diff --git a/examples/microphone_and_webRTC_integration/README.md b/examples/microphone_and_webRTC_integration/README.md index 1d75cd7..98982cc 100644 --- a/examples/microphone_and_webRTC_integration/README.md +++ b/examples/microphone_and_webRTC_integration/README.md @@ -1,6 +1,28 @@ -In this example, an integration with the microphone and the webRTC VAD has been done. I used [this](https://github.com/mozilla/DeepSpeech-examples/tree/r0.8/mic_vad_streaming) as a draft. -Here short video to present the results: +In this example, an integration with the microphone and the webRTC VAD has been done. I used [this](https://github.com/mozilla/DeepSpeech-examples/tree/r0.8/mic_vad_streaming) as a draft. +Here a short video to present the results: https://user-images.githubusercontent.com/28188499/116685087-182ff100-a9b2-11eb-927d-ed9f621226ee.mp4 +# Requirements: +The libraries used for the following example are: +``` +Python == 3.6.9 +webrtcvad >= 2.0.10 +torchaudio >= 0.8.1 +torch >= 1.8.1 +halo >= 0.0.31 +Soundfile >= 0.13.3 +``` +Using pip3: +``` +pip3 install webrtcvad +pip3 install torchaudio +pip3 install torch +pip3 install halo +pip3 install soundfile +``` +Moreover, to make the code easier, the default sample_rate is 16KHz without resampling. + +This example has been tested on ``` ubuntu 18.04.3 LTS``` + diff --git a/examples/microphone_and_webRTC_integration/microphone_and_webRTC_integration.py b/examples/microphone_and_webRTC_integration/microphone_and_webRTC_integration.py index 1a6d155..2474657 100644 --- a/examples/microphone_and_webRTC_integration/microphone_and_webRTC_integration.py +++ b/examples/microphone_and_webRTC_integration/microphone_and_webRTC_integration.py @@ -1,17 +1,11 @@ -import time, logging -from datetime import datetime -import threading, collections, queue, os, os.path +import collections, queue 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) - class Audio(object): """Streams raw audio from microphone. Data is received in a separate thread, and stored in a buffer, to be read from.""" @@ -21,11 +15,9 @@ class Audio(object): CHANNELS = 1 BLOCKS_PER_SECOND = 50 - def __init__(self, callback=None, device=None, input_rate=RATE_PROCESS, file=None): + def __init__(self, callback=None, device=None, input_rate=RATE_PROCESS): def proxy_callback(in_data, frame_count, time_info, status): #pylint: disable=unused-argument - if self.chunk is not None: - in_data = self.wf.readframes(self.chunk) callback(in_data) return (None, pyaudio.paContinue) if callback is None: callback = lambda in_data: self.buffer_queue.put(in_data) @@ -50,34 +42,10 @@ class Audio(object): # if not default device if self.device: kwargs['input_device_index'] = self.device - elif file is not None: - self.chunk = 320 - self.wf = wave.open(file, 'rb') self.stream = self.pa.open(**kwargs) self.stream.start_stream() - def resample(self, data, input_rate): - """ - Microphone may not support our native processing sampling rate, so - resample from input_rate to RATE_PROCESS here for webrtcvad and - deepspeech - - Args: - data (binary): Input audio stream - input_rate (int): Input audio rate to resample from - """ - data16 = np.fromstring(string=data, dtype=np.int16) - resample_size = int(len(data16) / self.input_rate * self.RATE_PROCESS) - resample = signal.resample(data16, resample_size) - resample16 = np.array(resample, dtype=np.int16) - return resample16.tostring() - - def read_resampled(self): - """Return a block of audio data resampled to 16000hz, blocking if necessary.""" - return self.resample(data=self.buffer_queue.get(), - input_rate=self.input_rate) - def read(self): """Return a block of audio data, blocking if necessary.""" return self.buffer_queue.get() @@ -89,23 +57,12 @@ class Audio(object): frame_duration_ms = property(lambda self: 1000 * self.block_size // self.sample_rate) - def write_wav(self, filename, data): - logging.info("write wav %s", filename) - wf = wave.open(filename, 'wb') - wf.setnchannels(self.CHANNELS) - # wf.setsampwidth(self.pa.get_sample_size(FORMAT)) - assert self.FORMAT == pyaudio.paInt16 - wf.setsampwidth(2) - wf.setframerate(self.sample_rate) - wf.writeframes(data) - wf.close() - class VADAudio(Audio): """Filter & segment audio with voice activity detection.""" - def __init__(self, aggressiveness=3, device=None, input_rate=None, file=None): - super().