e

client/src/main.py

@@ -31,83 +31,84 @@ def main(transcriptions_queue):
     energy_threshold = 300
 
     data_queue = Queue()
+    microphone = get_microphone(sample_rate=sample_rate)
+    print('microphone is:', microphone)
 
-    with get_microphone(sample_rate=sample_rate) as microphone:
-        print('microphone is:', microphone)
+    with microphone:
         speech_recognizer = get_speech_recognizer(energy_threshold=energy_threshold)
         speech_recognizer.adjust_for_ambient_noise(source=microphone)
 
-        def record_callback(_, audio: sr.AudioData) -> None:
-            data = audio.get_raw_data()
-            data_queue.put(data)
-
-        speech_recognizer.listen_in_background(source=microphone, callback=record_callback, phrase_time_limit=recording_duration)
-
-        print("\n🎤 Microphone is now listening...\n")
-
-        prev_audio_array = None
-        current_audio_chunk = AudioChunk(start_time=datetime.now(tz=UTC))
-
-        while True:
-            try:
-                now = datetime.now(tz=UTC)
-                # Pull raw recorded audio from the queue.
-                if not data_queue.empty():
-                    # Store end time if we're over the recording time limit.
-                    if now - current_audio_chunk.start_time > timedelta(seconds=recording_duration):
-                        current_audio_chunk.end_time = now
-
-                    # Get audio data from queue
-                    audio_data = get_all_audio_queue(data_queue)
-                    audio_np_array = to_audio_array(audio_data)
-
-                    if current_audio_chunk.is_complete:
-                        print('start serialize')
-                        if prev_audio_array is not None:
-                            serialized = pickle.dumps(
-                                np.concatenate((
-                                    prev_audio_array,
-                                    current_audio_chunk.audio_array
-                                ))
-                            )
-                        else:
-                            serialized = pickle.dumps(current_audio_chunk.audio_array)
-                        prev_audio_array = current_audio_chunk.audio_array
-                        print('end serialize')
-
-                        start = time.time()
-                        print('start req')
-                        response = httpx.post(TRANSCRIBING_SERVER, data=serialized)
-                        transcription = response.json()['transcribe']
-                        print('req done', response.text, response.status_code, time.time() - start)
-                        transcriptions_queue.put(transcription)
-
-                        # text = transcribe_model.transcribe(current_audio_chunk.audio_array)
-                        # sentence = Sentence(
-                        #     start_time=current_audio_chunk.start_time, end_time=current_audio_chunk.end_time, text=text
-                        # )
-                        current_audio_chunk = AudioChunk(
-                            audio_array=audio_np_array, start_time=datetime.now(tz=UTC)
+    def record_callback(_, audio: sr.AudioData) -> None:
+        data = audio.get_raw_data()
+        data_queue.put(data)
+
+    speech_recognizer.listen_in_background(source=microphone, callback=record_callback, phrase_time_limit=recording_duration)
+
+    print("\n🎤 Microphone is now listening...\n")
+
+    prev_audio_array = None
+    current_audio_chunk = AudioChunk(start_time=datetime.now(tz=UTC))
+
+    while True:
+        try:
+            now = datetime.now(tz=UTC)
+            # Pull raw recorded audio from the queue.
+            if not data_queue.empty():
+                # Store end time if we're over the recording time limit.
+                if now - current_audio_chunk.start_time > timedelta(seconds=recording_duration):
+                    current_audio_chunk.end_time = now
+
+                # Get audio data from queue
+                audio_data = get_all_audio_queue(data_queue)
+                audio_np_array = to_audio_array(audio_data)
+
+                if current_audio_chunk.is_complete:
+                    print('start serialize')
+                    if prev_audio_array is not None:
+                        serialized = pickle.dumps(
+                            np.concatenate((
+                                prev_audio_array,
+                                current_audio_chunk.audio_array
+                            ))
                         )
-                        # print(sentence.text)  # noqa: T201
                     else:
-                        current_audio_chunk.update_array(audio_np_array)
+                        serialized = pickle.dumps(current_audio_chunk.audio_array)
+                    prev_audio_array = current_audio_chunk.audio_array
+                    print('end serialize')
 
-                    # Flush stdout
-                    print("", end="", flush=True)  # noqa: T201
+                    start = time.time()
+                    print('start req')
+                    response = httpx.post(TRANSCRIBING_SERVER, data=serialized)
+                    transcription = response.json()['transcribe']
+                    print('req done', response.text, response.status_code, time.time() - start)
+                    transcriptions_queue.put(transcription)
 
-                    # Infinite loops are bad for processors, must sleep.
-                    sleep(0.25)
-            except KeyboardInterrupt:
-                current_audio_chunk.end_time = datetime.now(tz=UTC)
-                if current_audio_chunk.is_complete:
-                    logger.warning("⚠️ Transcribing last chunk...")
                     # text = transcribe_model.transcribe(current_audio_chunk.audio_array)
                     # sentence = Sentence(
                     #     start_time=current_audio_chunk.start_time, end_time=current_audio_chunk.end_time, text=text
                     # )
+                    current_audio_chunk = AudioChunk(
+                        audio_array=audio_np_array, start_time=datetime.now(tz=UTC)
+                    )
                     # print(sentence.text)  # noqa: T201
-                break
+                else:
+                    current_audio_chunk.update_array(audio_np_array)
+
+                # Flush stdout
+                print("", end="", flush=True)  # noqa: T201
+
+                # Infinite loops are bad for processors, must sleep.
+                sleep(0.25)
+        except KeyboardInterrupt:
+            current_audio_chunk.end_time = datetime.now(tz=UTC)
+            if current_audio_chunk.is_complete:
+                logger.warning("⚠️ Transcribing last chunk...")
+                # text = transcribe_model.transcribe(current_audio_chunk.audio_array)
+                # sentence = Sentence(
+                #     start_time=current_audio_chunk.start_time, end_time=current_audio_chunk.end_time, text=text
+                # )
+                # print(sentence.text)  # noqa: T201
+            break
 
 
     # for i in range(minimum, maximum + 1):