initial

Signed-off-by: Roger Wang <ywang@roblox.com>

vllm/entrypoints/openai/api_server.py

@@ -67,6 +67,8 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               TokenizeResponse,
                                               TranscriptionRequest,
                                               TranscriptionResponse,
+                                              TranslationRequest,
+                                              TranslationResponse,
                                               UnloadLoRAAdapterRequest)
 # yapf: enable
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
@@ -80,7 +82,7 @@ from vllm.entrypoints.openai.serving_score import ServingScores
 from vllm.entrypoints.openai.serving_tokenization import (
     OpenAIServingTokenization)
 from vllm.entrypoints.openai.serving_transcription import (
-    OpenAIServingTranscription)
+    OpenAIServingTranscription, OpenAIServingTranslation)
 from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
                                     with_cancellation)
@@ -383,6 +385,10 @@ def transcription(request: Request) -> OpenAIServingTranscription:
     return request.app.state.openai_serving_transcription
 
 
+def translation(request: Request) -> OpenAIServingTranslation:
+    return request.app.state.openai_serving_translation
+
+
 def engine_client(request: Request) -> EngineClient:
     return request.app.state.engine_client
 
@@ -625,6 +631,31 @@ async def create_transcriptions(request: Annotated[TranscriptionRequest,
     return StreamingResponse(content=generator, media_type="text/event-stream")
 
 
+@router.post("/v1/audio/translations")
+@with_cancellation
+@load_aware_call
+async def create_translations(request: Annotated[TranslationRequest,
+                                                 Form()],
+                              raw_request: Request):
+    handler = translation(raw_request)
+    if handler is None:
+        return base(raw_request).create_error_response(
+            message="The model does not support Translations API")
+
+    audio_data = await request.file.read()
+    generator = await handler.create_translation(audio_data, request,
+                                                 raw_request)
+
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+
+    elif isinstance(generator, TranslationResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    return StreamingResponse(content=generator, media_type="text/event-stream")
+
+
 @router.post("/rerank", dependencies=[Depends(validate_json_request)])
 @with_cancellation
 @load_aware_call

vllm/entrypoints/openai/protocol.py

@@ -1652,3 +1652,196 @@ class TranscriptionResponseVerbose(OpenAIBaseModel):
 
