From 82091de39349c96d3477641ad5f47e4eae0cd64b Mon Sep 17 00:00:00 2001
From: Krrish Dholakia <krrishdholakia@gmail.com>
Date: Fri, 12 Sep 2025 17:15:14 -0700
Subject: [PATCH] feat(hosted_vllm/): transcription endpoint support

Closes https://github.com/BerriAI/litellm/issues/361#issuecomment-3244548055
---
 litellm/constants.py                          | 10 ++-
 .../audio_transcription/transformation.py     | 10 +--
 litellm/llms/custom_httpx/llm_http_handler.py | 52 +++++++----
 .../transcriptions/transformation.py          | 86 +++++++++++++++++++
 .../transcriptions/gpt_transformation.py      | 11 ++-
 litellm/llms/openai/transcriptions/handler.py |  7 +-
 .../transcriptions/whisper_transformation.py  | 39 +++++++--
 litellm/main.py                               | 54 +++++++-----
 litellm/proxy/_new_secret_config.yaml         |  5 ++
 litellm/utils.py                              | 18 +++-
 10 files changed, 229 insertions(+), 63 deletions(-)
 create mode 100644 litellm/llms/hosted_vllm/transcriptions/transformation.py

diff --git a/litellm/constants.py b/litellm/constants.py
index 75c25d9ea9..c0ce0f265b 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int(
     os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10)
 )
 DEFAULT_NUM_WORKERS_LITELLM_PROXY = int(
-    os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4)
+    os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1)
 )
 DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512))
 SQS_SEND_MESSAGE_ACTION = "SendMessage"
@@ -60,7 +60,9 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int(
     os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128)
 )
 DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int(
-    os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512)
+    os.getenv(
+        "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512
+    )
 )
 
 # Generic fallback for unknown models
@@ -949,7 +951,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token"
 DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job"
 PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics"
 CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data"
-CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000))
+CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(
+    os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)
+)
 SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup"
 SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500))
 SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000))
diff --git a/litellm/llms/base_llm/audio_transcription/transformation.py b/litellm/llms/base_llm/audio_transcription/transformation.py
index 179b8d0fb0..c20ee0f737 100644
--- a/litellm/llms/base_llm/audio_transcription/transformation.py
+++ b/litellm/llms/base_llm/audio_transcription/transformation.py
@@ -23,12 +23,13 @@ else:
 class AudioTranscriptionRequestData:
     """
     Structured data for audio transcription requests.
-    
+
     Attributes:
         data: The request data (form data for multipart, json data for regular requests)
         files: Optional files dict for multipart form data
         content_type: Optional content type override
     """
+
     data: Union[dict, bytes]
     files: Optional[dict] = None
     content_type: Optional[str] = None
@@ -66,13 +67,11 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC):
         audio_file: FileTypes,
         optional_params: dict,
         litellm_params: dict,
-    ) -> Union[AudioTranscriptionRequestData, Dict]:
+    ) -> AudioTranscriptionRequestData:
         raise NotImplementedError(
             "AudioTranscriptionConfig needs a request transformation for audio transcription models"
         )
 
-
-    
     def transform_audio_transcription_response(
         self,
         raw_response: httpx.Response,
@@ -110,7 +109,6 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC):
         raise NotImplementedError(
             "AudioTranscriptionConfig does not need a response transformation for audio transcription models"
         )
-    
 
     def get_provider_specific_params(
         self,
@@ -141,7 +139,7 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC):
             provider_specific_params[key] = value
 
         return provider_specific_params
-    
+
     def _should_exclude_param(
         self,
         param_name: str,
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 13133a56aa..5dc0d2bb95 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -2221,7 +2221,9 @@ class BaseLLMHTTPHandler:
 
