from whisper_online import * from voice_activity_controller import * import soundfile import io SAMPLING_RATE = 16000 class VACOnlineASRProcessor(OnlineASRProcessor): def __init__(self, online_chunk_size, *a, **kw): self.online_chunk_size = online_chunk_size self.online = OnlineASRProcessor(*a, **kw) self.vac = VoiceActivityController(use_vad_result = False) self.logfile = self.online.logfile self.init() def init(self): self.online.init() self.vac.reset_states() self.current_online_chunk_buffer_size = 0 self.is_currently_final = False def insert_audio_chunk(self, audio): r = self.vac.detect_speech_iter(audio,audio_in_int16=False) audio, is_final = r print(is_final) self.is_currently_final = is_final self.online.insert_audio_chunk(audio) self.current_online_chunk_buffer_size += len(audio) def process_iter(self): if self.is_currently_final: return self.finish() elif self.current_online_chunk_buffer_size > SAMPLING_RATE*self.online_chunk_size: self.current_online_chunk_buffer_size = 0 ret = self.online.process_iter() return ret else: print("no online update, only VAD", file=self.logfile) return (None, None, "") def finish(self): ret = self.online.finish() self.online.init(keep_offset=True) self.current_online_chunk_buffer_size = 0 return ret if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument('audio_path', type=str, help="Filename of 16kHz mono channel wav, on which live streaming is simulated.") add_shared_args(parser) parser.add_argument('--start_at', type=float, default=0.0, help='Start processing audio at this time.') parser.add_argument('--offline', action="store_true", default=False, help='Offline mode.') parser.add_argument('--comp_unaware', action="store_true", default=False, help='Computationally unaware simulation.') parser.add_argument('--vac-chunk-size', type=float, default=0.04, help='VAC sample size in seconds.') args = parser.parse_args() # reset to store stderr to different file stream, e.g. open(os.devnull,"w") logfile = sys.stderr if args.offline and args.comp_unaware: print("No or one option from --offline and --comp_unaware are available, not both. Exiting.",file=logfile) sys.exit(1) audio_path = args.audio_path SAMPLING_RATE = 16000 duration = len(load_audio(audio_path))/SAMPLING_RATE print("Audio duration is: %2.2f seconds" % duration, file=logfile) size = args.model language = args.lan t = time.time() print(f"Loading Whisper {size} model for {language}...",file=logfile,end=" ",flush=True) if args.backend == "faster-whisper": asr_cls = FasterWhisperASR else: asr_cls = WhisperTimestampedASR asr = asr_cls(modelsize=size, lan=language, cache_dir=args.model_cache_dir, model_dir=args.model_dir) if args.task == "translate": asr.set_translate_task() tgt_language = "en" # Whisper translates into English else: tgt_language = language # Whisper transcribes in this language e = time.time() print(f"done. It took {round(e-t,2)} seconds.",file=logfile) if args.vad: print("setting VAD filter",file=logfile) asr.use_vad() min_chunk = args.vac_chunk_size if args.buffer_trimming == "sentence": tokenizer = create_tokenizer(tgt_language) else: tokenizer = None online = VACOnlineASRProcessor(args.min_chunk_size, asr,tokenizer,logfile=logfile,buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec)) # load the audio into the LRU cache before we start the timer a = load_audio_chunk(audio_path,0,1) # warm up the ASR, because the very first transcribe takes much more time than the other asr.transcribe(a) beg = args.start_at start = time.time()-beg def output_transcript(o, now=None): # output format in stdout is like: # 4186.3606 0 1720 Takhle to je # - the first three words are: # - emission time from beginning of processing, in milliseconds # - beg and end timestamp of the text segment, as estimated by Whisper model. The timestamps are not accurate, but they're useful anyway # - the next words: segment transcript if now is None: now = time.time()-start if o[0] is not None: print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),file=logfile,flush=True) print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),flush=True) else: print(o,file=logfile,flush=True) if args.offline: ## offline mode processing (for testing/debugging) a = load_audio(audio_path) online.insert_audio_chunk(a) try: o = online.process_iter() except AssertionError: print("assertion error",file=logfile) pass else: output_transcript(o) now = None elif args.comp_unaware: # computational unaware mode end = beg + min_chunk while True: a = load_audio_chunk(audio_path,beg,end) online.insert_audio_chunk(a) try: o = online.process_iter() except AssertionError: print("assertion error",file=logfile) pass else: output_transcript(o, now=end) print(f"## last processed {end:.2f}s",file=logfile,flush=True) if end >= duration: break beg = end if end + min_chunk > duration: end = duration else: end += min_chunk now = duration else: # online = simultaneous mode end = 0 while True: now = time.time() - start if now < end+min_chunk: time.sleep(min_chunk+end-now) end = time.time() - start a = load_audio_chunk(audio_path,beg,end) beg = end online.insert_audio_chunk(a) try: o = online.process_iter() except AssertionError: print("assertion error",file=logfile) pass else: output_transcript(o) now = time.time() - start print(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}",file=logfile,flush=True) if end >= duration: break now = None o = online.finish() output_transcript(o, now=now)