Sync from v0.13
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559
vllm/entrypoints/openai/speech_to_text.py
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559
vllm/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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from transformers import PreTrainedTokenizerBase
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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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TranscriptionResponseVerbose,
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TranscriptionSegment,
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TranscriptionStreamResponse,
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TranslationResponse,
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TranslationResponseStreamChoice,
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TranslationResponseVerbose,
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TranslationSegment,
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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.tokenizers import get_tokenizer
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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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SpeechToTextResponseVerbose: TypeAlias = (
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TranscriptionResponseVerbose | TranslationResponseVerbose
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)
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SpeechToTextSegment: TypeAlias = TranscriptionSegment | TranslationSegment
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T = TypeVar("T", bound=SpeechToTextResponse)
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V = TypeVar("V", bound=SpeechToTextResponseVerbose)
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S = TypeVar("S", bound=SpeechToTextSegment)
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ResponseType: TypeAlias = (
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TranscriptionResponse
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| TranslationResponse
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| TranscriptionResponseVerbose
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| TranslationResponseVerbose
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)
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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.model_cls.supports_segment_timestamp:
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self.tokenizer = cast(
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PreTrainedTokenizerBase,
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get_tokenizer(
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tokenizer_name=self.model_config.tokenizer,
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tokenizer_mode=self.model_config.tokenizer_mode,
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),
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)
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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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if request.response_format == "verbose_json":
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if not isinstance(prompt, dict):
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raise ValueError(f"Expected prompt to be a dict,got {type(prompt)}")
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prompt_dict = cast(dict, prompt)
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decoder_prompt = prompt.get("decoder_prompt")
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if not isinstance(decoder_prompt, str):
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raise ValueError(
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f"Expected decoder_prompt to bestr, got {type(decoder_prompt)}"
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)
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prompt_dict["decoder_prompt"] = decoder_prompt.replace(
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"<|notimestamps|>", "<|0.00|>"
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)
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prompts.append(prompt)
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return prompts, duration
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def _get_verbose_segments(
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self,
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tokens: tuple,
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request: SpeechToTextRequest,
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segment_class: type[SpeechToTextSegment],
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start_time: float = 0,
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) -> list[SpeechToTextSegment]:
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"""
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Convert tokens to verbose segments.
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This method expects the model to produce
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timestamps as tokens (similar to Whisper).
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If the tokens do not include timestamp information,
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the segments may not be generated correctly.
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Note: Fields like avg_logprob, compression_ratio,
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and no_speech_prob are not supported
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in this implementation and will be None. See docs for details.
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"""
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BASE_OFFSET = 0.02
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init_token = self.tokenizer.encode("<|0.00|>", add_special_tokens=False)[0]
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if tokens[-1] == self.tokenizer.eos_token_id:
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tokens = tokens[:-1]
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tokens_with_start = (init_token,) + tokens
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segments: list[SpeechToTextSegment] = []
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last_timestamp_start = 0
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if tokens_with_start[-2] < init_token and tokens_with_start[-1] >= init_token:
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tokens_with_start = tokens_with_start + (tokens_with_start[-1],)
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for idx, token in enumerate(tokens_with_start):
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# Timestamp tokens (e.g., <|0.00|>) are assumed to be sorted.
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# If the ordering is violated, this slicing may produce incorrect results.
