Refactor audio conversion and cleanup in BotService and Transcriber classes

Author: Repulsiveprogress

src/audio_converter.py

@@ -97,16 +97,15 @@ def convert_to_wav(self, audio_bytes: bytes, mime: str) -> str | None:
         Caller must delete the file (e.g. via os.unlink in a finally block).
         Blocking — must be called via run_in_executor.
         """
-        from pydub import AudioSegment  # noqa: PLC0415
+        from pydub import AudioSegment
 
         fmt = _pydub_format(mime)
         try:
             seg = AudioSegment.from_file(io.BytesIO(audio_bytes), format=fmt)
             seg = seg.set_frame_rate(16000).set_channels(1).set_sample_width(2)
-            tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-            seg.export(tmp.name, format="wav")
-            tmp.close()
-            return tmp.name
+            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+                seg.export(tmp.name, format="wav")
+                return tmp.name
         except Exception:
             logger.exception("Audio conversion failed (mime=%s)", mime)
             return None

src/bot_service.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import asyncio
+import contextlib
 import logging
 import os
 import time
@@ -97,10 +98,8 @@ async def handle_audio_event(
                 wav_path,
             )
         finally:
-            try:
+            with contextlib.suppress(OSError):
                 os.unlink(wav_path)
-            except OSError:
-                pass
 
         if not transcript or not transcript.strip():
             await self._send_plain(room.room_id, self.strings.transcription_failed)  # type: ignore[attr-defined]

src/transcriber.py

@@ -17,12 +17,10 @@ def __init__(self, model: object) -> None:
         self._model = model
 
     @classmethod
-    def load(cls, model_name: str) -> "Transcriber":
+    def load(cls, model_name: str) -> Transcriber:
+        import nemo.collections.asr as nemo_asr
         import torch
 
-        # Local import so this module is importable without nemo installed (e.g. lint CI)
-        import nemo.collections.asr as nemo_asr  # noqa: PLC0415
-
         logger.info("Loading ASR model: %s", model_name)
         model = nemo_asr.models.ASRModel.from_pretrained(model_name, map_location="cpu")
         model = model.to(torch.device("cpu"))