utils.py

import numpy as np
import torch


def next_power_of_2(x):  
    return 1 if x == 0 else 2**(x - 1).bit_length()


def int2float(sound):
    """
    Taken from https://github.com/snakers4/silero-vad
    """

    abs_max = np.abs(sound).max()
    sound = sound.astype('float32')
    if abs_max > 0:
        sound *= 1/32768
    sound = sound.squeeze()  # depends on the use case
    return sound


class VADIterator:
    def __init__(self,
                 model,
                 threshold: float = 0.5,
                 sampling_rate: int = 16000,
                 min_silence_duration_ms: int = 100,
                 speech_pad_ms: int = 30
                 ):

        """
        Mainly taken from https://github.com/snakers4/silero-vad
        Class for stream imitation

        Parameters
        ----------
        model: preloaded .jit/.onnx silero VAD model

        threshold: float (default - 0.5)
            Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
            It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.

        sampling_rate: int (default - 16000)
            Currently silero VAD models support 8000 and 16000 sample rates

        min_silence_duration_ms: int (default - 100 milliseconds)
            In the end of each speech chunk wait for min_silence_duration_ms before separating it

        speech_pad_ms: int (default - 30 milliseconds)
            Final speech chunks are padded by speech_pad_ms each side
        """

        self.model = model
        self.threshold = threshold
        self.sampling_rate = sampling_rate
        self.is_speaking = False
        self.buffer = []

        if sampling_rate not in [8000, 16000]:
            raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')

        self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
        self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
        self.reset_states()

    def reset_states(self):

        self.model.reset_states()
        self.triggered = False
        self.temp_end = 0
        self.current_sample = 0

    @torch.no_grad()
    def __call__(self, x):
        """
        x: torch.Tensor
            audio chunk (see examples in repo)

        return_seconds: bool (default - False)
            whether return timestamps in seconds (default - samples)
        """

        if not torch.is_tensor(x):
            try:
                x = torch.Tensor(x)
            except:
                raise TypeError("Audio cannot be casted to tensor. Cast it manually")

        window_size_samples = len(x[0]) if x.dim() == 2 else len(x)
        self.current_sample += window_size_samples

        speech_prob = self.model(x, self.sampling_rate).item()

        if (speech_prob >= self.threshold) and self.temp_end:
            self.temp_end = 0

        if (speech_prob >= self.threshold) and not self.triggered:
            self.triggered = True
            return None

        if (speech_prob < self.threshold - 0.15) and self.triggered:
            if not self.temp_end:
                self.temp_end = self.current_sample
            if self.current_sample - self.temp_end < self.min_silence_samples:
                return None
            else:
                # end of speak
                self.temp_end = 0
                self.triggered = False
                spoken_utterance = self.buffer 
                self.buffer = []
                return spoken_utterance
            
        if self.triggered:
            self.buffer.append(x)
                
        return None