VAC controller integrated

it works. Reproducing #39

voice_activity_controller.py

@@ -51,8 +51,12 @@ class VoiceActivityController:
         self.temp_end = 0
         self.current_sample = 0
 
+        self.last_silence_len= 0
+        self.speech_len = 0
+
     def apply_vad(self, audio):
-        x = int2float(audio)
+#        x = int2float(audio)
+        x = audio
         if not torch.is_tensor(x):
             try:
                 x = torch.Tensor(x)
@@ -79,38 +83,42 @@ class VoiceActivityController:
                 return np.array([], dtype=np.float16) if self.use_vad_result else audio, 0, window_size_samples
 
 
+    def detect_speech_iter(self, data, audio_in_int16 = False):
+#        audio_block = np.frombuffer(data, dtype=np.int16) if not audio_in_int16 else data
+        audio_block = data
+        wav = audio_block
+
+        print(wav, len(wav), type(wav), wav.dtype)
+        
+        is_final = False
+        voice_audio, speech_in_wav, last_silent_in_wav = self.apply_vad(wav)
+
+
+        if speech_in_wav > 0 :
+            self.last_silence_len= 0                
+            self.speech_len += speech_in_wav
+#            if self.activity_detected_callback is not None:
+#                self.activity_detected_callback()
+
+        self.last_silence_len +=  last_silent_in_wav
+        if self.last_silence_len>= self.final_silence_limit and self.speech_len >= self.final_speech_limit:
+
+            is_final = True
+            self.last_silence_len= 0
+            self.speech_len = 0                
+
+#        return voice_audio.tobytes(), is_final
+        return voice_audio, is_final
 
 
 
     def detect_user_speech(self, audio_stream, audio_in_int16 = False):
-        last_silence_len= 0
+        self.last_silence_len= 0
-        speech_len = 0
+        self.speech_len = 0
 
         for data in audio_stream:  # replace with your condition of choice
-            
+            yield self.detect_speech_iter(data, audio_in_int16)
-            
+           
-            audio_block = np.frombuffer(data, dtype=np.int16) if not audio_in_int16 else data
-            wav = audio_block
-            
-            is_final = False
-            voice_audio, speech_in_wav, last_silent_in_wav = self.apply_vad(wav)
-
-
-            if speech_in_wav > 0 :
-                last_silence_len= 0                
-                speech_len += speech_in_wav
-                if self.activity_detected_callback is not None:
-                    self.activity_detected_callback()
-
-            last_silence_len +=  last_silent_in_wav
-            if last_silence_len>= self.final_silence_limit and speech_len >= self.final_speech_limit:
-
-                is_final = True
-                last_silence_len= 0
-                speech_len = 0                
-
-            yield voice_audio.tobytes(), is_final
-
 
 
 

whisper_online_vac.py

@@ -0,0 +1,209 @@
+from whisper_online import *
+from voice_activity_controller import *
+import soundfile
+import io
+
+SAMPLING_RATE = 16000
+
+class VACOnlineASRProcessor(OnlineASRProcessor):
+
+    def __init__(self, *a, **kw):
+        self.online = OnlineASRProcessor(*a, **kw)
+        self.vac = VoiceActivityController(use_vad_result = True)
+
+        self.is_currently_final = False
+        self.logfile = self.online.logfile
+
+        #self.vac_buffer = io.BytesIO()
+        #self.vac_stream = self.vac.detect_user_speech(self.vac_buffer, audio_in_int16=False)
+
+        self.audio_log = open("audio_log.wav","wb")
+
+    def init(self):
+        self.online.init()
+        self.vac.reset_states()
+
+    def insert_audio_chunk(self, audio):
+        print(audio, len(audio), type(audio), audio.dtype)
+        r = self.vac.detect_speech_iter(audio,audio_in_int16=False)
+        raw_bytes, is_final = r
+        print("is_final",is_final)
+        print("raw_bytes", raw_bytes[:10], len(raw_bytes), type(raw_bytes))
+#        self.audio_log.write(raw_bytes)
+        #sf = soundfile.SoundFile(io.BytesIO(raw_bytes), channels=1,endian="LITTLE",samplerate=SAMPLING_RATE, subtype="PCM_16",format="RAW")
+        #audio, _ = librosa.load(sf,sr=SAMPLING_RATE)
+        audio = raw_bytes
+        print("po překonvertování", audio, len(audio), type(audio), audio.dtype)
+        self.is_currently_final = is_final
+        self.online.insert_audio_chunk(audio)
+#        self.audio_log.write(audio)
+        self.audio_log.flush()
+
+        print("inserted",file=self.logfile)
+
+    def process_iter(self):
+        if self.is_currently_final:
+            return self.finish()
+        else:
+            print(self.online.audio_buffer)
+            ret = self.online.process_iter()
+            print("tady",file=self.logfile)
+            return ret
+
+    def finish(self):
+        ret = self.online.finish()
+        self.online.init()
+        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.')
+    
+    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.min_chunk_size
+    if args.buffer_trimming == "sentence":
+        tokenizer = create_tokenizer(tgt_language)
+    else:
+        tokenizer = None
+    online = VACOnlineASRProcessor(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)