# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import asyncio import io import math import time from collections.abc import AsyncGenerator from math import ceil from typing import Final, Optional, Union, cast import numpy as np from fastapi import Request from vllm.config import ModelConfig from vllm.engine.protocol import EngineClient from vllm.entrypoints.logger import RequestLogger from vllm.entrypoints.openai.protocol import ( DeltaMessage, ErrorResponse, RequestResponseMetadata, TranscriptionRequest, TranscriptionResponse, TranscriptionResponseStreamChoice, TranscriptionStreamResponse, UsageInfo) from vllm.entrypoints.openai.serving_engine import OpenAIServing from vllm.entrypoints.openai.serving_models import OpenAIServingModels from vllm.inputs.data import PromptType from vllm.logger import init_logger from vllm.outputs import RequestOutput from vllm.transformers_utils.processor import cached_get_processor from vllm.utils import PlaceholderModule try: import librosa except ImportError: librosa = PlaceholderModule("librosa") # type: ignore[assignment] logger = init_logger(__name__) # From https://platform.openai.com/docs/guides/speech-to-text/supported-languages#supported-languages # TODO these configs should live somewhere with the model so we can support # additional ones ISO639_1_SUPPORTED_LANGS = { "af": "Afrikaans", "ar": "Arabic", "hy": "Armenian", "az": "Azerbaijani", "be": "Belarusian", "bs": "Bosnian", "bg": "Bulgarian", "ca": "Catalan", "zh": "Chinese", "hr": "Croatian", "cs": "Czech", "da": "Danish", "nl": "Dutch", "en": "English", "et": "Estonian", "fi": "Finnish", "fr": "French", "gl": "Galician", "de": "German", "el": "Greek", "he": "Hebrew", "hi": "Hindi", "hu": "Hungarian", "is": "Icelandic", "id": "Indonesian", "it": "Italian", "ja": "Japanese", "kn": "Kannada", "kk": "Kazakh", "ko": "Korean", "lv": "Latvian", "lt": "Lithuanian", "mk": "Macedonian", "ms": "Malay", "mr": "Marathi", "mi": "Maori", "ne": "Nepali", "no": "Norwegian", "fa": "Persian", "pl": "Polish", "pt": "Portuguese", "ro": "Romanian", "ru": "Russian", "sr": "Serbian", "sk": "Slovak", "sl": "Slovenian", "es": "Spanish", "sw": "Swahili", "sv": "Swedish", "tl": "Tagalog", "ta": "Tamil", "th": "Thai", "tr": "Turkish", "uk": "Ukrainian", "ur": "Urdu", "vi": "Vietnamese", "cy": "Welsh" } ISO639_1_OTHER_LANGS = { "lo": "Lao", "jw": "Javanese", "tk": "Turkmen", "yi": "Yiddish", "so": "Somali", "bn": "Bengali", "nn": "Norwegian Nynorsk", "si": "Sinhala", "yo": "Yoruba", "sa": "Sanskrit", "mi": "Māori", "fo": "Faroese", # codespell:ignore "mt": "Maltese", "tg": "Tajik", "mg": "Malagasy", "haw": "Hawaiian", "km": "Khmer", "br": "Breton", "ps": "Pashto", "ln": "Lingala", "la": "Latin", "ml": "Malayalam", "sq": "Albanian", "su": "Sundanese", "eu": "Basque", "ka": "Georgian", "uz": "Uzbek", "sn": "Shona", "ht": "Haitian", "as": "Assamese", "mn": "Mongolian", "te": "Telugu", "pa": "Panjabi", "tt": "Tatar", "gu": "Gujarati", "oc": "Occitan", "ha": "Hausa", "ba": "Bashkir", "my": "Burmese", "sd": "Sindhi", "am": "Amharic", "lb": "Luxembourgish", "bo": "Tibetan" } # As per https://platform.openai.com/docs/guides/speech-to-text#overview. # TODO configurable MAX_AUDIO_CLIP_FILESIZE_MB = 25 OVERLAP_CHUNK_SECOND = 1 MIN_ENERGY_WINDOW_SIZE = 1600 # 1600 ~ 100ms for 16000 Hz audio class OpenAIServingTranscription(OpenAIServing): def __init__( self, engine_client: EngineClient, model_config: ModelConfig, models: OpenAIServingModels, *, request_logger: Optional[RequestLogger], return_tokens_as_token_ids: bool = False, ): super().__init__(engine_client=engine_client, model_config=model_config, models=models, request_logger=request_logger, return_tokens_as_token_ids=return_tokens_as_token_ids) self.default_sampling_params = ( self.model_config.get_diff_sampling_param()) processor = cached_get_processor(model_config.model) self.max_audio_clip_s = processor.feature_extractor.chunk_length self.model_sr = processor.feature_extractor.sampling_rate self.hop_length = processor.feature_extractor.hop_length if self.default_sampling_params: logger.info( "Overwriting default completion sampling param with: %s", self.default_sampling_params) async def _preprocess_transcription( self, request: TranscriptionRequest, audio_data: bytes, ) -> tuple[list[PromptType], float]: # Validate request # TODO language should be optional and can be guessed. # For now we default to en. See # https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/generation_whisper.py#L1520 lang_token = f"<|{request.language}|>" if request.language else "<|en|>" if request.language: if request.language in ISO639_1_SUPPORTED_LANGS: pass elif request.language in ISO639_1_OTHER_LANGS: logger.warning( "The selected language %s has limited accuracy with" " reported WER>=0.5. Results may be less accurate " "for this choice.", request.language) else: raise ValueError( f"Unsupported language: {request.language}." "Language should be one of:" + f" {list(ISO639_1_SUPPORTED_LANGS.values())}" + f"or {list(ISO639_1_OTHER_LANGS.values())}") if len(audio_data) / 1024**2 > MAX_AUDIO_CLIP_FILESIZE_MB: raise ValueError("Maximum file size exceeded.") with io.BytesIO(audio_data) as bytes_: y, sr = librosa.load(bytes_) duration = librosa.get_duration(y=y, sr=sr) chunks = [y] if duration < 30 else self._split_audio(y, sr) prompts = [] for i, chunk in enumerate(chunks): prompt = { "encoder_prompt": { "prompt": "", "multi_modal_data": { "audio": (chunk, sr), }, }, "decoder_prompt": f"<|startoftranscript|>{lang_token}<|transcribe|><|notimestamps|>{request.prompt}" if i == 0 else "" } prompts.append(cast(PromptType, prompt)) return prompts, duration # TODO (varun) : Make verbose response work ! async def create_transcription( self, audio_data: bytes, request: TranscriptionRequest, raw_request: Request ) -> Union[TranscriptionResponse, AsyncGenerator[str, None], ErrorResponse]: """Transcription API similar to OpenAI's API. See https://platform.openai.com/docs/api-reference/audio/createTranscription for the API specification. This API mimics the OpenAI transcription API. """ error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret # If the engine is dead, raise the engine's DEAD_ERROR. # This is required for the streaming case, where we return a # success status before we actually start generating text :). if self.engine_client.errored: raise self.engine_client.dead_error if request.response_format not in ['text', 'json']: return self.create_error_response( "Currently only support response_format `text` or `json`") request_id = f"trsc-{self._base_request_id(raw_request)}" request_metadata = RequestResponseMetadata(request_id=request_id) if raw_request: raw_request.state.request_metadata = request_metadata try: ( lora_request, prompt_adapter_request, ) = self._maybe_get_adapters(request) if lora_request: return self.create_error_response( "Currently do not support LoRA for Transcription.") if prompt_adapter_request: return self.create_error_response( "Currently do not support PromptAdapter for Transcription." ) prompts, duration_s = await self._preprocess_transcription( request=request, audio_data=audio_data, ) except ValueError as e: logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(str(e)) list_result_generator: Optional[list[AsyncGenerator[RequestOutput, None]]] = None try: # Unlike most decoder-only models, whisper generation length is not # constrained by the size of the input audio, which is mapped to a # fixed-size log-mel-spectogram. default_max_tokens = self.model_config.max_model_len sampling_params = request.to_sampling_params( default_max_tokens, self.default_sampling_params) self._log_inputs( request_id, prompts[0]['decoder_prompt'], # type: ignore params=sampling_params, lora_request=None, prompt_adapter_request=None) list_result_generator = [ self.engine_client.generate( prompt, sampling_params, request_id, ) for prompt in prompts ] except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) if request.stream: return self.transcription_stream_generator(request, list_result_generator, request_id, request_metadata, duration_s) # Non-streaming response. try: assert list_result_generator is not None text = "" for result_generator in list_result_generator: async for op in result_generator: text += op.outputs[0].text return TranscriptionResponse(text=text) except asyncio.CancelledError: return self.create_error_response("Client disconnected") except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) async def transcription_stream_generator( self, request: TranscriptionRequest, list_result_generator: list[AsyncGenerator[RequestOutput, None]], request_id: str, request_metadata: RequestResponseMetadata, audio_duration_s: float) -> AsyncGenerator[str, None]: created_time = int(time.time()) model_name = request.model chunk_object_type: Final = "transcription.chunk" completion_tokens = 0 num_prompt_tokens = 0 include_usage = request.stream_include_usage \ if request.stream_include_usage else False 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: # Do not account the 4-tokens `<|startoftranscript|>..` # Could be negative when language token # is not specified. num_prompt_tokens = max( len(res.prompt_token_ids) - 4, 0) # NOTE(NickLucche) user can't pass encoder # prompts directly at least not to Whisper. # One indicator of the encoder amount of processing # is the log-mel spectogram length. num_prompt_tokens += ceil( audio_duration_s * self.model_sr / self.hop_length) # 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 = TranscriptionResponseStreamChoice( delta=delta_message) else: # Model is finished generating. choice_data = TranscriptionResponseStreamChoice( delta=delta_message, finish_reason=output.finish_reason, stop_reason=output.stop_reason) chunk = TranscriptionStreamResponse( id=request_id, object=chunk_object_type, created=created_time, choices=[choice_data], model=model_name) # 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 = TranscriptionStreamResponse( 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 chat completion stream generator.") 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.max_audio_clip_s overlap_size = sample_rate * 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 for i in range(0, len(segment) - MIN_ENERGY_WINDOW_SIZE, MIN_ENERGY_WINDOW_SIZE): window = segment[i:i + MIN_ENERGY_WINDOW_SIZE] energy = (window**2).mean()**0.5 if energy < min_energy: quietest_idx = i + start_idx min_energy = energy return quietest_idx