         if isinstance(transformed_request, dict) and "method" in transformed_request:
             # Handle pre-signed requests (e.g., from Bedrock S3 uploads)
-            upload_response = getattr(sync_httpx_client, transformed_request["method"].lower())(
+            upload_response = getattr(
+                sync_httpx_client, transformed_request["method"].lower()
+            )(
                 url=transformed_request["url"],
                 headers=transformed_request["headers"],
                 data=transformed_request["data"],
@@ -2233,8 +2235,8 @@ class BaseLLMHTTPHandler:
             # Handle traditional file uploads
             # Ensure transformed_request is a string for httpx compatibility
             if isinstance(transformed_request, bytes):
-                transformed_request = transformed_request.decode('utf-8')
-            
+                transformed_request = transformed_request.decode("utf-8")
+
             # Use the HTTP method specified by the provider config
             http_method = provider_config.file_upload_http_method.upper()
             if http_method == "PUT":
@@ -2310,7 +2312,7 @@ class BaseLLMHTTPHandler:
             )
         else:
             async_httpx_client = client
-        
+
         #########################################################
         # Debug Logging
         #########################################################
@@ -2326,7 +2328,9 @@ class BaseLLMHTTPHandler:
 
         if isinstance(transformed_request, dict) and "method" in transformed_request:
             # Handle pre-signed requests (e.g., from Bedrock S3 uploads)
-            upload_response = await getattr(async_httpx_client, transformed_request["method"].lower())(
+            upload_response = await getattr(
+                async_httpx_client, transformed_request["method"].lower()
+            )(
                 url=transformed_request["url"],
                 headers=transformed_request["headers"],
                 data=transformed_request["data"],
@@ -2338,8 +2342,8 @@ class BaseLLMHTTPHandler:
             # Handle traditional file uploads
             # Ensure transformed_request is a string for httpx compatibility
             if isinstance(transformed_request, bytes):
-                transformed_request = transformed_request.decode('utf-8')
-            
+                transformed_request = transformed_request.decode("utf-8")
+
             # Use the HTTP method specified by the provider config
             http_method = provider_config.file_upload_http_method.upper()
             if http_method == "PUT":
@@ -2460,9 +2464,14 @@ class BaseLLMHTTPHandler:
             sync_httpx_client = client
 
         try:
-            if isinstance(transformed_request, dict) and "method" in transformed_request:
+            if (
+                isinstance(transformed_request, dict)
+                and "method" in transformed_request
+            ):
                 # Handle pre-signed requests (e.g., from Bedrock with AWS auth)
-                batch_response = getattr(sync_httpx_client, transformed_request["method"].lower())(
+                batch_response = getattr(
+                    sync_httpx_client, transformed_request["method"].lower()
+                )(
                     url=transformed_request["url"],
                     headers=transformed_request["headers"],
                     data=transformed_request["data"],
@@ -2492,8 +2501,11 @@ class BaseLLMHTTPHandler:
             )
 
         # Store original request for response transformation
-        litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data}
-        
+        litellm_params_with_request = {
+            **litellm_params,
+            "original_batch_request": create_batch_data,
+        }
+
         return provider_config.transform_create_batch_response(
             model=None,
             raw_response=batch_response,
@@ -2522,7 +2534,7 @@ class BaseLLMHTTPHandler:
             )
         else:
             async_httpx_client = client
-        
+
         #########################################################
         # Debug Logging
         #########################################################
@@ -2537,9 +2549,14 @@ class BaseLLMHTTPHandler:
         )
 
         try:
-            if isinstance(transformed_request, dict) and "method" in transformed_request:
+            if (
+                isinstance(transformed_request, dict)
+                and "method" in transformed_request
+            ):
                 # Handle pre-signed requests (e.g., from Bedrock with AWS auth)
-                batch_response = await getattr(async_httpx_client, transformed_request["method"].lower())(
+                batch_response = await getattr(
+                    async_httpx_client, transformed_request["method"].lower()
+                )(
                     url=transformed_request["url"],
                     headers=transformed_request["headers"],
                     data=transformed_request["data"],
@@ -2569,8 +2586,11 @@ class BaseLLMHTTPHandler:
             )
 
