Fix broken file transcription and speaker diarization

qwen3 backend: model list pointed at nonexistent HF repos (Qwen3-ASR-2B/8B
don't exist), and inference went through the generic HF ASR pipeline with
Whisper-only options (return_timestamps, task/language generate_kwargs) that
Qwen3-ASR's chat-style architecture doesn't support. Switched to the real
0.6B/1.7B-hf checkpoints and drive them via
processor.apply_transcription_request() + model.generate(). Also added an
ffmpeg pre-conversion step since the model's feature extractor can't decode
m4a via librosa.

faster-whisper backend: speaker diarization was broken by two pyannote.audio
API changes it hadn't caught up with (use_auth_token= renamed to token=, and
pipeline() now returns a DiarizeOutput wrapper instead of an Annotation
directly). Also pinned LD_LIBRARY_PATH for that worker so it picks up its own
venv's cuDNN instead of the older one shadowing it via the container's global
LD_LIBRARY_PATH, which crashed pyannote's GPU init.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

app/supervisord.conf

@@ -6,6 +6,7 @@ pidfile=/tmp/supervisord.pid
 [program:faster_whisper]
 command=/opt/venvs/faster_whisper/bin/python /app/asr/workers/faster_whisper_worker.py --host 0.0.0.0 --port 8001
 directory=/app
+environment=LD_LIBRARY_PATH="/opt/venvs/faster_whisper/lib/python3.11/site-packages/nvidia/cudnn/lib:%(ENV_LD_LIBRARY_PATH)s"
 autostart=true
 autorestart=true
 stdout_logfile=/dev/fd/1