Create stt_handler.pyc

stt_handler.pyc

@@ -0,0 +1,35 @@
+# rentbot/stt_handler.py
+import whisper
+import numpy as np
+import asyncio
+import os
+from io import BytesIO
+
+# Load the model once when the module is imported
+print("Loading Whisper model...")
+model = whisper.load_model("base.en")
+print("Whisper model loaded.")
+
+async def transcribe_audio_chunk(audio_chunk: np.ndarray) -> str:
+    """
+    Transcribes an audio chunk using Whisper.
+    Runs the blocking whisper call in a separate thread.
+    """
+    # The audio data is 16-bit PCM, 8000 Hz. Whisper expects float32.
+    audio_float32 = audio_chunk.astype(np.float32) / 32768.0
+
+    # Using an in-memory buffer
+    wav_buffer = BytesIO()
+    
+    # We must provide the sample rate to whisper's transcribe function
+    loop = asyncio.get_event_loop()
+    result = await loop.run_in_executor(
+        None,  # Use the default executor (a ThreadPoolExecutor)
+        lambda: model.transcribe(
+            audio_float32, 
+            language="en", 
+            fp16=False # Set to False if not using a GPU
+        )
+    )
+    
+    return result.get("text", "").strip()