     words: Optional[list[TranscriptionWord]] = None
     """Extracted words and their corresponding timestamps."""
+
+
+class TranslationResponseStreamChoice(OpenAIBaseModel):
+    delta: DeltaMessage
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = None
+
+
+class TranslationStreamResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"trsl-{random_uuid()}")
+    object: Literal["translation.chunk"] = "translation.chunk"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: list[TranslationResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
+
+
+class TranslationRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/audio/createTranslation
+
+    file: UploadFile
+    """
+    The audio file object (not file name) to translate, in one of these
+    formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
+    """
+
+    model: Optional[str] = None
+    """ID of the model to use.
+    """
+
+    language: Optional[str] = None
+    """The language of the input audio.
+
+    Supplying the input language in
+    [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format
+    will improve accuracy and latency.
+    """
+
+    prompt: str = Field(default="")
+    """An optional text to guide the model's style or continue a previous audio
+    segment.
+
+    The [prompt](https://platform.openai.com/docs/guides/speech-to-text#prompting)
+    should match the audio language.
+    """
+
+    response_format: AudioResponseFormat = Field(default="json")
+    """
+    The format of the output, in one of these options: `json`, `text`, `srt`,
+    `verbose_json`, or `vtt`.
+    """
+
+    ## TODO (varun) : Support if set to 0, certain thresholds are met !!
+    temperature: float = Field(default=0.0)
+    """The sampling temperature, between 0 and 1.
+
+    Higher values like 0.8 will make the output more random, while lower values
+    like 0.2 will make it more focused / deterministic. If set to 0, the model
+    will use [log probability](https://en.wikipedia.org/wiki/Log_probability)
+    to automatically increase the temperature until certain thresholds are hit.
+    """
+
+    timestamp_granularities: list[Literal["word", "segment"]] = Field(
+        alias="timestamp_granularities[]", default=[])
+    """The timestamp granularities to populate for this translation.
+
+    `response_format` must be set `verbose_json` to use timestamp granularities.
+    Either or both of these options are supported: `word`, or `segment`. Note:
+    There is no additional latency for segment timestamps, but generating word
+    timestamps incurs additional latency.
+    """
+
+    stream: Optional[bool] = False
+    """Custom field not present in the original OpenAI definition. When set, 
+    it will enable output to be streamed in a similar fashion as the Chat
+    Completion endpoint. 
+    """
+    # Flattened stream option to simplify form data.
+    stream_include_usage: Optional[bool] = False
+    stream_continuous_usage_stats: Optional[bool] = False
+
+    # Default sampling parameters for translation requests.
+    _DEFAULT_SAMPLING_PARAMS: dict = {
+        "temperature": 0,
+    }
+
+    def to_sampling_params(
+            self,
+            default_max_tokens: int,
+            default_sampling_params: Optional[dict] = None) -> SamplingParams:
+        # TODO(#9845): remove max_tokens when field is removed from OpenAI API
+        max_tokens = default_max_tokens
+
+        if default_sampling_params is None:
+            default_sampling_params = {}
+        # Default parameters
+        if (temperature := self.temperature) is None:
+            temperature = default_sampling_params.get(
+                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
+
+        return SamplingParams.from_optional(temperature=temperature,
+                                            max_tokens=max_tokens,
+                                            output_kind=RequestOutputKind.DELTA
+                                            if self.stream \
+                                            else RequestOutputKind.FINAL_ONLY)
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_stream_options(cls, data):
+        stream_opts = ["stream_include_usage", "stream_continuous_usage_stats"]
+        stream = data.get("stream", False)
+        if any(bool(data.get(so, False)) for so in stream_opts) and not stream:
+            raise ValueError(
+                "Stream options can only be defined when `stream=True`.")
+
+        return data
+
+
+# Translation response objects
+class TranslationResponse(OpenAIBaseModel):
+    text: str
+    """The translated text."""
+
+
+class TranslationWord(OpenAIBaseModel):
+    end: float
+    """End time of the word in seconds."""
+
+    start: float
+    """Start time of the word in seconds."""
+
+    word: str
+    """The text content of the word."""
+
+
+class TranslationSegment(OpenAIBaseModel):
+    id: int
+    """Unique identifier of the segment."""
+
+    avg_logprob: float
+    """Average logprob of the segment.
+
+    If the value is lower than -1, consider the logprobs failed.
+    """
+
+    compression_ratio: float
+    """Compression ratio of the segment.
+
+    If the value is greater than 2.4, consider the compression failed.
+    """
+
+    end: float
+    """End time of the segment in seconds."""
+
+    no_speech_prob: float
+    """Probability of no speech in the segment.
+
+    If the value is higher than 1.0 and the `avg_logprob` is below -1, consider
+    this segment silent.
+    """
+
+    seek: int
+    """Seek offset of the segment."""
+
+    start: float
+    """Start time of the segment in seconds."""
+
+    temperature: float
+    """Temperature parameter used for generating the segment."""
+
+    text: str
+    """Text content of the segment."""
+
+    tokens: list[int]
+    """Array of token IDs for the text content."""
+
+
+class TranslationResponseVerbose(OpenAIBaseModel):
+    duration: str
+    """The duration of the input audio."""
+
+    language: str
+    """The language of the input audio."""
+
+    text: str
+    """The translated text."""
+
+    segments: Optional[list[TranslationSegment]] = None
+    """Segments of the translated text and their corresponding details."""
+
+    words: Optional[list[TranslationWord]] = None
+    """Extracted words and their corresponding timestamps."""

