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33 changes: 32 additions & 1 deletion vllm/entrypoints/openai/api_server.py
Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,8 @@
TokenizeResponse,
TranscriptionRequest,
TranscriptionResponse,
TranslationRequest,
TranslationResponse,
UnloadLoRAAdapterRequest)
# yapf: enable
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
Expand All @@ -80,7 +82,7 @@
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)
Expand Down Expand Up @@ -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
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I tink we're missing the instantiation of openai_serving_translation



def engine_client(request: Request) -> EngineClient:
return request.app.state.engine_client

Expand Down Expand Up @@ -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
Expand Down
193 changes: 193 additions & 0 deletions vllm/entrypoints/openai/protocol.py
Original file line number Diff line number Diff line change
Expand Up @@ -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."""
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