chore: cleanup

Author: rachwalk

src/rai/rai/agents/voice_agent.py

@@ -197,35 +197,3 @@ def transcription_thread(self, identifier: str):
         )
         self.transcription_threads[identifier]["transcription"] = transcription
         self.transcription_threads[identifier]["event"].set()
-
-        # with self.transcription_lock:
-        # while self.active_thread == identifier:
-        #     with self.sample_buffer_lock:
-        #         if len(self.sample_buffer) == 0:
-        #             continue
-        #         audio_data = self.sample_buffer.copy()
-        #         self.sample_buffer = []
-        #     audio_data = np.concatenate(audio_data)
-        #     with self.transcription_lock:
-        #         self.transcription_model.transcribe(audio_data)
-
-        # # transciption of the reminder of the buffer
-        # with self.sample_buffer_lock:
-        #     if identifier in self.transcription_buffers:
-        #         audio_data = self.transcription_buffers[identifier]
-        #         audio_data = np.concatenate(audio_data)
-        #         with self.transcription_lock:
-        #             self.transcription_model.transcribe(audio_data)
-        #         del self.transcription_buffers[identifier]
-        # # self.transcription_model.save_wav(f"{identifier}.wav")
-        # with self.transcription_lock:
-        #     transcription = self.transcription_model.consume_transcription()
-        # self.logger.info(f"Transcription: {transcription}")
-        # self.connectors["ros2"].send_message(
-        #     ROS2ARIMessage(
-        #         {"data": transcription}, {"msg_type": "std_msgs/msg/String"}
-        #     ),
-        #     "/from_human",
-        # )
-        # self.transcription_threads[identifier]["transcription"] = transcription
-        # self.transcription_threads[identifier]["event"].set()

src/rai_asr/rai_asr/models/local_whisper.py

@@ -32,25 +32,6 @@ def __init__(self, model_name: str, sample_rate: int, language: str = "en"):
             self.whisper = whisper.load_model(self.model_name)
 
         self.logger = logging.getLogger(__name__)
-        # TODO: remove sample storage before PR is merged, this is just to enable saving wav files for debugging
-        # self.samples = None
-
-    # def save_wav(self, output_filename: str):
-    #     assert self.samples is not None, "No samples to save"
-    #     combined_samples = self.samples
-    #     if combined_samples.dtype.kind == "f":
-    #         combined_samples = np.clip(combined_samples, -1.0, 1.0)
-    #         combined_samples = (combined_samples * 32767).astype(np.int16)
-    #     elif combined_samples.dtype != np.int16:
-    #         combined_samples = combined_samples.astype(np.int16)
-
-    #     with wave.open(output_filename, "wb") as wav_file:
-    #         n_channels = 1
-    #         sampwidth = 2
-    #         wav_file.setnchannels(n_channels)
-    #         wav_file.setsampwidth(sampwidth)
-    #         wav_file.setframerate(self.sample_rate)
-    #         wav_file.writeframes(combined_samples.tobytes())
 
     def transcribe(self, data: NDArray[np.int16]) -> str:
         normalized_data = data.astype(np.float32) / 32768.0