Streaming voice activity detection with

Hacking with streamz library for real-time voice activity detection


Hervé Bredin


August 5, 2021

In this blog post, I describe an early attempt at performing live voice activity detection with pretrained segmentation model.


# setting up for pretty visualization

%matplotlib inline
import matplotlib.pyplot as plt

from pyannote.core import notebook, Segment, SlidingWindow
from pyannote.core import SlidingWindowFeature as SWF
notebook.crop = Segment(0, 10)

def visualize(features):
    figsize = plt.rcParams["figure.figsize"]
    plt.rcParams["figure.figsize"] = (notebook.width, 2)

Rolling audio buffer

Let us assume that the audio stream is given as a 5s rolling buffer.
Here, we are going to fake it by sliding a 5s window over the duration of an audio file.

from import Audio, AudioFile

class RollingAudioBuffer(Audio):
    """Rolling audio buffer
    sample_rate : int
        Sample rate
    duration : float, optional
        Duration of rolling buffer. Defaults to 5s.
    step : float, optional
        Delay between two updates of the rolling buffer. Defaults to 1s.

    >>> buffer = RollingAudioBuffer()("audio.wav")
    >>> current_buffer = next(buffer)
    def __init__(self, sample_rate=16000, duration=5.0, step=1.):
        super().__init__(sample_rate=sample_rate, mono=True)
        self.duration = duration
        self.step = step
    def __call__(self, file: AudioFile):
        # duration of the whole audio file
        duration = self.get_duration(file)
        # slide a 5s window from the beginning to the end of the file
        window = SlidingWindow(start=0., duration=self.duration, step=self.step, end=duration)
        for chunk in window:
            # for each position of the window, yield the corresponding audio buffer
            # as a SlidingWindowFeature instance
            waveform, sample_rate = self.crop(file, chunk, duration=self.duration)
            resolution = SlidingWindow(start=chunk.start, 
            yield SWF(waveform.T, resolution)

We start by initializing rolling buffer on a sample file:

MY_AUDIO_FILE = "DH_0001.flac"
buffer = RollingAudioBuffer()(MY_AUDIO_FILE)

Each subsequent call to next(buffer) returns the current content of the 5s rolling buffer:




For illustration purposes, we also load the manual voice activity reference.

from pyannote.database.util import load_rttm
reference = load_rttm('DH_0001.rttm').popitem()[1].get_timeline()

Pretrained voice activity detection model comes with a decent pretrained segmentation model that can be used for voice activity detection.

import torch
import numpy as np
from import Model

class VoiceActivityDetection:
    def __init__(self):
        self.model = Model.from_pretrained("pyannote/segmentation")
    def __call__(self, current_buffer: SWF) -> SWF:
        # we start by applying the model on the current buffer
        with torch.no_grad():
            waveform =
            segmentation = self.model(waveform[np.newaxis]).numpy()[0]

        # temporal resolution of the output of the model
        resolution = self.model.introspection.frames
        # temporal shift to keep track of current buffer start time
        resolution = SlidingWindow(start=current_buffer.sliding_window.start, 
        # pyannote/segmentation pretrained model actually does more than just voice activity detection
        # see for more details.     
        speech_probability = np.max(segmentation, axis=-1, keepdims=True)
        return SWF(speech_probability, resolution)
vad = VoiceActivityDetection()

Let us try this thing on current buffer:

current_buffer = next(buffer)



Building a basic streaming pipeline with streamz

We now have a way to stream audio and apply voice activity detection.
According to its documentation, streamz seems like a good option to do that:

Streamz helps you build pipelines to manage continuous streams of data.

Let us start by creating a Stream that will ingest the rolling buffer and apply voice activity detection anytime the buffer is updated.

from streamz import Stream
source = Stream()

We re-initialize the audio buffer from the start of the file and push the rolling buffer into the pipeline:

buffer = RollingAudioBuffer()(MY_AUDIO_FILE)




Controlling latency / accuracy trade-off

This is nice but we can do better in case the pipeline is allowed a small delay (a.k.a. latency) between when it receives the audio and when it outputs the voice activity detection scores.

For instance, if we are allowed 2s latency, we could benefit from the multiple overlapping buffers and combine them to get a better estimate of the speech probability in regions where the model is not quite confident (e.g. just before t=4s).