         # Store original request for response transformation (for async version)
-        litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data or {}}
-        
+        litellm_params_with_request = {
+            **litellm_params,
+            "original_batch_request": create_batch_data or {},
+        }
+
         return provider_config.transform_create_batch_response(
             model=None,
             raw_response=batch_response,
diff --git a/litellm/llms/hosted_vllm/transcriptions/transformation.py b/litellm/llms/hosted_vllm/transcriptions/transformation.py
new file mode 100644
index 0000000000..14ed278c97
--- /dev/null
+++ b/litellm/llms/hosted_vllm/transcriptions/transformation.py
@@ -0,0 +1,86 @@
+"""
+Transformation logic for Hosted VLLM rerank
+"""
+
+import uuid
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.audio_transcription.transformation import (
+    AudioTranscriptionRequestData,
+)
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
+from litellm.llms.openai.transcriptions.whisper_transformation import (
+    OpenAIWhisperAudioTranscriptionConfig,
+)
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.rerank import (
+    OptionalRerankParams,
+    RerankBilledUnits,
+    RerankRequest,
+    RerankResponse,
+    RerankResponseDocument,
+    RerankResponseMeta,
+    RerankResponseResult,
+    RerankTokens,
+)
+from litellm.types.utils import FileTypes
+
+
+class HostedVLLMAudioTranscriptionError(BaseLLMException):
+    def __init__(
+        self,
+        status_code: int,
+        message: str,
+        headers: Optional[Union[dict, httpx.Headers]] = None,
+    ):
+        super().__init__(status_code=status_code, message=message, headers=headers)
+
+
+class HostedVLLMAudioTranscriptionConfig(OpenAIWhisperAudioTranscriptionConfig):
+    def __init__(self) -> None:
+        pass
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        if api_base:
+            # Remove trailing slashes and ensure clean base URL
+            api_base = api_base.rstrip("/")
+            if not api_base.endswith("/v1/audio/transcriptions"):
+                api_base = f"{api_base}/v1/audio/transcriptions"
+            return api_base
+        raise ValueError("api_base must be provided for Hosted VLLM rerank")
+
+    def transform_audio_transcription_request(
+        self,
+        model: str,
+        audio_file: FileTypes,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> AudioTranscriptionRequestData:
+        """
+        Transform the audio transcription request
+        """
+
+        data = {"model": model, "file": audio_file, **optional_params}
+
+        if "response_format" not in data or (
+            data["response_format"] == "text" or data["response_format"] == "json"
+        ):
+            data["response_format"] = (
+                "verbose_json"  # ensures 'duration' is received - used for cost calculation
+            )
+
+        return AudioTranscriptionRequestData(
+            data=data,
+        )
diff --git a/litellm/llms/openai/transcriptions/gpt_transformation.py b/litellm/llms/openai/transcriptions/gpt_transformation.py
index 796e10f515..34621c44e2 100644
--- a/litellm/llms/openai/transcriptions/gpt_transformation.py
+++ b/litellm/llms/openai/transcriptions/gpt_transformation.py
@@ -1,5 +1,8 @@
 from typing import List
 
+from litellm.llms.base_llm.audio_transcription.transformation import (
+    AudioTranscriptionRequestData,
+)
 from litellm.types.llms.openai import OpenAIAudioTranscriptionOptionalParams
 from litellm.types.utils import FileTypes
 