__init__(device=device, input_rate=input_rate, file=file) + def __init__(self, aggressiveness=3, device=None, input_rate=None): + super().__init__(device=device, input_rate=input_rate) self.vad = webrtcvad.Vad(aggressiveness) def frame_generator(self): @@ -114,8 +71,7 @@ class VADAudio(Audio): while True: yield self.read() else: - while True: - yield self.read_resampled() + raise Exception("Resampling required") def vad_collector(self, padding_ms=300, ratio=0.75, frames=None): """Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None. @@ -153,24 +109,20 @@ class VADAudio(Audio): ring_buffer.clear() def main(ARGS): - - - - # Start audio with VAD - vad_audio = VADAudio(aggressiveness=ARGS.vad_aggressiveness, + vad_audio = VADAudio(aggressiveness=ARGS.webRTC_aggressiveness, device=ARGS.device, - input_rate=ARGS.rate, - file=ARGS.file) + input_rate=ARGS.rate) + 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 + model=ARGS.silaro_model_name, + force_reload= ARGS.reload) + (get_speech_ts,_,_, _,_, _, _) = utils # Stream from microphone to DeepSpeech using VAD @@ -182,7 +134,6 @@ def main(ARGS): if frame is not None: if spinner: spinner.start() - logging.debug("streaming frame") wav_data.extend(frame) else: if spinner: spinner.stop() @@ -190,11 +141,12 @@ def main(ARGS): newsound= np.frombuffer(wav_data,np.int16) audio_float32=Int2Float(newsound) - time_stamps =get_speech_ts(audio_float32, model,num_steps=4) + time_stamps =get_speech_ts(audio_float32, model,num_steps=ARGS.num_steps,trig_sum=ARGS.trig_sum,neg_trig_sum=ARGS.neg_trig_sum, + num_samples_per_window=ARGS.num_samples_per_window,min_speech_samples=ARGS.min_speech_samples, + min_silence_samples=ARGS.min_silence_samples) + if(len(time_stamps)>0): print("silero VAD 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) else: print("silero VAD has detected a noise") print() @@ -216,18 +168,34 @@ if __name__ == '__main__': import argparse 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") + parser.add_argument('-v', '--webRTC_aggressiveness', type=int, default=3, + help="Set aggressiveness of webRTC: an integer between 0 and 3, 0 being the least aggressive about filtering out non-speech, 3 the most aggressive. Default: 3") parser.add_argument('--nospinner', action='store_true', help="Disable spinner") - parser.add_argument('-w', '--savewav', - 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('-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('-name', '--silaro_model_name', type=str, default="silero_vad", + help="select the name of the model. You can select between 'silero_vad',''silero_vad_micro','silero_vad_micro_8k','silero_vad_mini','silero_vad_mini_8k'") + parser.add_argument('--reload', action='store_true',help="download the last version of the silero vad") + + parser.add_argument('-ts', '--trig_sum', type=float, default=0.25, + help="overlapping windows are used for each audio chunk, trig sum defines average probability among those windows for switching into triggered state (speech state)") + + parser.add_argument('-nts', '--neg_trig_sum', type=float, default=0.07, + help="same as trig_sum, but for switching from triggered to non-triggered state (non-speech)") + + parser.add_argument('-N', '--num_steps', type=int, default=8, + help="nubmer of overlapping windows to split audio chunk into (we recommend 4 or 8)") + + parser.add_argument('-nspw', '--num_samples_per_window', type=int, default=4000, + help="number of samples in each window, our models were trained using 4000 samples (250 ms) per window, so this is preferable value (lesser values reduce quality)") + + parser.add_argument('-msps', '--min_speech_samples', type=int, default=10000, + help="minimum speech chunk duration in samples") + + parser.add_argument('-msis', '--min_silence_samples', type=int, default=500, + help=" minimum silence duration in samples between to separate speech chunks") ARGS = parser.parse_args() - if ARGS.savewav: os.makedirs(ARGS.savewav, exist_ok=True) + ARGS.rate=DEFAULT_SAMPLE_RATE main(ARGS) \ No newline at end of file