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405
entrypoints/openai/speech_to_text.py
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405
entrypoints/openai/speech_to_text.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import io
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import math
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import time
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from collections.abc import AsyncGenerator, Callable
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from functools import cached_property
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from typing import Literal, TypeAlias, TypeVar, cast
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import numpy as np
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from fastapi import Request
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import vllm.envs as envs
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (
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DeltaMessage,
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ErrorResponse,
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RequestResponseMetadata,
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TranscriptionResponse,
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TranscriptionResponseStreamChoice,
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TranscriptionStreamResponse,
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TranslationResponse,
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TranslationResponseStreamChoice,
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TranslationStreamResponse,
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UsageInfo,
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)
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from vllm.entrypoints.openai.serving_engine import OpenAIServing, SpeechToTextRequest
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.inputs.data import PromptType
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from vllm.logger import init_logger
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from vllm.model_executor.models import SupportsTranscription
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from vllm.outputs import RequestOutput
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from vllm.utils.import_utils import PlaceholderModule
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try:
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import librosa
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except ImportError:
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librosa = PlaceholderModule("librosa") # type: ignore[assignment]
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SpeechToTextResponse: TypeAlias = TranscriptionResponse | TranslationResponse
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T = TypeVar("T", bound=SpeechToTextResponse)
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logger = init_logger(__name__)
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class OpenAISpeechToText(OpenAIServing):
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"""Base class for speech-to-text operations like transcription and
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translation."""
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def __init__(
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self,
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engine_client: EngineClient,
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models: OpenAIServingModels,
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*,
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request_logger: RequestLogger | None,
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return_tokens_as_token_ids: bool = False,
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task_type: Literal["transcribe", "translate"] = "transcribe",
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log_error_stack: bool = False,
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enable_force_include_usage: bool = False,
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):
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super().__init__(
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engine_client=engine_client,
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models=models,
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request_logger=request_logger,
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return_tokens_as_token_ids=return_tokens_as_token_ids,
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log_error_stack=log_error_stack,
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)
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self.default_sampling_params = self.model_config.get_diff_sampling_param()
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self.task_type = task_type
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self.asr_config = self.model_cls.get_speech_to_text_config(
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self.model_config, task_type
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)
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self.enable_force_include_usage = enable_force_include_usage
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self.max_audio_filesize_mb = envs.VLLM_MAX_AUDIO_CLIP_FILESIZE_MB
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if self.default_sampling_params:
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logger.info(
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"Overwriting default completion sampling param with: %s",
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self.default_sampling_params,
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)
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@cached_property
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def model_cls(self) -> type[SupportsTranscription]:
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from vllm.model_executor.model_loader import get_model_cls
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model_cls = get_model_cls(self.model_config)
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return cast(type[SupportsTranscription], model_cls)
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async def _preprocess_speech_to_text(
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self,
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request: SpeechToTextRequest,
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audio_data: bytes,
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) -> tuple[list[PromptType], float]:
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# Validate request
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language = self.model_cls.validate_language(request.language)
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# Skip to_language validation to avoid extra logging for Whisper.
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to_language = (
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self.model_cls.validate_language(request.to_language)
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if request.to_language
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else None
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)
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if len(audio_data) / 1024**2 > self.max_audio_filesize_mb:
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raise ValueError("Maximum file size exceeded.")
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with io.BytesIO(audio_data) as bytes_:
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# NOTE resample to model SR here for efficiency. This is also a
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# pre-requisite for chunking, as it assumes Whisper SR.
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y, sr = librosa.load(bytes_, sr=self.asr_config.sample_rate)
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duration = librosa.get_duration(y=y, sr=sr)
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do_split_audio = (
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self.asr_config.allow_audio_chunking
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and duration > self.asr_config.max_audio_clip_s
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)
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chunks = [y] if not do_split_audio else self._split_audio(y, int(sr))
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prompts = []
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for chunk in chunks:
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# The model has control over the construction, as long as it
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# returns a valid PromptType.
