# Copyright (c) 2022 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 io import os from typing import List from typing import Optional from typing import Union import librosa import paddle import soundfile from yacs.config import CfgNode from paddlespeech.cli.utils import MODEL_HOME from paddlespeech.s2t.modules.ctc import CTCDecoder from paddlespeech.cli.asr.infer import ASRExecutor from paddlespeech.cli.log import logger from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.transform.transformation import Transformation from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.s2t.utils.utility import UpdateConfig from paddlespeech.server.utils.config import get_config from paddlespeech.server.utils.paddle_predictor import init_predictor from paddlespeech.server.utils.paddle_predictor import run_model from paddlespeech.server.engine.base_engine import BaseEngine __all__ = ['ASREngine'] pretrained_models = { "deepspeech2offline_aishell-zh-16k": { 'url': 'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz', 'md5': '932c3593d62fe5c741b59b31318aa314', 'cfg_path': 'model.yaml', 'ckpt_path': 'exp/deepspeech2/checkpoints/avg_1', 'model': 'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel', 'params': 'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams', 'lm_url': 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm', 'lm_md5': '29e02312deb2e59b3c8686c7966d4fe3' }, } class ASRServerExecutor(ASRExecutor): def __init__(self): super().__init__() pass def _init_from_path(self, model_type: str='wenetspeech', am_model: Optional[os.PathLike]=None, am_params: Optional[os.PathLike]=None, lang: str='zh', sample_rate: int=16000, cfg_path: Optional[os.PathLike]=None, decode_method: str='attention_rescoring', am_predictor_conf: dict=None): """ Init model and other resources from a specific path. """ if cfg_path is None or am_model is None or am_params is None: sample_rate_str = '16k' if sample_rate == 16000 else '8k' tag = model_type + '-' + lang + '-' + sample_rate_str res_path = self._get_pretrained_path(tag) # wenetspeech_zh self.res_path = res_path self.cfg_path = os.path.join(res_path, pretrained_models[tag]['cfg_path']) self.am_model = os.path.join(res_path, pretrained_models[tag]['model']) self.am_params = os.path.join(res_path, pretrained_models[tag]['params']) logger.info(res_path) logger.info(self.cfg_path) logger.info(self.am_model) logger.info(self.am_params) else: self.cfg_path = os.path.abspath(cfg_path) self.am_model = os.path.abspath(am_model) self.am_params = os.path.abspath(am_params) self.res_path = os.path.dirname( os.path.dirname(os.path.abspath(self.cfg_path))) #Init body. self.config = CfgNode(new_allowed=True) self.config.merge_from_file(self.cfg_path) with UpdateConfig(self.config): if "deepspeech2online" in model_type or "deepspeech2offline" in model_type: from paddlespeech.s2t.io.collator import SpeechCollator self.vocab = self.config.vocab_filepath self.config.decode.lang_model_path = os.path.join( MODEL_HOME, 'language_model', self.config.decode.lang_model_path) self.collate_fn_test = SpeechCollator.from_config(self.config) self.text_feature = TextFeaturizer( unit_type=self.config.unit_type, vocab=self.vocab) lm_url = pretrained_models[tag]['lm_url'] lm_md5 = pretrained_models[tag]['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 or "wenetspeech" in model_type: raise Exception("wrong type") else: raise Exception("wrong type") # AM predictor self.am_predictor_conf = am_predictor_conf self.am_predictor = init_predictor( model_file=self.am_model, params_file=self.am_params, predictor_conf=self.am_predictor_conf) # decoder self.decoder = CTCDecoder( odim=self.config.output_dim, # is in vocab enc_n_units=self.config.rnn_layer_size * 2, blank_id=self.config.blank_id, dropout_rate=0.0, reduction=True, # sum batch_average=True, # sum / batch_size grad_norm_type=self.config.get('ctc_grad_norm_type', None)) @paddle.no_grad() def infer(self, model_type: str): """ Model inference and result stored in self.output. """ cfg = self.config.decode audio = self._inputs["audio"] audio_len = self._inputs["audio_len"] if "deepspeech2online" in model_type or "deepspeech2offline" in model_type: decode_batch_size = audio.shape[0] # init once self.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) output_data = run_model( self.am_predictor, [audio.numpy(), audio_len.numpy()]) probs = output_data[0] eouts_len = output_data[1] batch_size = probs.shape[0] self.decoder.reset_decoder(batch_size=batch_size) self.decoder.next(probs, eouts_len) trans_best, trans_beam = self.decoder.decode() # self.model.decoder.del_decoder() self._outputs["result"] = trans_best[0] elif "conformer" in model_type or "transformer" in model_type: raise Exception("invalid model name") else: raise Exception("invalid model name") class ASREngine(BaseEngine): """ASR server engine Args: metaclass: Defaults to Singleton. """ def __init__(self): super(ASREngine, self).__init__() def init(self, config_file: str) -> bool: """init engine resource Args: config_file (str): config file Returns: bool: init failed or success """ self.input = None self.output = None self.executor = ASRServerExecutor() self.config = get_config(config_file) paddle.set_device(paddle.get_device()) self.executor._init_from_path( model_type=self.config.model_type, am_model=self.config.am_model, am_params=self.config.am_params, lang=self.config.lang, sample_rate=self.config.sample_rate, cfg_path=self.config.cfg_path, decode_method=self.config.decode_method, am_predictor_conf=self.config.am_predictor_conf) logger.info("Initialize ASR server engine successfully.") return True def run(self, audio_data): """engine run Args: audio_data (bytes): base64.b64decode """ if self.executor._check( io.BytesIO(audio_data), self.config.sample_rate, self.config.force_yes): logger.info("start running asr engine") self.executor.preprocess(self.config.model_type, io.BytesIO(audio_data)) self.executor.infer(self.config.model_type) self.output = self.executor.postprocess() # Retrieve result of asr. logger.info("end inferring asr engine") else: logger.info("file check failed!") self.output = None def postprocess(self): """postprocess """ return self.output