Merge pull request #2401 from BerriAI/litellm_transcription_endpoints

feat(main.py): support openai transcription endpoints

litellm/main.py

@@ -8,7 +8,7 @@
 #  Thank you ! We ❤️ you! - Krrish & Ishaan
 
 import os, openai, sys, json, inspect, uuid, datetime, threading
-from typing import Any, Literal, Union
+from typing import Any, Literal, Union, BinaryIO
 from functools import partial
 import dotenv, traceback, random, asyncio, time, contextvars
 from copy import deepcopy
@@ -88,6 +88,7 @@ from litellm.utils import (
     read_config_args,
     Choices,
     Message,
+    TranscriptionResponse,
 )
 
 ####### ENVIRONMENT VARIABLES ###################
@@ -3048,7 +3049,6 @@ def moderation(
     return response
 
 
-##### Moderation #######################
 @client
 async def amoderation(input: str, model: str, api_key: Optional[str] = None, **kwargs):
     # only supports open ai for now
@@ -3071,11 +3071,11 @@ async def aimage_generation(*args, **kwargs):
     Asynchronously calls the `image_generation` function with the given arguments and keyword arguments.
 
     Parameters:
-    - `args` (tuple): Positional arguments to be passed to the `embedding` function.
-    - `kwargs` (dict): Keyword arguments to be passed to the `embedding` function.
+    - `args` (tuple): Positional arguments to be passed to the `image_generation` function.
+    - `kwargs` (dict): Keyword arguments to be passed to the `image_generation` function.
 
     Returns:
-    - `response` (Any): The response returned by the `embedding` function.
+    - `response` (Any): The response returned by the `image_generation` function.
     """
     loop = asyncio.get_event_loop()
     model = args[0] if len(args) > 0 else kwargs["model"]
@@ -3097,7 +3097,7 @@ async def aimage_generation(*args, **kwargs):
         # Await normally
         init_response = await loop.run_in_executor(None, func_with_context)
         if isinstance(init_response, dict) or isinstance(
-            init_response, ModelResponse
+            init_response, ImageResponse
         ):  ## CACHING SCENARIO
             response = init_response
         elif asyncio.iscoroutine(init_response):
@@ -3315,6 +3315,142 @@ def image_generation(
         )
 
 
+##### Transcription #######################
+
+
+async def atranscription(*args, **kwargs):
+    """
+    Calls openai + azure whisper endpoints.
+
+    Allows router to load balance between them
+    """
+    loop = asyncio.get_event_loop()
+    model = args[0] if len(args) > 0 else kwargs["model"]
+    ### PASS ARGS TO Image Generation ###
+    kwargs["atranscription"] = True
+    custom_llm_provider = None
+    try:
+        # Use a partial function to pass your keyword arguments
+        func = partial(transcription, *args, **kwargs)
+
+        # Add the context to the function
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
+
+        _, custom_llm_provider, _, _ = get_llm_provider(
+            model=model, api_base=kwargs.get("api_base", None)
+        )
+
+        # Await normally
+        init_response = await loop.run_in_executor(None, func_with_context)
+        if isinstance(init_response, dict) or isinstance(
+            init_response, TranscriptionResponse
+        ):  ## CACHING SCENARIO
+            response = init_response
+        elif asyncio.iscoroutine(init_response):
+            response = await init_response
+        else:
+            # Call the synchronous function using run_in_executor
+            response = await loop.run_in_executor(None, func_with_context)
+        return response
+    except Exception as e:
+        custom_llm_provider = custom_llm_provider or "openai"
+        raise exception_type(
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=args,
+        )
+
+
+@client
+def transcription(
+    model: str,
+    file: BinaryIO,
+    ## OPTIONAL OPENAI PARAMS ##
+    language: Optional[str] = None,
+    prompt: Optional[str] = None,
+    response_format: Optional[
+        Literal["json", "text", "srt", "verbose_json", "vtt"]
+    ] = None,
+    temperature: Optional[int] = None,  # openai defaults this to 0
+    ## LITELLM PARAMS ##
+    user: Optional[str] = None,
+    timeout=600,  # default to 10 minutes
+    api_key: Optional[str] = None,
+    api_base: Optional[str] = None,
+    api_version: Optional[str] = None,
+    litellm_logging_obj=None,
+    custom_llm_provider=None,
+    **kwargs,
+):
+    """
+    Calls openai + azure whisper endpoints.
+
+    Allows router to load balance between them
+    """
+    atranscriptions = kwargs.get("atranscriptions", False)
+    litellm_call_id = kwargs.get("litellm_call_id", None)
+    logger_fn = kwargs.get("logger_fn", None)
+    proxy_server_request = kwargs.get("proxy_server_request", None)
+    model_info = kwargs.get("model_info", None)
+    metadata = kwargs.get("metadata", {})
+
+    model_response = litellm.utils.TranscriptionResponse()
+
+    model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base)  # type: ignore
+
+    optional_params = {
+        "language": language,
+        "prompt": prompt,
+        "response_format": response_format,
+        "temperature": None,  # openai defaults this to 0
+    }
+
+    if custom_llm_provider == "azure":
+        # azure configs
+        api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+
+        api_version = (
+            api_version or litellm.api_version or get_secret("AZURE_API_VERSION")
+        )
+
+        azure_ad_token = kwargs.pop("azure_ad_token", None) or get_secret(
+            "AZURE_AD_TOKEN"
+        )
+
+        api_key = (
+            api_key
+            or litellm.api_key
+            or litellm.azure_key
+            or get_secret("AZURE_API_KEY")
+        )
+        response = azure_chat_completions.audio_transcriptions(
+            model=model,
+            audio_file=file,
+            optional_params=optional_params,
+            model_response=model_response,
+            atranscriptions=atranscriptions,
+            timeout=timeout,
+            logging_obj=litellm_logging_obj,
+            api_base=api_base,
+            api_key=api_key,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+        )
+    elif custom_llm_provider == "openai":
+        response = openai_chat_completions.audio_transcriptions(
+            model=model,
+            audio_file=file,
+            optional_params=optional_params,
+            model_response=model_response,
+            atranscriptions=atranscriptions,
+            timeout=timeout,
+            logging_obj=litellm_logging_obj,
+        )
+    return response
+
+
 ##### Health Endpoints #######################