vad_utils.py

# @title Define funcs
import torch
import torchaudio
from typing import Callable, List
import torch.nn.functional as F
import warnings
import pandas as pd
from matplotlib import pyplot as plt

def get_speech_probs(audio: torch.Tensor,
                        #   model,
                          threshold: float = 0.5,
                          sampling_rate: int = 16000,
                          window_size_samples: int = 512,
                          progress_tracking_callback: Callable[[float], None] = None):
    if not torch.is_tensor(audio):
        try:
            audio = torch.Tensor(audio)
        except:
            raise TypeError("Audio cannot be casted to tensor. Cast it manually")

    if len(audio.shape) > 1:
        for i in range(len(audio.shape)):  # trying to squeeze empty dimensions
            audio = audio.squeeze(0)
        if len(audio.shape) > 1:
            raise ValueError("More than one dimension in audio. Are you trying to process audio with 2 channels?")

    if sampling_rate > 16000 and (sampling_rate % 16000 == 0):
        step = sampling_rate // 16000
        sampling_rate = 16000
        audio = audio[::step]
        warnings.warn('Sampling rate is a multiply of 16000, casting to 16000 manually!')
    else:
        step = 1

    if sampling_rate == 8000 and window_size_samples > 768:
        warnings.warn('window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 768 for 8000 sample rate!')
    if window_size_samples not in [256, 512, 768, 1024, 1536]:
        warnings.warn('Unusual window_size_samples! Supported window_size_samples:\n - [512, 1024, 1536] for 16000 sampling_rate\n - [256, 512, 768] for 8000 sampling_rate')

    model.reset_states()

    audio_length_samples = len(audio)

    speech_probs = []
    for current_start_sample in range(0, audio_length_samples, window_size_samples):
        chunk = audio[current_start_sample: current_start_sample + window_size_samples]
        if len(chunk) < window_size_samples:
            chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk))))
        speech_prob = model(chunk, sampling_rate).item()
        speech_probs.append(speech_prob)
        # caculate progress and seng it to callback function
        progress = current_start_sample + window_size_samples
        if progress > audio_length_samples:
            progress = audio_length_samples
        progress_percent = (progress / audio_length_samples) * 100
        if progress_tracking_callback:
            progress_tracking_callback(progress_percent)
    return speech_probs

def probs2speech_timestamps(speech_probs, audio_length_samples,
                          threshold: float = 0.5,
                          sampling_rate: int = 16000,
                          min_speech_duration_ms: int = 250,
                          max_speech_duration_s: float = float('inf'),
                          min_silence_duration_ms: int = 100,
                          window_size_samples: int = 512,
                          speech_pad_ms: int = 30,
                          return_seconds: bool = False,
                          rounding: int = 1,):

    step = sampling_rate // 16000

    min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
    speech_pad_samples = sampling_rate * speech_pad_ms / 1000
    max_speech_samples = sampling_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples
    min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
    min_silence_samples_at_max_speech = sampling_rate * 98 / 1000

    triggered = False
    speeches = []
    current_speech = {}
    neg_threshold = threshold - 0.15
    temp_end = 0 # to save potential segment end (and tolerate some silence)
    prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached

    for i, speech_prob in enumerate(speech_probs):
        if (speech_prob >= threshold) and temp_end:
            temp_end = 0
            if next_start < prev_end:
               next_start = window_size_samples * i

        if (speech_prob >= threshold) and not triggered:
            triggered = True
            current_speech['start'] = window_size_samples * i
            continue

        if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
            if prev_end:
                current_speech['end'] = prev_end
                speeches.append(current_speech)
                current_speech = {}
                if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres)
                    triggered = False
                else:
                    current_speech['start'] = next_start
                prev_end = next_start = temp_end = 0
            else:
                current_speech['end'] = window_size_samples * i
                speeches.append(current_speech)
                current_speech = {}
                prev_end = next_start = temp_end = 0
                triggered = False
                continue

        if (speech_prob < neg_threshold) and triggered:
            if not temp_end:
                temp_end = window_size_samples * i
            if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech : # condition to avoid cutting in very short silence
                prev_end = temp_end
            if (window_size_samples * i) - temp_end < min_silence_samples:
                continue
            else:
                current_speech['end'] = temp_end
                if (current_speech['end'] - current_speech['start']) > min_speech_samples:
                    speeches.append(current_speech)
                current_speech = {}
                prev_end = next_start = temp_end = 0
                triggered = False
                continue

    if current_speech and (audio_length_samples - current_speech['start']) > min_speech_samples:
        current_speech['end'] = audio_length_samples
        speeches.append(current_speech)

    for i, speech in enumerate(speeches):
        if i == 0:
            speech['start'] = int(max(0, speech['start'] - speech_pad_samples))
        if i != len(speeches) - 1:
            silence_duration = speeches[i+1]['start'] - speech['end']
            if silence_duration < 2 * speech_pad_samples:
                speech['end'] += int(silence_duration // 2)
                speeches[i+1]['start'] = int(max(0, speeches[i+1]['start'] - silence_duration // 2))
            else:
                speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))
                speeches[i+1]['start'] = int(max(0, speeches[i+1]['start'] - speech_pad_samples))
        else:
            speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))

    if return_seconds:
        for speech_dict in speeches:
            speech_dict['start'] = round(speech_dict['start'] / sampling_rate, rounding)
            speech_dict['end'] = round(speech_dict['end'] / sampling_rate, rounding)
    elif step > 1:
        for speech_dict in speeches:
            speech_dict['start'] *= step
            speech_dict['end'] *= step
    return speeches

def make_visualization(probs, step):
    fig, ax = plt.subplots(figsize=(16, 8),)

    pd.DataFrame({'probs': probs},
                 index=[x * step for x in range(len(probs))]).plot(ax = ax,
                 kind='area', ylim=[0, 1.05], xlim=[0, len(probs) * step],
                 xlabel='seconds',
                 ylabel='speech probability',
                 colormap='tab20')
    return fig
    
torch.set_num_threads(1)


USE_ONNX = True # change this to True if you want to test onnx model


model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                            model='silero_vad',
                            # force_reload=True,
                            onnx=USE_ONNX)
(_,
_, read_audio,
*_) = utils