fix(whisper---handle-openai/azure-vtt-response-format): Fixes https://github.com/BerriAI/litellm/issues/4595

litellm/llms/openai.py

@@ -348,6 +348,104 @@ class DeepInfraConfig:
         return optional_params
 
 
+class GroqConfig:
+    """
+    Reference: https://deepinfra.com/docs/advanced/openai_api
+
+    The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
+    """
+
+    frequency_penalty: Optional[int] = None
+    function_call: Optional[Union[str, dict]] = None
+    functions: Optional[list] = None
+    logit_bias: Optional[dict] = None
+    max_tokens: Optional[int] = None
+    n: Optional[int] = None
+    presence_penalty: Optional[int] = None
+    stop: Optional[Union[str, list]] = None
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    response_format: Optional[dict] = None
+    tools: Optional[list] = None
+    tool_choice: Optional[Union[str, dict]] = None
+
+    def __init__(
+        self,
+        frequency_penalty: Optional[int] = None,
+        function_call: Optional[Union[str, dict]] = None,
+        functions: Optional[list] = None,
+        logit_bias: Optional[dict] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[int] = None,
+        stop: Optional[Union[str, list]] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        response_format: Optional[dict] = None,
+        tools: Optional[list] = None,
+        tool_choice: Optional[Union[str, dict]] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params_stt(self):
+        return [
+            "prompt",
+            "response_format",
+            "temperature",
+            "language",
+        ]
+
+    def get_supported_openai_response_formats_stt(self) -> List[str]:
+        return ["json", "verbose_json", "text"]
+
+    def map_openai_params_stt(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        response_formats = self.get_supported_openai_response_formats_stt()
+        for param, value in non_default_params.items():
+            if param == "response_format":
+                if value in response_formats:
+                    optional_params[param] = value
+                else:
+                    if litellm.drop_params is True or drop_params is True:
+                        pass
+                    else:
+                        raise litellm.utils.UnsupportedParamsError(
+                            message="Groq doesn't support response_format={}. To drop unsupported openai params from the call, set `litellm.drop_params = True`".format(
+                                value
+                            ),
+                            status_code=400,
+                        )
+            else:
+                optional_params[param] = value
+        return optional_params
+
+
 class OpenAIConfig:
     """
     Reference: https://platform.openai.com/docs/api-reference/chat/create
@@ -1360,7 +1458,11 @@ class OpenAIChatCompletion(BaseLLM):
             **data, timeout=timeout  # type: ignore
         )
 
-        stringified_response = response.model_dump()
+        if isinstance(response, BaseModel):
+            stringified_response = response.model_dump()
+        else:
+            stringified_response = TranscriptionResponse(text=response).model_dump()
+
         ## LOGGING
         logging_obj.post_call(
             input=audio_file.name,
@@ -1400,7 +1502,10 @@ class OpenAIChatCompletion(BaseLLM):
                 timeout=timeout,
             )
             logging_obj.model_call_details["response_headers"] = headers
-            stringified_response = response.model_dump()
+            if isinstance(response, BaseModel):
+                stringified_response = response.model_dump()
+            else:
+                stringified_response = TranscriptionResponse(text=response).model_dump()
             ## LOGGING
             logging_obj.post_call(
                 input=audio_file.name,