a energy-based VAD is used on the detected speech segments.
The neural network VAD often creates longer segments and tends to
merge close segments together. The energy VAD post-processes can be
useful for having a fine-grained voice activity detection.
The energy thresholds is managed by activation_th and
deactivation_th (see below).
double_check: bool
If True, double checks (using the neural VAD) that the candidate
speech segments actually contain speech. A threshold on the mean
posterior probabilities provided by the neural network is applied
based on the speech_th parameter (see below).
activation_th: float
Threshold of the neural posteriors above which starting a speech segment.
deactivation_th: float
Threshold of the neural posteriors below which ending a speech segment.
en_activation_th: float
A new speech segment is started it the energy is above activation_th.
This is active only if apply_energy_VAD is True.
en_deactivation_th: float
The segment is considered ended when the energy is <= deactivation_th.
This is active only if apply_energy_VAD is True.
speech_th: float
Threshold on the mean posterior probability within the candidate
speech segment. Below that threshold, the segment is re-assigned to
a non-speech region. This is active only if double_check is True.
close_th: float
If the distance between boundaries is smaller than close_th, the
segments will be merged.
len_th: float
If the length of the segment is smaller than close_th, the segments
will be merged.
Returns
-------
boundaries: torch.Tensor
Tensor containing the start second of speech segments in even
positions and their corresponding end in odd positions
(e.g, [1.0, 1.5, 5,.0 6.0] means that we have two speech segment;
one from 1.0 to 1.5 seconds and another from 5.0 to 6.0 seconds).
"""
# Fetch audio file from web if not local
source, fl = split_path(audio_file)
audio_file = fetch(fl, source=source)
# Computing speech vs non speech probabilities
prob_chunks = self.get_speech_prob_file(
audio_file,
large_chunk_size=large_chunk_size,
small_chunk_size=small_chunk_size,
overlap_small_chunk=overlap_small_chunk,
)
# Apply a threshold to get candidate speech segments
prob_th = self.apply_threshold(
prob_chunks,
activation_th=activation_th,
deactivation_th=deactivation_th,
).float()
# Compute the boundaries of the speech segments
boundaries = self.get_boundaries(prob_th, output_value="seconds")
# Apply energy-based VAD on the detected speech segments
if apply_energy_VAD:
boundaries = self.energy_VAD(
audio_file,
boundaries,
activation_th=en_activation_th,
deactivation_th=en_deactivation_th,
)
# Merge short segments
boundaries = self.merge_close_segments(boundaries, close_th=close_th)
# Remove short segments
boundaries = self.remove_short_segments(boundaries, len_th=len_th)
# Double check speech segments
if double_check:
boundaries = self.double_check_speech_segments(
boundaries, audio_file, speech_th=speech_th
)
return boundaries
[docs]
def forward(self, wavs, wav_lens=None):
"""Gets frame-level speech-activity predictions"""
return self.get_speech_prob_chunk(wavs, wav_lens)