import torch # This is copied from silero-vad's vad_utils.py: # https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/utils_vad.py#L340 # Their licence is MIT, same as ours: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE 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 ): """ Class for stream imitation Parameters ---------- model: preloaded .jit 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 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 def __call__(self, x, return_seconds=False): """ 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 speech_start = self.current_sample - self.speech_pad_samples return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, 1)} 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: speech_end = self.temp_end + self.speech_pad_samples self.temp_end = 0 self.triggered = False return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, 1)} return None