Integration with silero vad added

examples/microphone_and_webRTC_integration/microphone_and_webRTC_integration.py

@@ -1,13 +1,14 @@
 import time, logging
 from datetime import datetime
 import threading, collections, queue, os, os.path
-import deepspeech
 import numpy as np
 import pyaudio
 import wave
 import webrtcvad
 from halo import Halo
 from scipy import signal
+import torch
+import torchaudio
 
 logging.basicConfig(level=20)
 
@@ -152,18 +153,9 @@ class VADAudio(Audio):
                     ring_buffer.clear()
 
 def main(ARGS):
-    # Load DeepSpeech model
-    if os.path.isdir(ARGS.model):
-        model_dir = ARGS.model
-        ARGS.model = os.path.join(model_dir, 'output_graph.pb')
-        ARGS.scorer = os.path.join(model_dir, ARGS.scorer)
 
-    print('Initializing model...')
+
-    logging.info("ARGS.model: %s", ARGS.model)
+
-    model = deepspeech.Model(ARGS.model)
-    if ARGS.scorer:
-        logging.info("ARGS.scorer: %s", ARGS.scorer)
-        model.enableExternalScorer(ARGS.scorer)
 
     # Start audio with VAD
     vad_audio = VADAudio(aggressiveness=ARGS.vad_aggressiveness,
@@ -173,36 +165,56 @@ def main(ARGS):
     print("Listening (ctrl-C to exit)...")
     frames = vad_audio.vad_collector()
 
+    # load silero VAD
+    torchaudio.set_audio_backend("soundfile")
+    model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
+                                    model='silero_vad',
+                                    force_reload=True)
+    (get_speech_ts,get_speech_ts_adaptive,_, read_audio,_, _, _) = utils
+
+
     # Stream from microphone to DeepSpeech using VAD
     spinner = None
     if not ARGS.nospinner:
         spinner = Halo(spinner='line')
-    stream_context = model.createStream()
     wav_data = bytearray()
     for frame in frames:
         if frame is not None:
             if spinner: spinner.start()
+
             logging.debug("streaming frame")
-            stream_context.feedAudioContent(np.frombuffer(frame, np.int16))
+            wav_data.extend(frame)
-            if ARGS.savewav: wav_data.extend(frame)
         else:
             if spinner: spinner.stop()
-            logging.debug("end utterence")
+            print("webRTC has detected a possible speech")
-            if ARGS.savewav:
+
-                vad_audio.write_wav(os.path.join(ARGS.savewav, datetime.now().strftime("savewav_%Y-%m-%d_%H-%M-%S_%f.wav")), wav_data)
+            newsound= np.frombuffer(wav_data,np.int16)
-                wav_data = bytearray()
+            audio_float32=Int2Float(newsound)
-            text = stream_context.finishStream()
+            time_stamps =get_speech_ts(audio_float32, model,num_steps=4)
-            print("Recognized: %s" % text)
+            if(len(time_stamps)>0):
-            if ARGS.keyboard:
+                print("silero VAD has detected a possible speech")
-                from pyautogui import typewrite
+                if ARGS.savewav:
-                typewrite(text)
+                    vad_audio.write_wav(os.path.join(ARGS.savewav, datetime.now().strftime("savewav_%Y-%m-%d_%H-%M-%S_%f.wav")), wav_data)
-            stream_context = model.createStream()
+            else:
+                print("silero VAD has detected a noise")
+            print()
+            wav_data = bytearray()
+
+
+def Int2Float(sound):
+    _sound = np.copy(sound)  #
+    abs_max = np.abs(_sound).max()
+    _sound = _sound.astype('float32')
+    if abs_max > 0:
+        _sound *= 1/abs_max
+    audio_float32 = torch.from_numpy(_sound.squeeze())
+    return audio_float32
 
 if __name__ == '__main__':
     DEFAULT_SAMPLE_RATE = 16000
 
     import argparse
-    parser = argparse.ArgumentParser(description="Stream from microphone to DeepSpeech using VAD")
+    parser = argparse.ArgumentParser(description="Stream from microphone to webRTC and silero VAD")
 
     parser.add_argument('-v', '--vad_aggressiveness', type=int, default=3,
                         help="Set aggressiveness of VAD: an integer between 0 and 3, 0 being the least aggressive about filtering out non-speech, 3 the most aggressive. Default: 3")
@@ -212,17 +224,10 @@ if __name__ == '__main__':
                         help="Save .wav files of utterences to given directory")
     parser.add_argument('-f', '--file',
                         help="Read from .wav file instead of microphone")
-
-    parser.add_argument('-m', '--model', required=True,
-                        help="Path to the model (protocol buffer binary file, or entire directory containing all standard-named files for model)")
-    parser.add_argument('-s', '--scorer',
-                        help="Path to the external scorer file.")
     parser.add_argument('-d', '--device', type=int, default=None,
                         help="Device input index (Int) as listed by pyaudio.PyAudio.get_device_info_by_index(). If not provided, falls back to PyAudio.get_default_device().")
     parser.add_argument('-r', '--rate', type=int, default=DEFAULT_SAMPLE_RATE,
                         help=f"Input device sample rate. Default: {DEFAULT_SAMPLE_RATE}. Your device may require 44100.")
-    parser.add_argument('-k', '--keyboard', action='store_true',
-                        help="Type output through system keyboard")
     ARGS = parser.parse_args()
     if ARGS.savewav: os.makedirs(ARGS.savewav, exist_ok=True)
     main(ARGS)