# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, List, Optional, Union import numpy as np from ....utils.deps import pipeline_requires_extra from ...models.multilingual_speech_recognition.result import WhisperResult from ...utils.benchmark import benchmark from ...utils.hpi import HPIConfig from ...utils.pp_option import PaddlePredictorOption from ..base import BasePipeline @benchmark.time_methods @pipeline_requires_extra("speech") class MultilingualSpeechRecognitionPipeline(BasePipeline): """Multilingual Speech Recognition Pipeline""" entities = "multilingual_speech_recognition" def __init__( self, config: Dict, device: str = None, pp_option: PaddlePredictorOption = None, use_hpip: bool = False, hpi_config: Optional[Union[Dict[str, Any], HPIConfig]] = None, ) -> None: """ Initializes the class with given configurations and options. Args: config (Dict): Configuration dictionary containing model and other parameters. device (str): The device to run the prediction on. Default is None. pp_option (PaddlePredictorOption): Options for PaddlePaddle predictor. Default is None. use_hpip (bool, optional): Whether to use the high-performance inference plugin (HPIP) by default. Defaults to False. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): The default high-performance inference configuration dictionary. Defaults to None. """ super().__init__( device=device, pp_option=pp_option, use_hpip=use_hpip, hpi_config=hpi_config ) multilingual_speech_recognition_model_config = config["SubModules"][ "MultilingualSpeechRecognition" ] self.multilingual_speech_recognition_model = self.create_model( multilingual_speech_recognition_model_config ) # only support batch size 1 multilingual_speech_recognition_model_config["batch_size"] def predict( self, input: Union[str, List[str], np.ndarray, List[np.ndarray]], **kwargs ) -> WhisperResult: """Predicts speech recognition results for the given input. Args: input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input audio or path. **kwargs: Additional keyword arguments that can be passed to the function. Returns: WhisperResult: The predicted whisper results, support str and json output. """ yield from self.multilingual_speech_recognition_model(input)