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if (
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token >= init_token
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and idx != 0
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and tokens_with_start[idx - 1] >= init_token
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):
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sliced_timestamp_tokens = tokens_with_start[last_timestamp_start:idx]
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start_timestamp = sliced_timestamp_tokens[0] - init_token
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end_timestamp = sliced_timestamp_tokens[-1] - init_token
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casting_segment = cast(
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SpeechToTextSegment,
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segment_class(
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id=len(segments),
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seek=start_time,
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start=start_time + BASE_OFFSET * start_timestamp,
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end=start_time + BASE_OFFSET * end_timestamp,
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temperature=request.temperature,
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text=self.tokenizer.decode(sliced_timestamp_tokens[1:-1]),
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tokens=sliced_timestamp_tokens[1:-1],
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),
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)
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segments.append(casting_segment)
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last_timestamp_start = idx
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return segments
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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 | V],
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stream_generator_method: Callable[..., AsyncGenerator[str, None]],
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) -> T | V | 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", "verbose_json"]:
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return self.create_error_response(
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("Currently only support response_format")
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+ ("`text`, `json` or `verbose_json`")
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)
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if (
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request.response_format == "verbose_json"
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and not self.model_cls.supports_segment_timestamp
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):
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return self.create_error_response(
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f"Currently do not support verbose_json for {request.model}"
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)
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if request.response_format == "verbose_json" and request.stream:
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return self.create_error_response(
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"verbose_json format doesn't support streaming case"
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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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f"{request_id}_{i}",
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lora_request=lora_request,
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)
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for i, prompt in enumerate(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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total_segments = []
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text_parts = []
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try:
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assert list_result_generator is not None
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segments_types: dict[str, type[SpeechToTextSegment]] = {
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"transcribe": TranscriptionSegment,
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"translate": TranslationSegment,
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}
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segment_class: type[SpeechToTextSegment] = segments_types[self.task_type]
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text = ""
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for idx, result_generator in enumerate(list_result_generator):
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async for op in result_generator:
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if request.response_format == "verbose_json":
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segments: list[SpeechToTextSegment] = (
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self._get_verbose_segments(
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tokens=tuple(op.outputs[0].token_ids),
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segment_class=segment_class,
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request=request,
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start_time=idx * self.asr_config.max_audio_clip_s,
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)
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)
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total_segments.extend(segments)
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text_parts.extend([seg.text for seg in segments])
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else:
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text_parts.append(op.outputs[0].text)
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text = "".join(text_parts)
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if self.task_type == "transcribe":
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final_response: ResponseType
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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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if request.response_format != "verbose_json":
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final_response = cast(
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T, TranscriptionResponse(text=text, usage=usage)
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)
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else:
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final_response = cast(
|
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V,
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TranscriptionResponseVerbose(
|
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text=text,
|
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language=request.language,
|
||||
duration=str(duration_s),
|
||||
segments=total_segments,
|
||||
),
|
||||
)
|
||||
else:
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# no usage in response for translation task
|
||||
if request.response_format != "verbose_json":
|
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final_response = cast(T, TranslationResponse(text=text))
|
||||
else:
|
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final_response = cast(
|
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V,
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TranslationResponseVerbose(
|
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text=text,
|
||||
language=request.language,
|
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duration=str(duration_s),
|
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segments=total_segments,
|
||||
),
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)
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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")
|
||||
except ValueError as e:
|
||||
# TODO: Use a vllm-specific Validation Error
|
||||
return self.create_error_response(str(e))
|
||||
|
||||
async def _speech_to_text_stream_generator(
|
||||
self,
|
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request: SpeechToTextRequest,
|
||||
list_result_generator: list[AsyncGenerator[RequestOutput, None]],
|
||||
request_id: str,
|
||||
request_metadata: RequestResponseMetadata,
|
||||
audio_duration_s: float,
|
||||
chunk_object_type: Literal["translation.chunk", "transcription.chunk"],
|
||||
response_stream_choice_class: type[TranscriptionResponseStreamChoice]
|
||||
| type[TranslationResponseStreamChoice],
|
||||
stream_response_class: type[TranscriptionStreamResponse]
|
||||
| type[TranslationStreamResponse],
|
||||
) -> AsyncGenerator[str, None]:
|
||||
created_time = int(time.time())
|
||||
model_name = request.model
|
||||
|
||||
completion_tokens = 0
|
||||
num_prompt_tokens = 0
|
||||
|
||||
include_usage = self.enable_force_include_usage or request.stream_include_usage
|
||||
include_continuous_usage = (
|
||||
request.stream_continuous_usage_stats
|
||||
if include_usage and request.stream_continuous_usage_stats
|
||||
else False
|
||||
)
|
||||
|
||||
try:
|
||||
for result_generator in list_result_generator:
|
||||
async for res in result_generator:
|
||||
# On first result.
|
||||
if res.prompt_token_ids is not None:
|
||||
num_prompt_tokens = len(res.prompt_token_ids)
|
||||
if audio_tokens := self.model_cls.get_num_audio_tokens(
|
||||
audio_duration_s, self.asr_config, self.model_config
|
||||
):
