feat: functional multithreading transcription

src/rai/rai/agents/voice_agent.py

@@ -13,9 +13,10 @@
 # limitations under the License.
 
 
+import logging
 import time
 from threading import Event, Lock, Thread
-from typing import Any, List, TypedDict
+from typing import Any, List, Optional, TypedDict
 from uuid import uuid4
 
 import numpy as np
@@ -40,7 +41,12 @@ def __init__(
         transcription_model: BaseTranscriptionModel,
         vad: BaseVoiceDetectionModel,
         grace_period: float = 1.0,
+        logger: Optional[logging.Logger] = None,
     ):
+        if logger is None:
+            self.logger = logging.getLogger(__name__)
+        else:
+            self.logger = logger
         microphone = StreamingAudioInputDevice()
         microphone.configure_device(
             target=str(microphone_device_id), config=microphone_config
@@ -87,16 +93,20 @@ def run(self):
         )
 
     def stop(self):
+        self.logger.info("Stopping voice agent")
         self.running = False
         self.connectors["microphone"].terminate_action(self.listener_handle)
-        to_finish = list(self.transcription_threads.keys())
-        while len(to_finish) > 0:
+        to_finish = len(list(self.transcription_threads.keys()))
+        while to_finish > 0:
             for thread_id in self.transcription_threads:
                 if self.transcription_threads[thread_id]["event"].is_set():
                     self.transcription_threads[thread_id]["thread"].join()
-                    to_finish.remove(thread_id)
+                    to_finish -= 1
                 else:
-                    print(f"Waiting for transcription of {thread_id} to finish")
+                    self.logger.info(
+                        f"Waiting for transcription of {thread_id} to finish..."
+                    )
+        self.logger.info("Voice agent stopped")
 
     def on_new_sample(self, indata: np.ndarray, status_flags: dict[str, Any]):
         sample_time = time.time()
@@ -112,7 +122,7 @@ def on_new_sample(self, indata: np.ndarray, status_flags: dict[str, Any]):
             should_record = self.should_record(indata, output_parameters)
 
         if should_record:
-            print("Start recording")
+            self.logger.info("starting recording...")
             self.recording_started = True
             thread_id = str(uuid4())[0:8]
             transcription_thread = Thread(
@@ -129,13 +139,14 @@ def on_new_sample(self, indata: np.ndarray, status_flags: dict[str, Any]):
             }
 
         if voice_detected:
+            self.logger.debug("Voice detected... resetting grace period")
             self.grace_period_start = sample_time
 
         if (
             self.recording_started
             and sample_time - self.grace_period_start > self.grace_period
         ):
-            print("Stop recording")
+            self.logger.info("Grace period ended... stopping recording")
             self.recording_started = False
             self.grace_period_start = 0
             with self.sample_buffer_lock:
@@ -148,12 +159,12 @@ def should_record(
     ) -> bool:
         for model in self.should_record_pipeline:
             detected, output = model.detected(audio_data, input_parameters)
-            print(f"Detected: {detected}: {output}")
             if detected:
                 return True
         return False
 
     def transcription_thread(self, identifier: str):
+        self.logger.info(f"transcription thread {identifier} started")
         with self.transcription_lock:
             while self.active_thread == identifier:
                 with self.sample_buffer_lock:
@@ -171,7 +182,10 @@ def transcription_thread(self, identifier: str):
                     audio_data = np.concatenate(audio_data)
                     self.transcription_model.transcribe(audio_data)
                     del self.buffer_reminders[identifier]
+            # self.transcription_model.save_wav(f"{identifier}.wav")
             transcription = self.transcription_model.consume_transcription()
             self.transcription_threads[identifier]["transcription"] = transcription
             self.transcription_threads[identifier]["event"].set()
-        # TODO: sending the transcription
+        # TODO: sending the transcription once https://github.com/RobotecAI/rai/pull/360 is merged
+        self.logger.info(f"transcription thread {identifier} finished")
+        print(transcription)

src/rai/rai/communication/sound_device_connector.py

@@ -30,7 +30,6 @@ def __init__(self, msg: str):
 class AudioInputDeviceConfig(TypedDict):
     block_size: int
     consumer_sampling_rate: int
-    target_sampling_rate: int
     dtype: str
     device_number: Optional[int]
 
@@ -44,7 +43,6 @@ class ConfiguredAudioInputDevice:
     sample_rate (int): Device sample rate
     consumer_sampling_rate (int): The sampling rate of the consumer
     window_size_samples (int): The size of the window in samples
-    target_sampling_rate (int): The target sampling rate
     dtype (str): The data type of the audio samples
     """
 
@@ -58,7 +56,6 @@ def __init__(self, config: AudioInputDeviceConfig):
         self.window_size_samples = int(
             config["block_size"] * self.sample_rate / config["consumer_sampling_rate"]
         )
-        self.target_sampling_rate = int(config["target_sampling_rate"])
         self.dtype = config["dtype"]
 
 
@@ -108,9 +105,9 @@ def start_action(
 
         def callback(indata: np.ndarray, frames: int, _, status: CallbackFlags):
             indata = indata.flatten()
-            sample_time_length = len(indata) / target_device.target_sampling_rate
-            if target_device.sample_rate != target_device.target_sampling_rate:
-                indata = resample(indata, int(sample_time_length * target_device.target_sampling_rate))  # type: ignore
+            sample_time_length = len(indata) / target_device.sample_rate
+            if target_device.sample_rate != target_device.consumer_sampling_rate:
+                indata = resample(indata, int(sample_time_length * target_device.consumer_sampling_rate))  # type: ignore
             flag_dict = {
                 "input_overflow": status.input_overflow,
                 "input_underflow": status.input_underflow,

src/rai_asr/rai_asr/models/local_whisper.py

@@ -26,20 +26,43 @@ class LocalWhisper(BaseTranscriptionModel):
     def __init__(self, model_name: str, sample_rate: int, language: str = "en"):
         super().__init__(model_name, sample_rate, language)
         if torch.cuda.is_available():
-            print("Using CUDA")
             self.whisper = whisper.load_model(self.model_name, device="cuda")
         else:
             self.whisper = whisper.load_model(self.model_name)
 
-        self.samples = None
+        # TODO: remove sample storage before PR is merged, this is just to enable saving wav files for debugging
+        # self.samples = None
+
+    def consume_transcription(self) -> str:
+        ret = super().consume_transcription()
+        # self.samples = None
+        return ret
+
+    # 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]):
+        # self.samples = (
+        #     np.concatenate((self.samples, data)) if self.samples is not None else data
+        # )
         normalized_data = data.astype(np.float32) / 32768.0
-        print("Starting transcription")
         result = whisper.transcribe(
             self.whisper, normalized_data
         )  # TODO: handling of additional transcribe arguments (perhaps in model init)
-        print("Finished transcription")
         transcription = result["text"]
         transcription = cast(str, transcription)
         self.latest_transcription += transcription

tests/communication/test_sound_device_connector.py

@@ -31,7 +31,6 @@ def device_config():
     return {
         "block_size": 1024,
         "consumer_sampling_rate": 44100,
-        "target_sampling_rate": 16000,
         "dtype": "float32",
     }