added file path params for server whisper API

nexa/gguf/server/nexa_service.py

@@ -1720,18 +1720,28 @@ async def process_audio(
     language: Optional[str] = Query(
         None, description="Language code (e.g. 'en', 'fr') for transcription."),
     temperature: Optional[float] = Query(
-        0.0, description="Temperature for sampling.")
+        0.0, description="Temperature for sampling."),
+    tmp_file_dir: Optional[str] = Query(
+        None, description="Directory to save temporary audio file. If not provided, uses system temp directory.")
 ):
+    temp_audio_path = None
     try:
         if not whisper_model:
             raise HTTPException(
                 status_code=400,
                 detail="Whisper model is not loaded. Please load a Whisper model first."
             )
 
-        with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_audio:
-            temp_audio.write(await file.read())
-            temp_audio_path = temp_audio.name
+        # Modify temp file creation to use custom directory if provided
+        if tmp_file_dir:
+            os.makedirs(tmp_file_dir, exist_ok=True)
+            temp_audio_path = os.path.join(tmp_file_dir, f"temp_{file.filename}")
+            with open(temp_audio_path, 'wb') as temp_audio:
+                temp_audio.write(await file.read())
+        else:
+            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_audio:
+                temp_audio.write(await file.read())
+                temp_audio_path = temp_audio.name
 
         # Set up parameters for Whisper or similar model
         task_params = {
@@ -1753,9 +1763,13 @@ async def process_audio(
         raise HTTPException(
             status_code=500, detail=f"Error during {task}: {str(e)}")
     finally:
-        if 'temp_audio_path' in locals() and os.path.exists(temp_audio_path):
-            os.unlink(temp_audio_path)
-
+        # Clean up temp file if it was created
+        if temp_audio_path and os.path.exists(temp_audio_path):
+            try:
+                os.unlink(temp_audio_path)
+                logging.info(f"Cleaned up temporary file: {temp_audio_path}")
+            except Exception as e:
+                logging.error(f"Error cleaning up temporary file {temp_audio_path}: {e}")
 
 @app.post("/v1/audio/processing_stream", tags=["Audio"])
 async def processing_stream_audio(
@@ -1768,18 +1782,30 @@ async def processing_stream_audio(
         "auto", description="Language code (e.g., 'en', 'fr')"),
     min_chunk: Optional[float] = Query(
         1.0, description="Minimum chunk duration for streaming"),
+    tmp_file_dir: Optional[str] = Query(
+        None, description="Directory to save temporary audio file. If not provided, uses system memory.")
 ):
+    temp_audio_path = None
     try:
         if not whisper_model:
             raise HTTPException(
                 status_code=400,
                 detail="Whisper model is not loaded. Please load a Whisper model first."
             )
 
-        # Read the entire file into memory
-        audio_bytes = await file.read()
-        a_full = load_audio_from_bytes(audio_bytes)
-        duration = len(a_full) / SAMPLING_RATE
+        # Modify audio loading to optionally save to file
+        if tmp_file_dir:
+            os.makedirs(tmp_file_dir, exist_ok=True)
+            temp_audio_path = os.path.join(tmp_file_dir, f"temp_{file.filename}")
+            with open(temp_audio_path, 'wb') as temp_audio:
+                audio_bytes = await file.read()
+                temp_audio.write(audio_bytes)
+            # Read the saved file
+            a_full = load_audio_from_bytes(audio_bytes)
+        else:
+            # Original in-memory processing
+            audio_bytes = await file.read()
+            a_full = load_audio_from_bytes(audio_bytes)
 
         # Only include language parameter if task is "transcribe"
         # For "translate", the language is always defined as "en"
@@ -1798,13 +1824,13 @@ async def processing_stream_audio(
 
         def stream_generator():
             nonlocal beg
-            while beg < duration:
+            while beg < len(a_full) / SAMPLING_RATE:
                 now = time.time() - start
                 if now < beg + min_chunk:
                     time.sleep((beg + min_chunk) - now)
                 end = time.time() - start
-                if end > duration:
-                    end = duration
+                if end > len(a_full) / SAMPLING_RATE:
+                    end = len(a_full) / SAMPLING_RATE
 
                 chunk_samples = int((end - beg)*SAMPLING_RATE)
                 chunk_audio = a_full[int(
@@ -1839,7 +1865,14 @@ def stream_generator():
     except Exception as e:
         logging.error(f"Error in audio processing stream: {e}")
         raise HTTPException(status_code=500, detail=str(e))
-
+    finally:
+        # Clean up temp file if it was created
+        if temp_audio_path and os.path.exists(temp_audio_path):
+            try:
+                os.unlink(temp_audio_path)
+                logging.info(f"Cleaned up temporary file: {temp_audio_path}")
+            except Exception as e:
+                logging.error(f"Error cleaning up temporary file {temp_audio_path}: {e}")
 
 @app.post("/v1/audiolm/chat/completions", tags=["AudioLM"])
 async def audio_chat_completions(