|
||||
num_prompt_tokens += audio_tokens
|
||||
|
||||
# We need to do it here, because if there are exceptions in
|
||||
# the result_generator, it needs to be sent as the FIRST
|
||||
# response (by the try...catch).
|
||||
|
||||
# Just one output (n=1) supported.
|
||||
assert len(res.outputs) == 1
|
||||
output = res.outputs[0]
|
||||
|
||||
delta_message = DeltaMessage(content=output.text)
|
||||
completion_tokens += len(output.token_ids)
|
||||
|
||||
if output.finish_reason is None:
|
||||
# Still generating, send delta update.
|
||||
choice_data = response_stream_choice_class(delta=delta_message)
|
||||
else:
|
||||
# Model is finished generating.
|
||||
choice_data = response_stream_choice_class(
|
||||
delta=delta_message,
|
||||
finish_reason=output.finish_reason,
|
||||
stop_reason=output.stop_reason,
|
||||
)
|
||||
|
||||
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:
|
||||
chunk.usage = UsageInfo(
|
||||
prompt_tokens=num_prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=num_prompt_tokens + completion_tokens,
|
||||
)
|
||||
|
||||
data = chunk.model_dump_json(exclude_unset=True)
|
||||
yield f"data: {data}\n\n"
|
||||
|
||||
# Once the final token is handled, if stream_options.include_usage
|
||||
# is sent, send the usage.
|
||||
if include_usage:
|
||||
final_usage = UsageInfo(
|
||||
prompt_tokens=num_prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=num_prompt_tokens + completion_tokens,
|
||||
)
|
||||
|
||||
final_usage_chunk = stream_response_class(
|
||||
id=request_id,
|
||||
object=chunk_object_type,
|
||||
created=created_time,
|
||||
choices=[],
|
||||
model=model_name,
|
||||
usage=final_usage,
|
||||
)
|
||||
final_usage_data = final_usage_chunk.model_dump_json(
|
||||
exclude_unset=True, exclude_none=True
|
||||
)
|
||||
yield f"data: {final_usage_data}\n\n"
|
||||
|
||||
# report to FastAPI middleware aggregate usage across all choices
|
||||
request_metadata.final_usage_info = UsageInfo(
|
||||
prompt_tokens=num_prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=num_prompt_tokens + completion_tokens,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
# TODO: Use a vllm-specific Validation Error
|
||||
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"
|
||||
|
||||
def _split_audio(
|
||||
self, audio_data: np.ndarray, sample_rate: int
|
||||
) -> list[np.ndarray]:
|
||||
chunk_size = sample_rate * self.asr_config.max_audio_clip_s
|
||||
overlap_size = sample_rate * self.asr_config.overlap_chunk_second
|
||||
chunks = []
|
||||
i = 0
|
||||
while i < audio_data.shape[-1]:
|
||||
if i + chunk_size >= audio_data.shape[-1]:
|
||||
# handle last chunk
|
||||
chunks.append(audio_data[..., i:])
|
||||
break
|
||||
|
||||
# Find the best split point in the overlap region
|
||||
search_start = i + chunk_size - overlap_size
|
||||
search_end = min(i + chunk_size, audio_data.shape[-1])
|
||||
split_point = self._find_split_point(audio_data, search_start, search_end)
|
||||
|
||||
# Extract chunk up to the split point
|
||||
chunks.append(audio_data[..., i:split_point])
|
||||
i = split_point
|
||||
return chunks
|
||||
|
||||
def _find_split_point(self, wav: np.ndarray, start_idx: int, end_idx: int) -> int:
|
||||
"""Find the best point to split audio by
|
||||
looking for silence or low amplitude.
|
||||
Args:
|
||||
wav: Audio tensor [1, T]
|
||||
start_idx: Start index of search region
|
||||
end_idx: End index of search region
|
||||
Returns:
|
||||
Index of best splitting point
|
||||
"""
|
||||
segment = wav[start_idx:end_idx]
|
||||
|
||||
# Calculate RMS energy in small windows
|
||||
min_energy = math.inf
|
||||
quietest_idx = 0
|
||||
min_energy_window = self.asr_config.min_energy_split_window_size
|
||||
assert min_energy_window is not None
|
||||
for i in range(0, len(segment) - min_energy_window, min_energy_window):
|
||||
window = segment[i : i + min_energy_window]
|
||||
energy = (window**2).mean() ** 0.5
|
||||
if energy < min_energy:
|
||||
quietest_idx = i + start_idx
|
||||
min_energy = energy
|
||||
return quietest_idx
|
||||
Reference in New Issue
Block a user