Refactor project structure for modularity

Move model and pipeline components into dedicated modules
Extract utilities from CLI into shared helpers
Update imports throughout codebase
Refactor project structure for modularity

Move model configuration and hardware selection into `models/` package
Extract pipeline steps into `pipeline/` package
Add utility helpers in `utils/` package
Update CLI imports and references

src/audio_summary/pipeline/transcription.py

@@ -0,0 +1,68 @@
+"""Speech-to-Text on Apple Silicon.
+
+Two engines are supported, selected through ``config.STTModel.engine``:
+
+* ``voxtral`` -> Mistral Voxtral via ``mlx-audio``. Outperforms Whisper
+  large-v3 on most benchmarks and natively handles long audio with streaming.
+* ``whisper`` -> ``mlx-whisper`` fallback for memory-constrained Macs.
+"""
+
+from ..models import STTModel
+
+
+def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) -> str:
+    """Transcribe using Mistral Voxtral through mlx-audio."""
+    from mlx_audio.stt.utils import load
+
+    model = load(model_repo)
+    # Voxtral handles long audio natively; ``generate`` returns an object with a
+    # ``.text`` attribute. ``language`` is optional (auto-detected when None).
+    kwargs: dict = {}
+    if language:
+        kwargs["language"] = language
+    result = model.generate(file_path, **kwargs)
+    return result.text.strip()
+
+
+def _transcribe_whisper(file_path: str, model_repo: str, language: str | None) -> str:
+    """Transcribe using the mlx-whisper fallback engine."""
+    import mlx_whisper
+
+    result = mlx_whisper.transcribe(
+        file_path,
+        path_or_hf_repo=model_repo,
+        language=language,  # None triggers auto-detection
+    )
+    return result["text"].strip()
+
+
+def transcribe_file(
+    file_path: str,
+    output_file: str,
+    stt_model: STTModel,
+    language: str | None = None,
+) -> str:
+    """Transcribe ``file_path`` and persist the transcript to ``output_file``.
+
+    ``language`` may be a language code (e.g. ``"en"``, ``"fr"``) or ``None`` for
+    automatic detection. Returns the full transcript text.
+    """
+    print(f"Transcribing with {stt_model.engine} model: {stt_model.repo}")
+    if language:
+        print(f"Transcription language: {language}")
+    else:
+        print("Using automatic language detection")
+    print("Starting transcription (this may take a while for longer files)...")
+
+    if stt_model.engine == "voxtral":
+        text = _transcribe_voxtral(file_path, stt_model.repo, language)
+    elif stt_model.engine == "whisper":
+        text = _transcribe_whisper(file_path, stt_model.repo, language)
+    else:
+        raise ValueError(f"Unknown STT engine: {stt_model.engine!r}")
+
+    with open(output_file, "w") as tmp_file:
+        tmp_file.write(text)
+    print(f"Transcription saved to file: {output_file}")
+
+    return text