Fix linting errors

voicebot/bots/whisper.py

@@ -10,7 +10,9 @@ import time
 import threading
 from collections import deque
 from queue import Queue, Empty
-from typing import Dict, Optional, Callable, Awaitable, Deque, Any
+from typing import Dict, Optional, Callable, Awaitable, Deque, Any, cast
+import numpy.typing as npt
+from pydantic import BaseModel
 
 # Core dependencies
 import librosa
@@ -28,6 +30,23 @@ from shared.models import ChatMessageModel
 
 from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
 
+# Type definitions
+AudioArray = npt.NDArray[np.float32]
+
+class AudioQueueItem(BaseModel):
+    """Audio data with timestamp for processing queue."""
+    audio: AudioArray
+    timestamp: float
+    
+    class Config:
+        arbitrary_types_allowed = True
+
+class TranscriptionHistoryItem(BaseModel):
+    """Transcription history item with metadata."""
+    message: str
+    timestamp: float
+    is_final: bool
+
 AGENT_NAME = "whisper"
 AGENT_DESCRIPTION = "Real-time speech transcription (Whisper) - converts speech to text"
 sample_rate = 16000  # Whisper expects 16kHz
@@ -80,23 +99,23 @@ class AudioProcessor:
         self.samples_per_frame = 480  # Common WebRTC frame size at 16kHz (30ms)
         
         # Audio buffering
-        self.audio_buffer: Deque[np.ndarray] = deque(maxlen=1000)  # ~30 seconds at 30ms frames
+        self.audio_buffer: Deque[AudioArray] = deque(maxlen=1000)  # ~30 seconds at 30ms frames
         self.phrase_timeout = 3.0  # seconds of silence before considering phrase complete
         self.last_activity_time = time.time()
         
         # Transcription state
-        self.current_phrase_audio = np.array([], dtype=np.float32)
-        self.transcription_history = []
+        self.current_phrase_audio: AudioArray = np.array([], dtype=np.float32)
+        self.transcription_history: list[TranscriptionHistoryItem] = []
         
         # Background processing
-        self.processing_queue = Queue()
+        self.processing_queue: Queue[AudioQueueItem] = Queue()
         self.is_running = True
         self.processor_thread = threading.Thread(target=self._processing_loop, daemon=True)
         self.processor_thread.start()
         
         logger.info("AudioProcessor initialized for real-time transcription")
     
-    def add_audio_data(self, audio_data: np.ndarray):
+    def add_audio_data(self, audio_data: AudioArray):
         """Add new audio data to the processing buffer."""
         if not self.is_running:
             return
@@ -121,10 +140,11 @@ class AudioProcessor:
         
         # Add to processing queue
         try:
-            self.processing_queue.put_nowait({
-                'audio': combined_audio,
-                'timestamp': time.time()
-            })
+            queue_item = AudioQueueItem(
+                audio=combined_audio,
+                timestamp=time.time()
+            )
+            self.processing_queue.put_nowait(queue_item)
         except Exception:
             # Queue full, skip this chunk
             logger.debug("Audio processing queue full, dropping audio chunk")
@@ -141,8 +161,8 @@ class AudioProcessor:
                 except Empty:
                     continue
                 
-                audio_array = audio_data['audio']
-                chunk_timestamp = audio_data['timestamp']
+                audio_array = audio_data.audio
+                chunk_timestamp = audio_data.timestamp
                 
                 # Check if this is a new phrase (gap in audio)
                 time_since_last = chunk_timestamp - self.last_activity_time
@@ -179,7 +199,7 @@ class AudioProcessor:
             except Exception as e:
                 logger.error(f"Error in audio processing loop: {e}", exc_info=True)
     
-    async def _transcribe_and_send(self, audio_array: np.ndarray, is_final: bool):
+    async def _transcribe_and_send(self, audio_array: AudioArray, is_final: bool):
         """Transcribe audio and send result as chat message."""
         global sample_rate
         
@@ -203,15 +223,16 @@ class AudioProcessor:
                 message = f"{prefix}{text}"
                 
                 # Avoid sending duplicate messages
-                if is_final or message not in [h.get('message', '') for h in self.transcription_history[-3:]]:
+                if is_final or message not in [h.message for h in self.transcription_history[-3:]]:
                     await self.send_chat_func(message)
                     
                     # Keep history for deduplication
-                    self.transcription_history.append({
-                        'message': message,
-                        'timestamp': time.time(),
-                        'is_final': is_final
-                    })
+                    history_item = TranscriptionHistoryItem(
+                        message=message,
+                        timestamp=time.time(),
+                        is_final=is_final
+                    )
+                    self.transcription_history.append(history_item)
                     
                     # Limit history size
                     if len(self.transcription_history) > 10:
@@ -265,9 +286,12 @@ async def handle_track_received(peer: Peer, track: MediaStreamTrack):
                         target_sr=sample_rate
                     )
                 
+                # Ensure audio_data is AudioArray (float32) 
+                audio_data_float32 = cast(AudioArray, audio_data.astype(np.float32))
+                
                 # Send to audio processor
                 if _audio_processor:
-                    _audio_processor.add_audio_data(audio_data)
+                    _audio_processor.add_audio_data(audio_data_float32)
                 
     except Exception as e:
         logger.error(f"Error processing audio track from {peer.peer_name}: {e}", exc_info=True)