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488 lines
18 KiB
Python
488 lines
18 KiB
Python
# 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
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from math import ceil
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from typing import Final, Optional, Union, cast
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import numpy as np
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from fastapi import Request
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from vllm.config import ModelConfig
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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, ErrorResponse, RequestResponseMetadata, TranscriptionRequest,
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TranscriptionResponse, TranscriptionResponseStreamChoice,
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TranscriptionStreamResponse, UsageInfo)
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from vllm.entrypoints.openai.serving_engine import OpenAIServing
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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.outputs import RequestOutput
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from vllm.transformers_utils.processor import cached_get_processor
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from vllm.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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logger = init_logger(__name__)
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# From https://platform.openai.com/docs/guides/speech-to-text/supported-languages#supported-languages
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# TODO these configs should live somewhere with the model so we can support
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# additional ones
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ISO639_1_SUPPORTED_LANGS = {
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"af": "Afrikaans",
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"ar": "Arabic",
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"hy": "Armenian",
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"az": "Azerbaijani",
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"be": "Belarusian",
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"bs": "Bosnian",
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"bg": "Bulgarian",
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"ca": "Catalan",
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"zh": "Chinese",
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"hr": "Croatian",
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"cs": "Czech",
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"da": "Danish",
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"nl": "Dutch",
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"en": "English",
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"et": "Estonian",
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"fi": "Finnish",
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"fr": "French",
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"gl": "Galician",
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"de": "German",
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"el": "Greek",
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"he": "Hebrew",
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"hi": "Hindi",
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"hu": "Hungarian",
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"is": "Icelandic",
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"id": "Indonesian",
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"it": "Italian",
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"ja": "Japanese",
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"kn": "Kannada",
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"kk": "Kazakh",
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"ko": "Korean",
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"lv": "Latvian",
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"lt": "Lithuanian",
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"mk": "Macedonian",
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"ms": "Malay",
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"mr": "Marathi",
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"mi": "Maori",
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"ne": "Nepali",
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"no": "Norwegian",
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"fa": "Persian",
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"pl": "Polish",
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"pt": "Portuguese",
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"ro": "Romanian",
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"ru": "Russian",
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"sr": "Serbian",
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"sk": "Slovak",
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"sl": "Slovenian",
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"es": "Spanish",
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"sw": "Swahili",
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"sv": "Swedish",
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"tl": "Tagalog",
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"ta": "Tamil",
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"th": "Thai",
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"tr": "Turkish",
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"uk": "Ukrainian",
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"ur": "Urdu",
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"vi": "Vietnamese",
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"cy": "Welsh"
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}
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ISO639_1_OTHER_LANGS = {
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"lo": "Lao",
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"jw": "Javanese",
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"tk": "Turkmen",
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"yi": "Yiddish",
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"so": "Somali",
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"bn": "Bengali",
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"nn": "Norwegian Nynorsk",
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"si": "Sinhala",
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"yo": "Yoruba",
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"sa": "Sanskrit",
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"mi": "Māori",
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"fo": "Faroese", # codespell:ignore
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"mt": "Maltese",
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"tg": "Tajik",
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"mg": "Malagasy",
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"haw": "Hawaiian",
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"km": "Khmer",
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"br": "Breton",
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"ps": "Pashto",
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"ln": "Lingala",
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"la": "Latin",
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"ml": "Malayalam",
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"sq": "Albanian",
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"su": "Sundanese",
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"eu": "Basque",
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"ka": "Georgian",
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"uz": "Uzbek",
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"sn": "Shona",
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"ht": "Haitian",
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"as": "Assamese",
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"mn": "Mongolian",
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"te": "Telugu",
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"pa": "Panjabi",
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"tt": "Tatar",
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"gu": "Gujarati",
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"oc": "Occitan",
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"ha": "Hausa",
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"ba": "Bashkir",
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"my": "Burmese",
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"sd": "Sindhi",
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"am": "Amharic",
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"lb": "Luxembourgish",
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"bo": "Tibetan"
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}
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# As per https://platform.openai.com/docs/guides/speech-to-text#overview.
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# TODO configurable
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MAX_AUDIO_CLIP_FILESIZE_MB = 25
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OVERLAP_CHUNK_SECOND = 1
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MIN_ENERGY_WINDOW_SIZE = 1600 # 1600 ~ 100ms for 16000 Hz audio
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class OpenAIServingTranscription(OpenAIServing):
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def __init__(
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self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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models: OpenAIServingModels,
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*,
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request_logger: Optional[RequestLogger],
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return_tokens_as_token_ids: bool = False,
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):
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super().__init__(engine_client=engine_client,
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model_config=model_config,
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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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self.default_sampling_params = (
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self.model_config.get_diff_sampling_param())
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processor = cached_get_processor(model_config.model)
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self.max_audio_clip_s = processor.feature_extractor.chunk_length
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self.model_sr = processor.feature_extractor.sampling_rate
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self.hop_length = processor.feature_extractor.hop_length
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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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async def _preprocess_transcription(
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self,
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request: TranscriptionRequest,
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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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# TODO language should be optional and can be guessed.