@@ -27,8 +30,12 @@ class OpenAIGPTAudioTranscriptionConfig(OpenAIWhisperAudioTranscriptionConfig):
         audio_file: FileTypes,
         optional_params: dict,
         litellm_params: dict,
-    ) -> dict:
+    ) -> AudioTranscriptionRequestData:
         """
         Transform the audio transcription request
         """
-        return {"model": model, "file": audio_file, **optional_params}
+        data = {"model": model, "file": audio_file, **optional_params}
+
+        return AudioTranscriptionRequestData(
+            data=data,
+        )
diff --git a/litellm/llms/openai/transcriptions/handler.py b/litellm/llms/openai/transcriptions/handler.py
index 4fe48dd3c6..03ac34f3dd 100644
--- a/litellm/llms/openai/transcriptions/handler.py
+++ b/litellm/llms/openai/transcriptions/handler.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional, Union, cast
 
 import httpx
 from openai import AsyncOpenAI, OpenAI
@@ -93,15 +93,14 @@ class OpenAIAudioTranscription(OpenAIChatCompletion):
         Handle audio transcription request
         """
         if provider_config is not None:
-            data = provider_config.transform_audio_transcription_request(
+            transformed_data = provider_config.transform_audio_transcription_request(
                 model=model,
                 audio_file=audio_file,
                 optional_params=optional_params,
                 litellm_params=litellm_params,
             )
 
-            if not isinstance(data, dict):
-                raise ValueError("OpenAI transformation route requires a dict")
+            data = cast(dict, transformed_data.data)
         else:
             data = {"model": model, "file": audio_file, **optional_params}
 
diff --git a/litellm/llms/openai/transcriptions/whisper_transformation.py b/litellm/llms/openai/transcriptions/whisper_transformation.py
index c0ccc71579..bb14b4d47a 100644
--- a/litellm/llms/openai/transcriptions/whisper_transformation.py
+++ b/litellm/llms/openai/transcriptions/whisper_transformation.py
@@ -1,8 +1,9 @@
 from typing import List, Optional, Union
 
-from httpx import Headers
+from httpx import Headers, Response
 
 from litellm.llms.base_llm.audio_transcription.transformation import (
+    AudioTranscriptionRequestData,
     BaseAudioTranscriptionConfig,
 )
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
@@ -11,7 +12,7 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     OpenAIAudioTranscriptionOptionalParams,
 )
-from litellm.types.utils import FileTypes
+from litellm.types.utils import FileTypes, TranscriptionResponse
 
 from ..common_utils import OpenAIError
 
@@ -72,7 +73,7 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
         audio_file: FileTypes,
         optional_params: dict,
         litellm_params: dict,
-    ) -> dict:
+    ) -> AudioTranscriptionRequestData:
         """
         Transform the audio transcription request
         """
@@ -82,11 +83,13 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
         if "response_format" not in data or (
             data["response_format"] == "text" or data["response_format"] == "json"
         ):
-            data[
-                "response_format"
-            ] = "verbose_json"  # ensures 'duration' is received - used for cost calculation
+            data["response_format"] = (
+                "verbose_json"  # ensures 'duration' is received - used for cost calculation
+            )
 
-        return data
+        return AudioTranscriptionRequestData(
+            data=data,
+        )
 
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, Headers]
@@ -96,3 +99,25 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
             message=error_message,
             headers=headers,
         )
+
+    def transform_audio_transcription_response(
+        self,
+        raw_response: Response,
+    ) -> TranscriptionResponse:
+        try:
+            raw_response_json = raw_response.json()
+        except Exception as e:
+            raise ValueError(
+                f"Error transforming response to json: {str(e)}\nResponse: {raw_response.text}"
+            )
+
+        if any(
+            key in raw_response_json
+            for key in TranscriptionResponse.model_fields.keys()
+        ):
+            return TranscriptionResponse(**raw_response_json)
+        else:
+            raise ValueError(
+                "Invalid response format. Received response does not match the expected format. Got: ",
+                raw_response_json,
+            )
diff --git a/litellm/main.py b/litellm/main.py
index d7395eb145..b74f7e1d0f 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -150,9 +150,9 @@ from .llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
 from .llms.custom_llm import CustomLLM, custom_chat_llm_router
 from .llms.databricks.embed.handler import DatabricksEmbeddingHandler
 from .llms.deprecated_providers import aleph_alpha, palm
+from .llms.gemini.common_utils import get_api_key_from_env
 from .llms.groq.chat.handler import GroqChatCompletion
 from .llms.heroku.chat.transformation import HerokuChatConfig
-from .llms.gemini.common_utils import get_api_key_from_env
 from .llms.huggingface.embedding.handler import HuggingFaceEmbedding
 from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion
 from .llms.oci.chat.transformation import OCIChatConfig
@@ -358,7 +358,9 @@ async def acompletion(
     logprobs: Optional[bool] = None,
     top_logprobs: Optional[int] = None,
     deployment_id=None,
-    reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None,
+    reasoning_effort: Optional[
+        Literal["none", "minimal", "low", "medium", "high", "default"]
+    ] = None,
     safety_identifier: Optional[str] = None,
     # set api_base, api_version, api_key
     base_url: Optional[str] = None,
@@ -504,7 +506,9 @@ async def acompletion(
     }
     if custom_llm_provider is None:
         _, custom_llm_provider, _, _ = get_llm_provider(
-            model=model, custom_llm_provider=custom_llm_provider, api_base=completion_kwargs.get("base_url", None)
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            api_base=completion_kwargs.get("base_url", None),
         )
 