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prompt = self.model_cls.get_generation_prompt(
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audio=chunk,
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stt_config=self.asr_config,
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model_config=self.model_config,
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language=language,
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task_type=self.task_type,
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request_prompt=request.prompt,
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to_language=to_language,
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)
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prompts.append(prompt)
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return prompts, duration
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async def _create_speech_to_text(
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self,
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audio_data: bytes,
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request: SpeechToTextRequest,
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raw_request: Request,
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response_class: type[T],
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stream_generator_method: Callable[..., AsyncGenerator[str, None]],
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) -> T | AsyncGenerator[str, None] | ErrorResponse:
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"""Base method for speech-to-text operations like transcription and
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translation."""
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error_check_ret = await self._check_model(request)
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if error_check_ret is not None:
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return error_check_ret
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# If the engine is dead, raise the engine's DEAD_ERROR.
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# This is required for the streaming case, where we return a
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# success status before we actually start generating text :).
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if self.engine_client.errored:
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raise self.engine_client.dead_error
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if request.response_format not in ["text", "json"]:
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return self.create_error_response(
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"Currently only support response_format `text` or `json`"
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)
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request_id = f"{self.task_type}-{self._base_request_id(raw_request)}"
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request_metadata = RequestResponseMetadata(request_id=request_id)
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if raw_request:
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raw_request.state.request_metadata = request_metadata
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try:
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lora_request = self._maybe_get_adapters(request)
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prompts, duration_s = await self._preprocess_speech_to_text(
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request=request,
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audio_data=audio_data,
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)
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except ValueError as e:
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logger.exception("Error in preprocessing prompt inputs")
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return self.create_error_response(str(e))
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list_result_generator: list[AsyncGenerator[RequestOutput, None]] | None = None
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try:
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# Unlike most decoder-only models, whisper generation length is not
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# constrained by the size of the input audio, which is mapped to a
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# fixed-size log-mel-spectogram.
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default_max_tokens = self.model_config.max_model_len
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sampling_params = request.to_sampling_params(
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default_max_tokens, self.default_sampling_params
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)
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self._log_inputs(
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request_id,
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# It will not display special tokens like <|startoftranscript|>
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request.prompt,
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params=sampling_params,
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lora_request=lora_request,
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)
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list_result_generator = [
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self.engine_client.generate(
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prompt,
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sampling_params,
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request_id,
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lora_request=lora_request,
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)
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for prompt in prompts
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]
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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if request.stream:
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return stream_generator_method(
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request, list_result_generator, request_id, request_metadata, duration_s
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)
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# Non-streaming response.
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try:
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assert list_result_generator is not None
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text = ""
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for result_generator in list_result_generator:
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async for op in result_generator:
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text += op.outputs[0].text
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if self.task_type == "transcribe":
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# add usage in TranscriptionResponse.
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usage = {
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"type": "duration",
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# rounded up as per openAI specs
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"seconds": int(math.ceil(duration_s)),
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}
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final_response = cast(T, response_class(text=text, usage=usage))
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else:
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# no usage in response for translation task
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final_response = cast(T, response_class(text=text)) # type: ignore[call-arg]
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return final_response
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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async def _speech_to_text_stream_generator(
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self,
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request: SpeechToTextRequest,
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list_result_generator: list[AsyncGenerator[RequestOutput, None]],
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request_id: str,
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request_metadata: RequestResponseMetadata,
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audio_duration_s: float,
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chunk_object_type: Literal["translation.chunk", "transcription.chunk"],
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response_stream_choice_class: type[TranscriptionResponseStreamChoice]
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| type[TranslationResponseStreamChoice],
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stream_response_class: type[TranscriptionStreamResponse]
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| type[TranslationStreamResponse],
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) -> AsyncGenerator[str, None]:
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created_time = int(time.time())
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model_name = request.model
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completion_tokens = 0
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num_prompt_tokens = 0
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include_usage = self.enable_force_include_usage or request.stream_include_usage
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include_continuous_usage = (
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request.stream_continuous_usage_stats
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if include_usage and request.stream_continuous_usage_stats
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else False
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)
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try:
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for result_generator in list_result_generator:
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async for res in result_generator:
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# On first result.
