from VAD.vad_iterator import VADIterator from baseHandler import BaseHandler import numpy as np import torch from rich.console import Console from utils.utils import int2float import logging logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) logger = logging.getLogger(__name__) console = Console() class VADHandler(BaseHandler): """ Handles voice activity detection. When voice activity is detected, audio will be accumulated until the end of speech is detected and then passed to the following part. """ def setup( self, should_listen, thresh=0.3, sample_rate=16000, min_silence_ms=1000, min_speech_ms=500, max_speech_ms=float("inf"), speech_pad_ms=30, ): self.should_listen = should_listen self.sample_rate = sample_rate self.min_silence_ms = min_silence_ms self.min_speech_ms = min_speech_ms self.max_speech_ms = max_speech_ms self.model, _ = torch.hub.load("snakers4/silero-vad", "silero_vad") self.iterator = VADIterator( self.model, threshold=thresh, sampling_rate=sample_rate, min_silence_duration_ms=min_silence_ms, speech_pad_ms=speech_pad_ms, ) def process(self, audio_chunk): audio_int16 = np.frombuffer(audio_chunk, dtype=np.int16) audio_float32 = int2float(audio_int16) vad_output = self.iterator(torch.from_numpy(audio_float32)) if vad_output is not None and len(vad_output) != 0: logger.debug("VAD: end of speech detected") array = torch.cat(vad_output).cpu().numpy() duration_ms = len(array) / self.sample_rate * 1000 if duration_ms < self.min_speech_ms or duration_ms > self.max_speech_ms: logger.debug( f"audio input of duration: {len(array) / self.sample_rate}s, skipping" ) else: self.should_listen.clear() logger.debug("Stop listening") yield array