vllm/entrypoints/openai/serving_transcription.py

@@ -4,7 +4,7 @@ import io
 import time
 from collections.abc import AsyncGenerator
 from math import ceil
-from typing import Final, Optional, Union, cast
+from typing import Callable, Optional, Union, cast
 
 from fastapi import Request
 
@@ -14,7 +14,8 @@ from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.protocol import (
     DeltaMessage, ErrorResponse, RequestResponseMetadata, TranscriptionRequest,
     TranscriptionResponse, TranscriptionResponseStreamChoice,
-    TranscriptionStreamResponse, UsageInfo)
+    TranscriptionStreamResponse, TranslationRequest, TranslationResponse,
+    TranslationResponseStreamChoice, TranslationStreamResponse, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import OpenAIServing
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.inputs.data import PromptType
@@ -30,7 +31,7 @@ except ImportError:
 
 logger = init_logger(__name__)
 
-# From https://platform.openai.com/docs/guides/speech-to-text/supported-languages#supported-languages
+# From https://platform.openai.com/docs/guides/speech-to-text/supported-languages
 # TODO these configs should live somewhere with the model so we can support
 # additional ones
 
@@ -144,16 +145,19 @@ ISO639_1_OTHER_LANGS = {
 MAX_AUDIO_CLIP_FILESIZE_MB = 25
 
 
-class OpenAIServingTranscription(OpenAIServing):
+class OpenAISpeechToText(OpenAIServing):
+    """Base class for speech-to-text operations like transcription and 
+    translation."""
 
     def __init__(
-        self,
-        engine_client: EngineClient,
-        model_config: ModelConfig,
-        models: OpenAIServingModels,
-        *,
-        request_logger: Optional[RequestLogger],
-        return_tokens_as_token_ids: bool = False,
+            self,
+            engine_client: EngineClient,
+            model_config: ModelConfig,
+            models: OpenAIServingModels,
+            *,
+            request_logger: Optional[RequestLogger],
+            return_tokens_as_token_ids: bool = False,
+            task_type: str = "transcribe",  # or "translate"
     ):
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
@@ -167,15 +171,16 @@ class OpenAIServingTranscription(OpenAIServing):
         self.max_audio_clip_s = processor.feature_extractor.chunk_length
         self.model_sr = processor.feature_extractor.sampling_rate
         self.hop_length = processor.feature_extractor.hop_length
+        self.task_type = task_type
 
         if self.default_sampling_params:
             logger.info(
                 "Overwriting default completion sampling param with: %s",
                 self.default_sampling_params)
 
-    async def _preprocess_transcription(
+    async def _preprocess_speech_to_text(
         self,
-        request: TranscriptionRequest,
+        request: Union[TranscriptionRequest, TranslationRequest],
         audio_data: bytes,
     ) -> tuple[PromptType, float]:
         # Validate request
@@ -218,21 +223,22 @@ class OpenAIServingTranscription(OpenAIServing):
                 },
             },
             "decoder_prompt":
-            f"<|startoftranscript|>{lang_token}<|transcribe|><|notimestamps|>{request.prompt}"
+            (f"<|startoftranscript|>{lang_token}"
+             f"<|{self.task_type}|><|notimestamps|>{request.prompt}")
         }
         return cast(PromptType, prompt), duration
 
-    # TODO (varun) : Make verbose response work !
-    async def create_transcription(
-        self, audio_data: bytes, request: TranscriptionRequest,
-        raw_request: Request
-    ) -> Union[TranscriptionResponse, AsyncGenerator[str, None],
-               ErrorResponse]:
-        """Transcription API similar to OpenAI's API.
-
-        See https://platform.openai.com/docs/api-reference/audio/createTranscription
-        for the API specification. This API mimics the OpenAI transcription API.
-        """
+    async def _create_speech_to_text(
+        self,
+        audio_data: bytes,
+        request: Union[TranscriptionRequest, TranslationRequest],
+        raw_request: Request,
+        response_class: Union[TranscriptionResponse, TranslationResponse],
+        stream_generator_method: Callable,
+    ) -> Union[Union[TranscriptionResponse, TranslationResponse],
+               AsyncGenerator[str, None], ErrorResponse]:
+        """Base method for speech-to-text operations like transcription and 
+        translation."""
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
             return error_check_ret
@@ -247,7 +253,7 @@ class OpenAIServingTranscription(OpenAIServing):
             return self.create_error_response(
                 "Currently only support response_format `text` or `json`")
 