     fallbacks = fallbacks or litellm.model_fallbacks
@@ -899,7 +903,9 @@ def completion(  # type: ignore # noqa: PLR0915
     logit_bias: Optional[dict] = None,
     user: Optional[str] = None,
     # openai v1.0+ new params
-    reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None,
+    reasoning_effort: Optional[
+        Literal["none", "minimal", "low", "medium", "high", "default"]
+    ] = None,
     response_format: Optional[Union[dict, Type[BaseModel]]] = None,
     seed: Optional[int] = None,
     tools: Optional[List] = None,
@@ -1116,10 +1122,12 @@ def completion(  # type: ignore # noqa: PLR0915
         )
 
         if provider_specific_header is not None:
-            headers.update(ProviderSpecificHeaderUtils.get_provider_specific_headers(
-                provider_specific_header=provider_specific_header,
-                custom_llm_provider=custom_llm_provider,
-            ))
+            headers.update(
+                ProviderSpecificHeaderUtils.get_provider_specific_headers(
+                    provider_specific_header=provider_specific_header,
+                    custom_llm_provider=custom_llm_provider,
+                )
+            )
 
         if model_response is not None and hasattr(model_response, "_hidden_params"):
             model_response._hidden_params["custom_llm_provider"] = custom_llm_provider
@@ -2712,9 +2720,7 @@ def completion(  # type: ignore # noqa: PLR0915
             )
 
             api_key = (
-                api_key
-                or litellm.api_key
-                or get_secret("VERCEL_AI_GATEWAY_API_KEY")
+                api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY")
             )
 
             vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai"
@@ -2730,7 +2736,7 @@ def completion(  # type: ignore # noqa: PLR0915
                 vercel_headers.update(_headers)
 
             headers = vercel_headers
-            
+
             ## Load Config
             config = litellm.VercelAIGatewayConfig.get_config()
             for k, v in config.items():
@@ -3712,7 +3718,9 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse:
         func_with_context = partial(ctx.run, func)
 
         _, custom_llm_provider, _, _ = get_llm_provider(
-            model=model, custom_llm_provider=custom_llm_provider, api_base=kwargs.get("api_base", None)
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            api_base=kwargs.get("api_base", None),
         )
 