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# For now we default to en. See
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/generation_whisper.py#L1520
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lang_token = f"<|{request.language}|>" if request.language else "<|en|>"
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if request.language:
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if request.language in ISO639_1_SUPPORTED_LANGS:
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pass
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elif request.language in ISO639_1_OTHER_LANGS:
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logger.warning(
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"The selected language %s has limited accuracy with"
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" reported WER>=0.5. Results may be less accurate "
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"for this choice.", request.language)
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else:
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raise ValueError(
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f"Unsupported language: {request.language}."
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"Language should be one of:" +
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f" {list(ISO639_1_SUPPORTED_LANGS.values())}" +
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f"or {list(ISO639_1_OTHER_LANGS.values())}")
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if len(audio_data) / 1024**2 > MAX_AUDIO_CLIP_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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y, sr = librosa.load(bytes_)
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duration = librosa.get_duration(y=y, sr=sr)
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chunks = [y] if duration < 30 else self._split_audio(y, sr)
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prompts = []
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for i, chunk in enumerate(chunks):
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prompt = {
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"encoder_prompt": {
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"prompt": "",
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"multi_modal_data": {
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"audio": (chunk, sr),
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},
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},
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"decoder_prompt":
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f"<|startoftranscript|>{lang_token}<|transcribe|><|notimestamps|>{request.prompt}"
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if i == 0 else ""
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}
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prompts.append(cast(PromptType, prompt))
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return prompts, duration
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# TODO (varun) : Make verbose response work !
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async def create_transcription(
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self, audio_data: bytes, request: TranscriptionRequest,
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raw_request: Request
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) -> Union[TranscriptionResponse, AsyncGenerator[str, None],
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ErrorResponse]:
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"""Transcription API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/audio/createTranscription
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for the API specification. This API mimics the OpenAI transcription API.
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"""
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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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request_id = f"trsc-{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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(
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lora_request,
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prompt_adapter_request,
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) = self._maybe_get_adapters(request)
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if lora_request:
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return self.create_error_response(
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"Currently do not support LoRA for Transcription.")
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if prompt_adapter_request:
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return self.create_error_response(
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"Currently do not support PromptAdapter for Transcription."
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)
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prompts, duration_s = await self._preprocess_transcription(
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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: Optional[list[AsyncGenerator[RequestOutput,
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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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self._log_inputs(
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request_id,
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prompts[0]['decoder_prompt'], # type: ignore
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params=sampling_params,
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lora_request=None,
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prompt_adapter_request=None)
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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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) 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 self.transcription_stream_generator(request,
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list_result_generator,
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request_id,
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request_metadata,
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duration_s)
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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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return TranscriptionResponse(text=text)
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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 transcription_stream_generator(
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self, request: TranscriptionRequest,
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list_result_generator: list[AsyncGenerator[RequestOutput, None]],
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request_id: str, request_metadata: RequestResponseMetadata,
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audio_duration_s: float) -> AsyncGenerator[str, None]:
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created_time = int(time.time())
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model_name = request.model
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chunk_object_type: Final = "transcription.chunk"
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completion_tokens = 0
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num_prompt_tokens = 0
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include_usage = request.stream_include_usage \
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if request.stream_include_usage else False
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include_continuous_usage = 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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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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# Do not account the 4-tokens `<|startoftranscript|>..`
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# Could be negative when language token
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# is not specified.
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num_prompt_tokens = max(
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len(res.prompt_token_ids) - 4, 0)
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# NOTE(NickLucche) user can't pass encoder
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# prompts directly at least not to Whisper.
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# One indicator of the encoder amount of processing
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# is the log-mel spectogram length.
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num_prompt_tokens += ceil(
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audio_duration_s * self.model_sr / self.hop_length)
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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 = TranscriptionResponseStreamChoice(
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delta=delta_message)
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else:
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# Model is finished generating.
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choice_data = TranscriptionResponseStreamChoice(
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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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chunk = TranscriptionStreamResponse(
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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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# 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(prompt_tokens=num_prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=num_prompt_tokens +
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completion_tokens)
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final_usage_chunk = TranscriptionStreamResponse(
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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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final_usage_data = (final_usage_chunk.model_dump_json(
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exclude_unset=True, exclude_none=True))
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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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except Exception as e:
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# TODO: Use a vllm-specific Validation Error
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logger.exception("Error in chat completion stream generator.")
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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(self, audio_data: np.ndarray,
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sample_rate: int) -> list[np.ndarray]:
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chunk_size = sample_rate * self.max_audio_clip_s
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overlap_size = sample_rate * 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,
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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,
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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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for i in range(0,
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len(segment) - MIN_ENERGY_WINDOW_SIZE,
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MIN_ENERGY_WINDOW_SIZE):
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window = segment[i:i + MIN_ENERGY_WINDOW_SIZE]
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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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