feat(main.py): support openai transcription endpoints

enable user to load balance between openai + azure transcription endpoints

litellm/llms/openai.py

@@ -1,4 +1,4 @@
-from typing import Optional, Union, Any
+from typing import Optional, Union, Any, BinaryIO
 import types, time, json, traceback
 import httpx
 from .base import BaseLLM
@@ -9,6 +9,7 @@ from litellm.utils import (
     CustomStreamWrapper,
     convert_to_model_response_object,
     Usage,
+    TranscriptionResponse,
 )
 from typing import Callable, Optional
 import aiohttp, requests
@@ -766,6 +767,103 @@ class OpenAIChatCompletion(BaseLLM):
             else:
                 raise OpenAIError(status_code=500, message=str(e))
 
+    def audio_transcriptions(
+        self,
+        model: str,
+        audio_file: BinaryIO,
+        optional_params: dict,
+        model_response: TranscriptionResponse,
+        timeout: float,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client=None,
+        max_retries=None,
+        logging_obj=None,
+        atranscriptions: bool = False,
+    ):
+        data = {"model": model, "file": audio_file, **optional_params}
+        if atranscriptions == True:
+            return self.async_audio_transcriptions(
+                audio_file=audio_file,
+                data=data,
+                model_response=model_response,
+                timeout=timeout,
+                api_key=api_key,
+                api_base=api_base,
+                client=client,
+                max_retries=max_retries,
+                logging_obj=logging_obj,
+            )
+        if client is None:
+            openai_client = OpenAI(
+                api_key=api_key,
+                base_url=api_base,
+                http_client=litellm.client_session,
+                timeout=timeout,
+                max_retries=max_retries,
+            )
+        else:
+            openai_client = client
+        response = openai_client.audio.transcriptions.create(
+            **data, timeout=timeout  # type: ignore
+        )
+
+        stringified_response = response.model_dump()
+        ## LOGGING
+        logging_obj.post_call(
+            input=audio_file.name,
+            api_key=api_key,
+            additional_args={"complete_input_dict": data},
+            original_response=stringified_response,
+        )
+        final_response = convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, response_type="audio_transcription")  # type: ignore
+        return final_response
+
+    async def async_audio_transcriptions(
+        self,
+        audio_file: BinaryIO,
+        data: dict,
+        model_response: TranscriptionResponse,
+        timeout: float,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client=None,
+        max_retries=None,
+        logging_obj=None,
+    ):
+        response = None
+        try:
+            if client is None:
+                openai_aclient = AsyncOpenAI(
+                    api_key=api_key,
+                    base_url=api_base,
+                    http_client=litellm.aclient_session,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                )
+            else:
+                openai_aclient = client
+            response = await openai_aclient.audio.transcriptions.create(
+                **data, timeout=timeout
+            )  # type: ignore
+            stringified_response = response.model_dump()
+            ## LOGGING
+            logging_obj.post_call(
+                input=audio_file.name,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=stringified_response,
+            )
+            return convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, response_type="image_generation")  # type: ignore
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                original_response=str(e),
+            )
+            raise e
+
     async def ahealth_check(
         self,
         model: Optional[str],