         # Await normally
@@ -5338,9 +5346,9 @@ def transcription(
             max_retries=max_retries,
             litellm_params=litellm_params_dict,
         )
-    elif (
-        custom_llm_provider == "openai"
-        or custom_llm_provider in litellm.openai_compatible_providers
+    elif custom_llm_provider == "openai" or (
+        custom_llm_provider in litellm.openai_compatible_providers
+        and provider_config is None
     ):
         api_base = (
             api_base
@@ -5371,10 +5379,7 @@ def transcription(
             provider_config=provider_config,
             litellm_params=litellm_params_dict,
         )
-    elif custom_llm_provider in [
-        LlmProviders.DEEPGRAM.value,
-        LlmProviders.ELEVENLABS.value,
-    ]:
+    elif provider_config is not None:
         response = base_llm_http_handler.audio_transcriptions(
             model=model,
             audio_file=file,
@@ -5780,7 +5785,14 @@ async def ahealth_check(
                 input=input or ["test"],
             ),
             "audio_speech": lambda: litellm.aspeech(
-                **{**_filter_model_params(model_params), **({"voice": "alloy"} if "voice" not in _filter_model_params(model_params) else {})},
+                **{
+                    **_filter_model_params(model_params),
+                    **(
+                        {"voice": "alloy"}
+                        if "voice" not in _filter_model_params(model_params)
+                        else {}
+                    ),
+                },
                 input=prompt or "test",
             ),
             "audio_transcription": lambda: litellm.atranscription(
diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml
index c785dd05c4..e93902039b 100644
--- a/litellm/proxy/_new_secret_config.yaml
+++ b/litellm/proxy/_new_secret_config.yaml
@@ -7,3 +7,8 @@ model_list:
   - model_name: wildcard_models/*
     litellm_params:
       model: openai/*
+  - model_name: hosted_vllm/*
+    litellm_params:
+      model: hosted_vllm/*
+      api_base: https://webhook.site/6fbe498e-88b5-4a5f-8f07-edb9806c1937
+      api_key: fake-key
diff --git a/litellm/utils.py b/litellm/utils.py
index 0d2fe5d4d6..a20bd904d6 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -2437,7 +2437,7 @@ def get_optional_params_transcription(
         "prompt": None,
         "response_format": None,
         "temperature": None,  # openai defaults this to 0
-        "timestamp_granularities": None
+        "timestamp_granularities": None,
     }
 
     non_default_params = {
@@ -2505,7 +2505,7 @@ def _map_openai_size_to_vertex_ai_aspect_ratio(size: Optional[str]) -> str:
     """Map OpenAI size parameter to Vertex AI aspectRatio."""
     if size is None:
         return "1:1"
-    
+
     # Map OpenAI size strings to Vertex AI aspect ratio strings
     # Vertex AI accepts: "1:1", "9:16", "16:9", "4:3", "3:4"
     size_to_aspect_ratio = {
@@ -2515,7 +2515,9 @@ def _map_openai_size_to_vertex_ai_aspect_ratio(size: Optional[str]) -> str:
         "1792x1024": "16:9",  # Landscape
         "1024x1792": "9:16",  # Portrait
     }
-    return size_to_aspect_ratio.get(size, "1:1")  # Default to square if size not recognized
+    return size_to_aspect_ratio.get(
+        size, "1:1"
+    )  # Default to square if size not recognized
 
 
 def get_optional_params_image_gen(
@@ -2631,7 +2633,9 @@ def get_optional_params_image_gen(
 
         # Map OpenAI size parameter to Vertex AI aspectRatio
         if size is not None:
-            optional_params["aspectRatio"] = _map_openai_size_to_vertex_ai_aspect_ratio(size)
+            optional_params["aspectRatio"] = _map_openai_size_to_vertex_ai_aspect_ratio(
+                size
+            )
 
     openai_params: list[str] = list(default_params.keys())
     if provider_config is not None:
@@ -7209,6 +7213,12 @@ class ProviderConfigManager:
                 return litellm.OpenAIGPTAudioTranscriptionConfig()
             else:
                 return litellm.OpenAIWhisperAudioTranscriptionConfig()
+        elif litellm.LlmProviders.HOSTED_VLLM == provider:
+            from litellm.llms.hosted_vllm.transcriptions.transformation import (
+                HostedVLLMAudioTranscriptionConfig,
+            )
+
+            return HostedVLLMAudioTranscriptionConfig()
         return None
 
     @staticmethod