fix(transcription): transcode uploads to WAV before STT (#957)

The /transcribe endpoint passed the raw uploaded file straight to the STT
backend (mlx_audio.stt -> miniaudio), which only decodes WAV/FLAC/MP3/Vorbis.
Browser recordings arrive as WebM/Opus (Chrome/Firefox MediaRecorder), so
web-mode dictation failed with 500 "unsupported file format". The Tauri app
was unaffected because WebKit produces MP4.

librosa already fully decodes the upload to compute duration (falling back to
audioread/ffmpeg for exotic containers), so re-encode that PCM to a temp WAV
and hand it to Whisper. WAV inputs pass through unchanged; the temp file is
cleaned up in the finally block.

Co-authored-by: Claude Opus 4.8 <[email protected]>

backend/routes/transcription.py

@@ -35,13 +35,25 @@ async def transcribe_audio(
             tmp.write(chunk)
         tmp_path = tmp.name
 
+    stt_path = tmp_path
     try:
-        from ..utils.audio import load_audio
+        from ..utils.audio import load_audio, save_audio
         from ..backends import WHISPER_HF_REPOS
 
         audio, sr = await asyncio.to_thread(load_audio, tmp_path)
         duration = len(audio) / sr
 
+        # The STT backend (mlx_audio.stt -> miniaudio) only decodes
+        # WAV/FLAC/MP3/Vorbis, so browser recordings uploaded as WebM/Opus
+        # fail with "unsupported file format" (issue: web-mode dictation).
+        # librosa already decoded the file above (it falls back to
+        # audioread/ffmpeg for exotic containers), so re-encode that PCM to a
+        # temp WAV and hand *that* to Whisper. WAV inputs pass through
+        # unchanged.
+        if file_suffix != ".wav":
+            stt_path = f"{tmp_path}.stt.wav"
+            await asyncio.to_thread(save_audio, audio, stt_path, sr)
+
         whisper_model = transcribe.get_whisper_model()
         model_size = model if model else whisper_model.model_size
 
@@ -76,7 +88,7 @@ async def transcribe_audio(
                 },
             )
 
-        text = await whisper_model.transcribe(tmp_path, language, model_size)
+        text = await whisper_model.transcribe(stt_path, language, model_size)
 
         return models.TranscriptionResponse(
             text=text,
@@ -89,3 +101,5 @@ async def transcribe_audio(
         raise HTTPException(status_code=500, detail=str(e))
     finally:
         Path(tmp_path).unlink(missing_ok=True)
+        if stt_path != tmp_path:
+            Path(stt_path).unlink(missing_ok=True)