-        request_id = f"trsc-{self._base_request_id(raw_request)}"
+        request_id = f"{self.task_type}-{self._base_request_id(raw_request)}"
 
         request_metadata = RequestResponseMetadata(request_id=request_id)
         if raw_request:
@@ -261,13 +267,14 @@ class OpenAIServingTranscription(OpenAIServing):
 
             if lora_request:
                 return self.create_error_response(
-                    "Currently do not support LoRA for Transcription.")
+                    "Currently do not support LoRA for "
+                    f"{self.task_type.title()}.")
             if prompt_adapter_request:
                 return self.create_error_response(
-                    "Currently do not support PromptAdapter for Transcription."
-                )
+                    f"Currently do not support PromptAdapter for "
+                    f"{self.task_type.title()}.")
 
-            prompt, duration_s = await self._preprocess_transcription(
+            prompt, duration_s = await self._preprocess_speech_to_text(
                 request=request,
                 audio_data=audio_data,
             )
@@ -300,31 +307,36 @@ class OpenAIServingTranscription(OpenAIServing):
             return self.create_error_response(str(e))
 
         if request.stream:
-            return self.transcription_stream_generator(request,
-                                                       result_generator,
-                                                       request_id,
-                                                       request_metadata,
-                                                       duration_s)
+            return stream_generator_method(request, result_generator,
+                                           request_id, request_metadata,
+                                           duration_s)
         # Non-streaming response.
         try:
             assert result_generator is not None
             async for op in result_generator:
                 result = op
-            return TranscriptionResponse(text=result.outputs[0].text)
+            return response_class(text=result.outputs[0].text)
         except asyncio.CancelledError:
             return self.create_error_response("Client disconnected")
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             return self.create_error_response(str(e))
 
-    async def transcription_stream_generator(
-            self, request: TranscriptionRequest,
-            result_generator: AsyncGenerator[RequestOutput, None],
-            request_id: str, request_metadata: RequestResponseMetadata,
-            audio_duration_s: float) -> AsyncGenerator[str, None]:
+    async def _speech_to_text_stream_generator(
+        self,
+        request: Union[TranscriptionRequest, TranslationRequest],
+        result_generator: AsyncGenerator[RequestOutput, None],
+        request_id: str,
+        request_metadata: RequestResponseMetadata,
+        audio_duration_s: float,
+        chunk_object_type: str,
+        response_stream_choice_class: Union[TranscriptionResponseStreamChoice,
+                                            TranslationResponseStreamChoice],
+        stream_response_class: Union[TranscriptionStreamResponse,
+                                     TranslationStreamResponse],
+    ) -> AsyncGenerator[str, None]:
         created_time = int(time.time())
         model_name = request.model
-        chunk_object_type: Final = "transcription.chunk"
 
         completion_tokens = 0
         num_prompt_tokens = 0
@@ -361,20 +373,20 @@ class OpenAIServingTranscription(OpenAIServing):
 
                 if output.finish_reason is None:
                     # Still generating, send delta update.
-                    choice_data = TranscriptionResponseStreamChoice(
+                    choice_data = response_stream_choice_class(
                         delta=delta_message)
                 else:
                     # Model is finished generating.
-                    choice_data = TranscriptionResponseStreamChoice(
+                    choice_data = response_stream_choice_class(
                         delta=delta_message,
                         finish_reason=output.finish_reason,
                         stop_reason=output.stop_reason)
 
-                chunk = TranscriptionStreamResponse(id=request_id,
-                                                    object=chunk_object_type,
-                                                    created=created_time,
-                                                    choices=[choice_data],
-                                                    model=model_name)
+                chunk = stream_response_class(id=request_id,
+                                              object=chunk_object_type,
+                                              created=created_time,
+                                              choices=[choice_data],
+                                              model=model_name)
 