This is what the Aggregation class does.

from typing import Tuple, List

class Aggregation:
    """Aggregate multiple overlapping buffers with a 
    latency : float, optional
        Allowed latency, in seconds. Defaults to 0.
    def __init__(self, latency=0.0):
        self.latency = latency
    def __call__(self, internal_state, current_buffer: SWF) -> Tuple[Tuple[float, List[SWF]], SWF]:
        """Ingest new buffer and return aggregated output with delay

        internal_state : (internal_time, past_buffers) tuple
            `internal_time` is a float such that previous call emitted aggregated scores up 
            to time `delayed_time`.  `past_buffers` is a rolling list of past buffers that 
            we are going to aggregate.
        current_buffer : SlidingWindowFeature
            New incoming score buffer.

        if internal_state is None:
            internal_state = (0.0, list())
        # previous call led to the emission of aggregated scores up to time `delayed_time`
        # `past_buffers` is a rolling list of past buffers that we are going to aggregate
        delayed_time, past_buffers = internal_state
        # real time is the current end time of the audio buffer
        # (here, estimated from the end time of the VAD buffer)
        real_time = current_buffer.extent.end
        # because we are only allowed `self.latency` seconds of latency, this call should
        # return aggregated scores for [delayed_time, real_time - latency] time range. 
        required = Segment(delayed_time, real_time - self.latency)
        # to compute more robust scores, we will combine all buffers that have a non-empty
        # temporal intersection with required time range. we can get rid of the others as they
        # will no longer be needed as they are too far away in the past.
        past_buffers = [buffer for buffer in past_buffers if buffer.extent.end > required.start] + [current_buffer]
        # we aggregate all past buffers (but only on the 'required' region of interest)
        intersection = np.stack([buffer.crop(required, fixed=required.duration) for buffer in past_buffers])
        aggregation = np.mean(intersection, axis=0)
        # ... and wrap it into a self-contained SlidingWindowFeature (SWF) instance
        resolution = current_buffer.sliding_window
        resolution = SlidingWindow(start=required.start, duration=resolution.duration, step=resolution.step)
        output = SWF(aggregation, resolution)
        # we update the internal state
        delayed_time = real_time - self.latency
        internal_state = (delayed_time, past_buffers)
        # ... and return the whole thing for next call to know where we are
        return internal_state, output

Let’s add this new accumulator into the streaming pipeline, with a 2s latency:

source = Stream()
source \
    .map(vad) \
    .accumulate(Aggregation(latency=2.), returns_state=True, start=None) \

buffer = RollingAudioBuffer()(MY_AUDIO_FILE)
# first [0s, 5s] buffer
current_buffer = next(buffer); current_buffer

# ... and its corresponding output up to 3s (= 5s - latency)

# next [1s, 6s] buffer
current_buffer = next(buffer); current_buffer

# ... and its corresponding [3s, 6s - latency = 4s] output

# next [2s, 7s] buffer
current_buffer = next(buffer); current_buffer

# ... and its corresponding [4s, 7s - latency = 5s] output

Look how the aggregation process actually refined the speech probability just before t=4s. This has been enabled by the longer latency.

That’s all folks!

For technical questions and bug reports, please check Github repository.

I also offer scientific consulting services around speaker diarization (and speech processing in general), please contact me if you think this type of technology might help your business/startup!

Bonus: concatenating output

For visualization purposes, you might want to add an accumulator to the pipeline that takes care of concatenating the output of each step…

class Concatenation:
    def __call__(self, concatenation: SWF, current_buffer: SWF) -> Tuple[SWF, SWF]:
        if concatenation is None:
            return current_buffer, current_buffer
        resolution = concatenation.sliding_window
        current_start_frame = resolution.closest_frame(current_buffer.extent.start)
        current_end_frame = current_start_frame + len(current_buffer)
        = np.pad(, ((0, current_end_frame - len(, (0, 0)))[current_start_frame: current_end_frame] =        
        return concatenation, concatenation
source = Stream()
source \
    .map(vad) \
    .accumulate(Aggregation(latency=2.), returns_state=True, start=None) \
    .accumulate(Concatenation(), returns_state=True, start=None) \

buffer = RollingAudioBuffer()(MY_AUDIO_FILE)
notebook.crop = Segment(0, 30)
for _ in range(30):