Merge pull request #2401 from BerriAI/litellm_transcription_endpoints

feat(main.py): support openai transcription endpoints

litellm/utils.py

@@ -10,7 +10,6 @@
 import sys, re, binascii, struct
 import litellm
 import dotenv, json, traceback, threading, base64, ast
-
 import subprocess, os
 from os.path import abspath, join, dirname
 import litellm, openai
@@ -98,7 +97,7 @@ try:
 except Exception as e:
     verbose_logger.debug(f"Exception import enterprise features {str(e)}")
 
-from typing import cast, List, Dict, Union, Optional, Literal, Any
+from typing import cast, List, Dict, Union, Optional, Literal, Any, BinaryIO
 from .caching import Cache
 from concurrent.futures import ThreadPoolExecutor
 
@@ -790,6 +789,38 @@ class ImageResponse(OpenAIObject):
             return self.dict()
 
 
+class TranscriptionResponse(OpenAIObject):
+    text: Optional[str] = None
+
+    _hidden_params: dict = {}
+
+    def __init__(self, text=None):
+        super().__init__(text=text)
+
+    def __contains__(self, key):
+        # Define custom behavior for the 'in' operator
+        return hasattr(self, key)
+
+    def get(self, key, default=None):
+        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
+        return getattr(self, key, default)
+
+    def __getitem__(self, key):
+        # Allow dictionary-style access to attributes
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        # Allow dictionary-style assignment of attributes
+        setattr(self, key, value)
+
+    def json(self, **kwargs):
+        try:
+            return self.model_dump()  # noqa
+        except:
+            # if using pydantic v1
+            return self.dict()
+
+
 ############################################################
 def print_verbose(print_statement, logger_only: bool = False):
     try:
@@ -815,6 +846,8 @@ class CallTypes(Enum):
     aimage_generation = "aimage_generation"
     moderation = "moderation"
     amoderation = "amoderation"
+    atranscription = "atranscription"
+    transcription = "transcription"
 
 
 # Logging function -> log the exact model details + what's being sent | Non-BlockingP
@@ -948,6 +981,7 @@ class Logging:
                 curl_command = self.model_call_details
 
             # only print verbose if verbose logger is not set
+
             if verbose_logger.level == 0:
                 # this means verbose logger was not switched on - user is in litellm.set_verbose=True
                 print_verbose(f"\033[92m{curl_command}\033[0m\n")
@@ -2293,6 +2327,12 @@ def client(original_function):
                 or call_type == CallTypes.text_completion.value
             ):
                 messages = args[0] if len(args) > 0 else kwargs["prompt"]
+            elif (
+                call_type == CallTypes.atranscription.value
+                or call_type == CallTypes.transcription.value
+            ):
+                _file_name: BinaryIO = args[1] if len(args) > 1 else kwargs["file"]
+                messages = _file_name.name
             stream = True if "stream" in kwargs and kwargs["stream"] == True else False
             logging_obj = Logging(
                 model=model,
@@ -6264,10 +6304,10 @@ def convert_to_streaming_response(response_object: Optional[dict] = None):
 def convert_to_model_response_object(
     response_object: Optional[dict] = None,
     model_response_object: Optional[
-        Union[ModelResponse, EmbeddingResponse, ImageResponse]
+        Union[ModelResponse, EmbeddingResponse, ImageResponse, TranscriptionResponse]
     ] = None,
     response_type: Literal[
-        "completion", "embedding", "image_generation"
+        "completion", "embedding", "image_generation", "audio_transcription"
     ] = "completion",
     stream=False,
     start_time=None,
@@ -6378,6 +6418,19 @@ def convert_to_model_response_object(
                 model_response_object.data = response_object["data"]
 
             return model_response_object
+        elif response_type == "audio_transcription" and (
+            model_response_object is None
+            or isinstance(model_response_object, TranscriptionResponse)
+        ):
+            if response_object is None:
+                raise Exception("Error in response object format")
+
+            if model_response_object is None:
+                model_response_object = TranscriptionResponse()
+
+            if "text" in response_object:
+                model_response_object.text = response_object["text"]
+            return model_response_object
     except Exception as e:
         raise Exception(f"Invalid response object {traceback.format_exc()}")