Initial commit

backend/routes/transcription.py

@@ -0,0 +1,84 @@
+"""Transcription endpoints."""
+
+import asyncio
+import tempfile
+from pathlib import Path
+
+from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+
+from .. import models
+from ..services import transcribe
+from ..services.task_queue import create_background_task
+from ..utils.tasks import get_task_manager
+
+router = APIRouter()
+
+UPLOAD_CHUNK_SIZE = 1024 * 1024  # 1MB
+
+
+@router.post("/transcribe", response_model=models.TranscriptionResponse)
+async def transcribe_audio(
+    file: UploadFile = File(...),
+    language: str | None = Form(None),
+    model: str | None = Form(None),
+):
+    """Transcribe audio file to text."""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        while chunk := await file.read(UPLOAD_CHUNK_SIZE):
+            tmp.write(chunk)
+        tmp_path = tmp.name
+
+    try:
+        from ..utils.audio import load_audio
+        from ..backends import WHISPER_HF_REPOS
+
+        audio, sr = await asyncio.to_thread(load_audio, tmp_path)
+        duration = len(audio) / sr
+
+        whisper_model = transcribe.get_whisper_model()
+        model_size = model if model else whisper_model.model_size
+
+        valid_sizes = list(WHISPER_HF_REPOS.keys())
+        if model_size not in valid_sizes:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Invalid model size '{model_size}'. Must be one of: {', '.join(valid_sizes)}",
+            )
+
+        already_loaded = whisper_model.is_loaded() and whisper_model.model_size == model_size
+        if not already_loaded and not whisper_model._is_model_cached(model_size):
+            progress_model_name = f"whisper-{model_size}"
+            task_manager = get_task_manager()
+
+            async def download_whisper_background():
+                try:
+                    await whisper_model.load_model_async(model_size)
+                    task_manager.complete_download(progress_model_name)
+                except Exception as e:
+                    task_manager.error_download(progress_model_name, str(e))
+
+            task_manager.start_download(progress_model_name)
+            create_background_task(download_whisper_background())
+
+            raise HTTPException(
+                status_code=202,
+                detail={
+                    "message": f"Whisper model {model_size} is being downloaded. Please wait and try again.",
+                    "model_name": progress_model_name,
+                    "downloading": True,
+                },
+            )
+
+        text = await whisper_model.transcribe(tmp_path, language, model_size)
+
+        return models.TranscriptionResponse(
+            text=text,
+            duration=duration,
+        )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        Path(tmp_path).unlink(missing_ok=True)