                 # handle usage stats if requested & if continuous
                 if include_continuous_usage:
@@ -395,7 +407,7 @@ class OpenAIServingTranscription(OpenAIServing):
                                         total_tokens=num_prompt_tokens +
                                         completion_tokens)
 
-                final_usage_chunk = TranscriptionStreamResponse(
+                final_usage_chunk = stream_response_class(
                     id=request_id,
                     object=chunk_object_type,
                     created=created_time,
@@ -414,8 +426,115 @@ class OpenAIServingTranscription(OpenAIServing):
 
         except Exception as e:
             # TODO: Use a vllm-specific Validation Error
-            logger.exception("Error in chat completion stream generator.")
+            logger.exception("Error in %s stream generator.", self.task_type)
             data = self.create_streaming_error_response(str(e))
             yield f"data: {data}\n\n"
         # Send the final done message after all response.n are finished
         yield "data: [DONE]\n\n"
+
+
+class OpenAIServingTranscription(OpenAISpeechToText):
+    """Handles transcription requests."""
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+        return_tokens_as_token_ids: bool = False,
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids,
+                         task_type="transcribe")
+
+    async def create_transcription(
+        self, audio_data: bytes, request: TranscriptionRequest,
+        raw_request: Request
+    ) -> Union[TranscriptionResponse, AsyncGenerator[str, None],
+               ErrorResponse]:
+        """Transcription API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/audio/createTranscription
+        for the API specification. This API mimics the OpenAI transcription API.
+        """
+        return await self._create_speech_to_text(
+            audio_data=audio_data,
+            request=request,
+            raw_request=raw_request,
+            response_class=TranscriptionResponse,
+            stream_generator_method=self.transcription_stream_generator,
+        )
+
+    async def transcription_stream_generator(
+            self, request: TranscriptionRequest,
+            result_generator: AsyncGenerator[RequestOutput, None],
+            request_id: str, request_metadata: RequestResponseMetadata,
+            audio_duration_s: float) -> AsyncGenerator[str, None]:
+        return await self._speech_to_text_stream_generator(
+            request=request,
+            result_generator=result_generator,
+            request_id=request_id,
+            request_metadata=request_metadata,
+            audio_duration_s=audio_duration_s,
+            chunk_object_type="transcription.chunk",
+            response_stream_choice_class=TranscriptionResponseStreamChoice,
+            stream_response_class=TranscriptionStreamResponse,
+        )
+
+
+class OpenAIServingTranslation(OpenAISpeechToText):
+    """Handles translation requests."""
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+        return_tokens_as_token_ids: bool = False,
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids,
+                         task_type="translate")
+
+    async def create_translation(
+        self, audio_data: bytes, request: TranslationRequest,
+        raw_request: Request
+    ) -> Union[TranslationResponse, AsyncGenerator[str, None], ErrorResponse]:
+        """Translation API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/audio/createTranslation
+        for the API specification. This API mimics the OpenAI translation API.
+        """
+        return await self._create_speech_to_text(
+            audio_data=audio_data,
+            request=request,
+            raw_request=raw_request,
+            response_class=TranslationResponse,
+            stream_generator_method=self.translation_stream_generator,
+        )
+
+    async def translation_stream_generator(
+            self, request: TranslationRequest,
+            result_generator: AsyncGenerator[RequestOutput, None],
+            request_id: str, request_metadata: RequestResponseMetadata,
+            audio_duration_s: float) -> AsyncGenerator[str, None]:
+        return await self._speech_to_text_stream_generator(
+            request=request,
+            result_generator=result_generator,
+            request_id=request_id,
+            request_metadata=request_metadata,
+            audio_duration_s=audio_duration_s,
+            chunk_object_type="translation.chunk",
+            response_stream_choice_class=TranslationResponseStreamChoice,
+            stream_response_class=TranslationStreamResponse,
+        )