# Copyright (c) 2021 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. import argparse import io import os import sys import time from collections import OrderedDict from typing import List from typing import Optional from typing import Union import librosa import numpy as np import paddle import soundfile from yacs.config import CfgNode from ...utils.env import MODEL_HOME from ..download import get_path_from_url from ..executor import BaseExecutor from ..log import logger from ..utils import CLI_TIMER from ..utils import stats_wrapper from ..utils import timer_register from paddlespeech.audio.transform.transformation import Transformation from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.utils.utility import UpdateConfig __all__ = ['ASRExecutor'] @timer_register class ASRExecutor(BaseExecutor): def __init__(self): super().__init__(task='asr', inference_type='offline') self.parser = argparse.ArgumentParser( prog='paddlespeech.asr', add_help=True) self.parser.add_argument( '--input', type=str, default=None, help='Audio file to recognize.') self.parser.add_argument( '--model', type=str, default='conformer_u2pp_online_wenetspeech', choices=[ tag[:tag.index('-')] for tag in self.task_resource.pretrained_models.keys() ], help='Choose model type of asr task.') self.parser.add_argument( '--lang', type=str, default='zh', help='Choose model language. zh or en, zh:[conformer_wenetspeech-zh-16k], en:[transformer_librispeech-en-16k]' ) self.parser.add_argument( "--sample_rate", type=int, default=16000, choices=[8000, 16000], help='Choose the audio sample rate of the model. 8000 or 16000') self.parser.add_argument( '--config', type=str, default=None, help='Config of asr task. Use deault config when it is None.') self.parser.add_argument( '--decode_method', type=str, default='attention_rescoring', choices=[ 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention', 'attention_rescoring' ], help='only support transformer and conformer model') self.parser.add_argument( '--num_decoding_left_chunks', '-num_left', type=str, default=-1, help='only support transformer and conformer online model') self.parser.add_argument( '--ckpt_path', type=str, default=None, help='Checkpoint file of model.') self.parser.add_argument( '--yes', '-y', action="store_true", default=False, help='No additional parameters required. \ Once set this parameter, it means accepting the request of the program by default, \ which includes transforming the audio sample rate') self.parser.add_argument( '--rtf', action="store_true", default=False, help='Show Real-time Factor(RTF).') self.parser.add_argument( '--device', type=str, default=paddle.get_device(), help='Choose device to execute model inference.') self.parser.add_argument( '-d', '--job_dump_result', action='store_true', help='Save job result into file.') self.parser.add_argument( '-v', '--verbose', action='store_true', help='Increase logger verbosity of current task.') def _init_from_path(self, model_type: str='wenetspeech', lang: str='zh', sample_rate: int=16000, cfg_path: Optional[os.PathLike]=None, decode_method: str='attention_rescoring', num_decoding_left_chunks: int=-1, ckpt_path: Optional[os.PathLike]=None): """ Init model and other resources from a specific path. """ logger.debug("start to init the model") # default max_len: unit:second self.max_len = 50 if hasattr(self, 'model'): logger.debug('Model had been initialized.') return if cfg_path is None or ckpt_path is None: sample_rate_str = '16k' if sample_rate == 16000 else '8k' tag = model_type + '-' + lang + '-' + sample_rate_str self.task_resource.set_task_model(tag, version=None) self.res_path = self.task_resource.res_dir self.cfg_path = os.path.join( self.res_path, self.task_resource.res_dict['cfg_path']) self.ckpt_path = os.path.join( self.res_path, self.task_resource.res_dict['ckpt_path'] + ".pdparams") logger.debug(self.res_path) else: self.cfg_path = os.path.abspath(cfg_path) self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams") self.res_path = os.path.dirname( os.path.dirname(os.path.abspath(self.cfg_path))) logger.debug(self.cfg_path) logger.debug(self.ckpt_path) #Init body. self.config = CfgNode(new_allowed=True) self.config.merge_from_file(self.cfg_path) with UpdateConfig(self.config): if self.config.spm_model_prefix: self.config.spm_model_prefix = os.path.join( self.res_path, self.config.spm_model_prefix) self.text_feature = TextFeaturizer( unit_type=self.config.unit_type, vocab=self.config.vocab_filepath, spm_model_prefix=self.config.spm_model_prefix) if "deepspeech2" in model_type: self.config.decode.lang_model_path = os.path.join( MODEL_HOME, 'language_model', self.config.decode.lang_model_path) lm_url = self.task_resource.res_dict['lm_url'] lm_md5 = self.task_resource.res_dict['lm_md5'] self.download_lm( lm_url, os.path.dirname(self.config.decode.lang_model_path), lm_md5) elif "conformer" in model_type or "transformer" in model_type: self.config.decode.decoding_method = decode_method if num_decoding_left_chunks: assert num_decoding_left_chunks == -1 or num_decoding_left_chunks >= 0, "num_decoding_left_chunks should be -1 or >=0" self.config.num_decoding_left_chunks = num_decoding_left_chunks else: raise Exception("wrong type") model_name = model_type[:model_type.rindex( '_')] # model_type: {model_name}_{dataset} model_class = self.task_resource.get_model_class(model_name) model_conf = self.config model = model_class.from_config(model_conf) self.model = model self.model.eval() # load model model_dict = paddle.load(self.ckpt_path) self.model.set_state_dict(model_dict) # compute the max len limit if "conformer" in model_type or "transformer" in model_type: # in transformer like model, we may use the subsample rate cnn network subsample_rate = self.model.subsampling_rate() frame_shift_ms = self.config.preprocess_config.process[0][ 'n_shift'] / self.config.preprocess_config.process[0]['fs'] max_len = self.model.encoder.embed.pos_enc.max_len if self.config.encoder_conf.get("max_len", None): max_len = self.config.encoder_conf.max_len self.max_len = frame_shift_ms * max_len * subsample_rate logger.debug( f"The asr server limit max duration len: {self.max_len}") def preprocess(self, model_type: str, input: Union[str, os.PathLike]): """ Input preprocess and return paddle.Tensor stored in self.input. Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet). """ audio_file = input if isinstance(audio_file, (str, os.PathLike)): logger.debug("Preprocess audio_file:" + audio_file) elif isinstance(audio_file, io.BytesIO): audio_file.seek(0) # Get the object for feature extraction if "deepspeech2" in model_type or "conformer" in model_type or "transformer" in model_type: logger.debug("get the preprocess conf") preprocess_conf = self.config.preprocess_config preprocess_args = {"train": False} preprocessing = Transformation(preprocess_conf) logger.debug("read the audio file") audio, audio_sample_rate = soundfile.read( audio_file, dtype="int16", always_2d=True) if self.change_format: if audio.shape[1] >= 2: audio = audio.mean(axis=1, dtype=np.int16) else: audio = audio[:, 0] # pcm16 -> pcm 32 audio = self._pcm16to32(audio) audio = librosa.resample( audio, orig_sr=audio_sample_rate, target_sr=self.sample_rate) audio_sample_rate = self.sample_rate # pcm32 -> pcm 16 audio = self._pcm32to16(audio) else: audio = audio[:, 0] logger.debug(f"audio shape: {audio.shape}") # fbank audio = preprocessing(audio, **preprocess_args) audio_len = paddle.to_tensor(audio.shape[0]) audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0) self._inputs["audio"] = audio self._inputs["audio_len"] = audio_len logger.debug(f"audio feat shape: {audio.shape}") else: raise Exception("wrong type") logger.debug("audio feat process success") @paddle.no_grad() def infer(self, model_type: str): """ Model inference and result stored in self.output. """ logger.debug("start to infer the model to get the output") cfg = self.config.decode audio = self._inputs["audio"] audio_len = self._inputs["audio_len"] if "deepspeech2" in model_type: decode_batch_size = audio.shape[0] self.model.decoder.init_decoder( decode_batch_size, self.text_feature.vocab_list, cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta, cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n, cfg.num_proc_bsearch) result_transcripts = self.model.decode(audio, audio_len) self.model.decoder.del_decoder() self._outputs["result"] = result_transcripts[0] elif "conformer" in model_type or "transformer" in model_type: logger.debug( f"we will use the transformer like model : {model_type}") try: result_transcripts = self.model.decode( audio, audio_len, text_feature=self.text_feature, decoding_method=cfg.decoding_method, beam_size=cfg.beam_size, ctc_weight=cfg.ctc_weight, decoding_chunk_size=cfg.decoding_chunk_size, num_decoding_left_chunks=cfg.num_decoding_left_chunks, simulate_streaming=cfg.simulate_streaming) self._outputs["result"] = result_transcripts[0][0] except Exception as e: logger.exception(e) else: raise Exception("invalid model name") def postprocess(self) -> Union[str, os.PathLike]: """ Output postprocess and return human-readable results such as texts and audio files. """ return self._outputs["result"] def download_lm(self, url, lm_dir, md5sum): download_path = get_path_from_url( url=url, root_dir=lm_dir, md5sum=md5sum, decompress=False, ) def _pcm16to32(self, audio): assert (audio.dtype == np.int16) audio = audio.astype("float32") bits = np.iinfo(np.int16).bits audio = audio / (2**(bits - 1)) return audio def _pcm32to16(self, audio): assert (audio.dtype == np.float32) bits = np.iinfo(np.int16).bits audio = audio * (2**(bits - 1)) audio = np.round(audio).astype("int16") return audio def _check(self, audio_file: str, sample_rate: int, force_yes: bool=False): self.sample_rate = sample_rate if self.sample_rate != 16000 and self.sample_rate != 8000: logger.error( "invalid sample rate, please input --sr 8000 or --sr 16000") return False if isinstance(audio_file, (str, os.PathLike)): if not os.path.isfile(audio_file): logger.error("Please input the right audio file path") return False elif isinstance(audio_file, io.BytesIO): audio_file.seek(0) logger.debug("checking the audio file format......") try: audio, audio_sample_rate = soundfile.read( audio_file, dtype="int16", always_2d=True) audio_duration = audio.shape[0] / audio_sample_rate if audio_duration > self.max_len: logger.error( f"Please input audio file less then {self.max_len} seconds.\n" ) return False except Exception as e: logger.exception(e) logger.error( f"can not open the audio file, please check the audio file({audio_file}) format is 'wav'. \n \ you can try to use sox to change the file format.\n \ For example: \n \ sample rate: 16k \n \ sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \ sample rate: 8k \n \ sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \ ") return False logger.debug("The sample rate is %d" % audio_sample_rate) if audio_sample_rate != self.sample_rate: logger.warning("The sample rate of the input file is not {}.\n \ The program will resample the wav file to {}.\n \ If the result does not meet your expectations，\n \ Please input the 16k 16 bit 1 channel wav file. \ ".format(self.sample_rate, self.sample_rate)) if force_yes is False: while (True): logger.debug( "Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream." ) content = input("Input(Y/N):") if content.strip() == "Y" or content.strip( ) == "y" or content.strip() == "yes" or content.strip( ) == "Yes": logger.debug( "change the sampele rate, channel to 16k and 1 channel" ) break elif content.strip() == "N" or content.strip( ) == "n" or content.strip() == "no" or content.strip( ) == "No": logger.debug("Exit the program") return False else: logger.warning("Not regular input, please input again") self.change_format = True else: logger.debug("The audio file format is right") self.change_format = False return True def execute(self, argv: List[str]) -> bool: """ Command line entry. """ parser_args = self.parser.parse_args(argv) model = parser_args.model lang = parser_args.lang sample_rate = parser_args.sample_rate config = parser_args.config ckpt_path = parser_args.ckpt_path decode_method = parser_args.decode_method force_yes = parser_args.yes rtf = parser_args.rtf device = parser_args.device if not parser_args.verbose: self.disable_task_loggers() task_source = self.get_input_source(parser_args.input) task_results = OrderedDict() has_exceptions = False for id_, input_ in task_source.items(): try: res = self( audio_file=input_, model=model, lang=lang, sample_rate=sample_rate, config=config, ckpt_path=ckpt_path, decode_method=decode_method, force_yes=force_yes, rtf=rtf, device=device) task_results[id_] = res except Exception as e: has_exceptions = True task_results[id_] = f'{e.__class__.__name__}: {e}' if rtf: self.show_rtf(CLI_TIMER[self.__class__.__name__]) self.process_task_results(parser_args.input, task_results, parser_args.job_dump_result) if has_exceptions: return False else: return True @stats_wrapper def __call__(self, audio_file: os.PathLike, model: str='conformer_u2pp_online_wenetspeech', lang: str='zh', sample_rate: int=16000, config: os.PathLike=None, ckpt_path: os.PathLike=None, decode_method: str='attention_rescoring', num_decoding_left_chunks: int=-1, force_yes: bool=False, rtf: bool=False, device=paddle.get_device()): """ Python API to call an executor. """ audio_file = os.path.abspath(audio_file) paddle.set_device(device) self._init_from_path(model, lang, sample_rate, config, decode_method, num_decoding_left_chunks, ckpt_path) if not self._check(audio_file, sample_rate, force_yes): sys.exit(-1) if rtf: k = self.__class__.__name__ CLI_TIMER[k]['start'].append(time.time()) self.preprocess(model, audio_file) self.infer(model) res = self.postprocess() # Retrieve result of asr. if rtf: CLI_TIMER[k]['end'].append(time.time()) audio, audio_sample_rate = soundfile.read( audio_file, dtype="int16", always_2d=True) CLI_TIMER[k]['extra'].append(audio.shape[0] / audio_sample_rate) return res