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if res.prompt_token_ids is not None:
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num_prompt_tokens = len(res.prompt_token_ids)
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if audio_tokens := self.model_cls.get_num_audio_tokens(
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audio_duration_s, self.asr_config, self.model_config
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):
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num_prompt_tokens += audio_tokens
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# We need to do it here, because if there are exceptions in
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# the result_generator, it needs to be sent as the FIRST
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# response (by the try...catch).
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# Just one output (n=1) supported.
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assert len(res.outputs) == 1
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output = res.outputs[0]
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delta_message = DeltaMessage(content=output.text)
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completion_tokens += len(output.token_ids)
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if output.finish_reason is None:
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# Still generating, send delta update.
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choice_data = response_stream_choice_class(delta=delta_message)
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else:
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# Model is finished generating.
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choice_data = response_stream_choice_class(
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delta=delta_message,
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finish_reason=output.finish_reason,
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stop_reason=output.stop_reason,
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)
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chunk = stream_response_class(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name,
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)
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# handle usage stats if requested & if continuous
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens + completion_tokens,
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)
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data = chunk.model_dump_json(exclude_unset=True)
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yield f"data: {data}\n\n"
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# Once the final token is handled, if stream_options.include_usage
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# is sent, send the usage.
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if include_usage:
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final_usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens + completion_tokens,
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)
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final_usage_chunk = stream_response_class(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[],
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model=model_name,
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usage=final_usage,
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)
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final_usage_data = final_usage_chunk.model_dump_json(
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exclude_unset=True, exclude_none=True
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)
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yield f"data: {final_usage_data}\n\n"
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# report to FastAPI middleware aggregate usage across all choices
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request_metadata.final_usage_info = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens + completion_tokens,
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)
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except Exception as e:
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# TODO: Use a vllm-specific Validation Error
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logger.exception("Error in %s stream generator.", self.task_type)
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data = self.create_streaming_error_response(str(e))
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yield f"data: {data}\n\n"
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# Send the final done message after all response.n are finished
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yield "data: [DONE]\n\n"
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def _split_audio(
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self, audio_data: np.ndarray, sample_rate: int
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) -> list[np.ndarray]:
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chunk_size = sample_rate * self.asr_config.max_audio_clip_s
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overlap_size = sample_rate * self.asr_config.overlap_chunk_second
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chunks = []
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i = 0
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while i < audio_data.shape[-1]:
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if i + chunk_size >= audio_data.shape[-1]:
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# handle last chunk
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chunks.append(audio_data[..., i:])
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break
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# Find the best split point in the overlap region
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search_start = i + chunk_size - overlap_size
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search_end = min(i + chunk_size, audio_data.shape[-1])
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split_point = self._find_split_point(audio_data, search_start, search_end)
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# Extract chunk up to the split point
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chunks.append(audio_data[..., i:split_point])
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i = split_point
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return chunks
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def _find_split_point(self, wav: np.ndarray, start_idx: int, end_idx: int) -> int:
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"""Find the best point to split audio by
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looking for silence or low amplitude.
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Args:
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wav: Audio tensor [1, T]
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start_idx: Start index of search region
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end_idx: End index of search region
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Returns:
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Index of best splitting point
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"""
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segment = wav[start_idx:end_idx]
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# Calculate RMS energy in small windows
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min_energy = math.inf
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quietest_idx = 0
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min_energy_window = self.asr_config.min_energy_split_window_size
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assert min_energy_window is not None
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for i in range(0, len(segment) - min_energy_window, min_energy_window):
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window = segment[i : i + min_energy_window]
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energy = (window**2).mean() ** 0.5
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if energy < min_energy:
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quietest_idx = i + start_idx
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min_energy = energy
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return quietest_idx
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