fix(openai.py): fix openai response for /audio/speech endpoint

litellm/proxy/proxy_server.py

@@ -79,6 +79,9 @@ def generate_feedback_box():
 
 
 import litellm
+from litellm.types.llms.openai import (
+    HttpxBinaryResponseContent,
+)
 from litellm.proxy.utils import (
     PrismaClient,
     DBClient,
@@ -4875,6 +4878,143 @@ async def image_generation(
             )
 
 
+@router.post(
+    "/v1/audio/speech",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["audio"],
+)
+@router.post(
+    "/audio/speech",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["audio"],
+)
+async def audio_speech(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Same params as:
+
+    https://platform.openai.com/docs/api-reference/audio/createSpeech
+    """
+    global proxy_logging_obj
+    data: Dict = {}
+    try:
+        # Use orjson to parse JSON data, orjson speeds up requests significantly
+        body = await request.body()
+        data = orjson.loads(body)
+
+        # Include original request and headers in the data
+        data["proxy_server_request"] = {  # type: ignore
+            "url": str(request.url),
+            "method": request.method,
+            "headers": dict(request.headers),
+            "body": copy.copy(data),  # use copy instead of deepcopy
+        }
+
+        if data.get("user", None) is None and user_api_key_dict.user_id is not None:
+            data["user"] = user_api_key_dict.user_id
+
+        if user_model:
+            data["model"] = user_model
+
+        if "metadata" not in data:
+            data["metadata"] = {}
+        data["metadata"]["user_api_key"] = user_api_key_dict.api_key
+        data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
+        _headers = dict(request.headers)
+        _headers.pop(
+            "authorization", None
+        )  # do not store the original `sk-..` api key in the db
+        data["metadata"]["headers"] = _headers
+        data["metadata"]["user_api_key_alias"] = getattr(
+            user_api_key_dict, "key_alias", None
+        )
+        data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id
+        data["metadata"]["user_api_key_team_id"] = getattr(
+            user_api_key_dict, "team_id", None
+        )
+        data["metadata"]["global_max_parallel_requests"] = general_settings.get(
+            "global_max_parallel_requests", None
+        )
+        data["metadata"]["user_api_key_team_alias"] = getattr(
+            user_api_key_dict, "team_alias", None
+        )
+        data["metadata"]["endpoint"] = str(request.url)
+
+        ### TEAM-SPECIFIC PARAMS ###
+        if user_api_key_dict.team_id is not None:
+            team_config = await proxy_config.load_team_config(
+                team_id=user_api_key_dict.team_id
+            )
+            if len(team_config) == 0:
+                pass
+            else:
+                team_id = team_config.pop("team_id", None)
+                data["metadata"]["team_id"] = team_id
+                data = {
+                    **team_config,
+                    **data,
+                }  # add the team-specific configs to the completion call
+
+        router_model_names = llm_router.model_names if llm_router is not None else []
+
+        ### CALL HOOKS ### - modify incoming data / reject request before calling the model
+        data = await proxy_logging_obj.pre_call_hook(
+            user_api_key_dict=user_api_key_dict, data=data, call_type="image_generation"
+        )
+
+        ## ROUTE TO CORRECT ENDPOINT ##
+        # skip router if user passed their key
+        if "api_key" in data:
+            response = await litellm.aspeech(**data)
+        elif (
+            llm_router is not None and data["model"] in router_model_names
+        ):  # model in router model list
+            response = await llm_router.aspeech(**data)
+        elif (
+            llm_router is not None and data["model"] in llm_router.deployment_names
+        ):  # model in router deployments, calling a specific deployment on the router
+            response = await llm_router.aspeech(**data, specific_deployment=True)
+        elif (
+            llm_router is not None
+            and llm_router.model_group_alias is not None
+            and data["model"] in llm_router.model_group_alias
+        ):  # model set in model_group_alias
+            response = await llm_router.aspeech(
+                **data
+            )  # ensure this goes the llm_router, router will do the correct alias mapping
+        elif (
+            llm_router is not None
+            and data["model"] not in router_model_names
+            and llm_router.default_deployment is not None
+        ):  # model in router deployments, calling a specific deployment on the router
+            response = await llm_router.aspeech(**data)
+        elif user_model is not None:  # `litellm --model <your-model-name>`
+            response = await litellm.aspeech(**data)
+        else:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail={
+                    "error": "audio_speech: Invalid model name passed in model="
+                    + data.get("model", "")
+                },
+            )
+
+        # Printing each chunk size
+        async def generate(_response: HttpxBinaryResponseContent):
+            _generator = await _response.aiter_bytes(chunk_size=1024)
+            async for chunk in _generator:
+                yield chunk
+
+        return StreamingResponse(generate(response), media_type="audio/mpeg")
+
+    except Exception as e:
+        traceback.print_exc()
+        raise e
+
+
 @router.post(
     "/v1/audio/transcriptions",
     dependencies=[Depends(user_api_key_auth)],