feat(router.py): add load balancing for async transcription calls

litellm/main.py

@@ -3313,6 +3313,7 @@ def image_generation(
 ##### Transcription #######################
 
 
+@client
 async def atranscription(*args, **kwargs):
     """
     Calls openai + azure whisper endpoints.

litellm/router.py

@@ -9,7 +9,7 @@
 
 import copy, httpx
 from datetime import datetime
-from typing import Dict, List, Optional, Union, Literal, Any
+from typing import Dict, List, Optional, Union, Literal, Any, BinaryIO
 import random, threading, time, traceback, uuid
 import litellm, openai
 from litellm.caching import RedisCache, InMemoryCache, DualCache
@@ -633,6 +633,84 @@ class Router:
                 self.fail_calls[model_name] += 1
             raise e
 
+    async def atranscription(self, file: BinaryIO, model: str, **kwargs):
+        try:
+            kwargs["model"] = model
+            kwargs["file"] = file
+            kwargs["original_function"] = self._atranscription
+            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+            timeout = kwargs.get("request_timeout", self.timeout)
+            kwargs.setdefault("metadata", {}).update({"model_group": model})
+            response = await self.async_function_with_fallbacks(**kwargs)
+
+            return response
+        except Exception as e:
+            raise e
+
+    async def _atranscription(self, file: BinaryIO, model: str, **kwargs):
+        try:
+            verbose_router_logger.debug(
+                f"Inside _atranscription()- model: {model}; kwargs: {kwargs}"
+            )
+            deployment = self.get_available_deployment(
+                model=model,
+                messages=[{"role": "user", "content": "prompt"}],
+                specific_deployment=kwargs.pop("specific_deployment", None),
+            )
+            kwargs.setdefault("metadata", {}).update(
+                {
+                    "deployment": deployment["litellm_params"]["model"],
+                    "model_info": deployment.get("model_info", {}),
+                }
+            )
+            kwargs["model_info"] = deployment.get("model_info", {})
+            data = deployment["litellm_params"].copy()
+            model_name = data["model"]
+            for k, v in self.default_litellm_params.items():
+                if (
+                    k not in kwargs
+                ):  # prioritize model-specific params > default router params
+                    kwargs[k] = v
+                elif k == "metadata":
+                    kwargs[k].update(v)
+
+            potential_model_client = self._get_client(
+                deployment=deployment, kwargs=kwargs, client_type="async"
+            )
+            # check if provided keys == client keys #
+            dynamic_api_key = kwargs.get("api_key", None)
+            if (
+                dynamic_api_key is not None
+                and potential_model_client is not None
+                and dynamic_api_key != potential_model_client.api_key
+            ):
+                model_client = None
+            else:
+                model_client = potential_model_client
+
+            self.total_calls[model_name] += 1
+            response = await litellm.atranscription(
+                **{
+                    **data,
+                    "file": file,
+                    "caching": self.cache_responses,
+                    "client": model_client,
+                    **kwargs,
+                }
+            )
+            self.success_calls[model_name] += 1
+            verbose_router_logger.info(
+                f"litellm.atranscription(model={model_name})\033[32m 200 OK\033[0m"
+            )
+            return response
+        except Exception as e:
+            verbose_router_logger.info(
+                f"litellm.atranscription(model={model_name})\033[31m Exception {str(e)}\033[0m"
+            )
+            if model_name is not None:
+                self.fail_calls[model_name] += 1
+            raise e
+
     async def amoderation(self, model: str, input: str, **kwargs):
         try:
             kwargs["model"] = model