# 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 os import subprocess from typing import List from typing import Optional from typing import Union import kaldi_io import numpy as np import paddle from kaldiio import WriteHelper from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.s2t.utils.utility import UpdateConfig from yacs.config import CfgNode from ..executor import BaseExecutor from ..utils import cli_register from ..utils import download_and_decompress from ..utils import logger from ..utils import MODEL_HOME __all__ = ["STExecutor"] pretrained_models = { "fat_st_ted_en_zh": { "url": "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_mtl.model.tar.gz", "md5": "210b8eacc390d9965334fa8e96c49a13", "cfg_path": "conf/transformer_mtl_noam.yaml", "ckpt_path": "exp/transformer_mtl_noam/checkpoints/fat_st_ted_en_zh", } } model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"} kaldi_bins = { "url": "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz", "md5": "c0682303b3f3393dbf6ed4c4e35a53eb", } @cli_register( name="paddlespeech.st", description="Speech translation infer command.") class STExecutor(BaseExecutor): def __init__(self): super(STExecutor, self).__init__() self.parser = argparse.ArgumentParser( prog="paddlespeech.st", add_help=True) self.parser.add_argument( "--input", type=str, required=True, help="Audio file to translate.") self.parser.add_argument( "--model", type=str, default="fat_st", help="Choose model type of st task.") self.parser.add_argument( "--lang", type=str, default="ted_en_zh", help="Choose model language.") self.parser.add_argument( "--config", type=str, default=None, help="Config of st task. Use deault config when it is None.") self.parser.add_argument( "--ckpt_path", type=str, default=None, help="Checkpoint file of model.") self.parser.add_argument( "--device", type=str, default=paddle.get_device(), help="Choose device to execute model inference.") def _get_pretrained_path(self, tag: str) -> os.PathLike: """ Download and returns pretrained resources path of current task. """ assert tag in pretrained_models, "Can not find pretrained resources of {}.".format( tag) res_path = os.path.join(MODEL_HOME, tag) decompressed_path = download_and_decompress(pretrained_models[tag], res_path) decompressed_path = os.path.abspath(decompressed_path) logger.info( "Use pretrained model stored in: {}".format(decompressed_path)) return decompressed_path def _set_kaldi_bins(self) -> os.PathLike: """ Download and returns kaldi_bins resources path of current task. """ decompressed_path = download_and_decompress(kaldi_bins, MODEL_HOME) decompressed_path = os.path.abspath(decompressed_path) logger.info("Kaldi_bins stored in: {}".format(decompressed_path)) os.environ['LD_LIBRARY_PATH'] += f':{decompressed_path}' os.environ["PATH"] += f":{decompressed_path}" return decompressed_path def _init_from_path(self, model_type: str="fat_st", lang: str="zh", cfg_path: Optional[os.PathLike]=None, ckpt_path: Optional[os.PathLike]=None): """ Init model and other resources from a specific path. """ if cfg_path is None or ckpt_path is None: tag = model_type + "_" + lang res_path = self._get_pretrained_path(tag) self.cfg_path = os.path.join(res_path, pretrained_models[tag]["cfg_path"]) self.ckpt_path = os.path.join(res_path, pretrained_models[tag]["ckpt_path"]) logger.info(res_path) logger.info(self.cfg_path) logger.info(self.ckpt_path) else: self.cfg_path = os.path.abspath(cfg_path) self.ckpt_path = os.path.abspath(ckpt_path) 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) self.config.decoding.decoding_method = "fullsentence" with UpdateConfig(self.config): self.config.collator.vocab_filepath = os.path.join( res_path, self.config.collator.vocab_filepath) self.config.collator.cmvn_path = os.path.join( res_path, self.config.collator.cmvn_path) self.config.collator.spm_model_prefix = os.path.join( res_path, self.config.collator.spm_model_prefix) self.text_feature = TextFeaturizer( unit_type=self.config.collator.unit_type, vocab_filepath=self.config.collator.vocab_filepath, spm_model_prefix=self.config.collator.spm_model_prefix) self.config.model.input_dim = self.config.collator.feat_dim self.config.model.output_dim = self.text_feature.vocab_size model_conf = self.config.model logger.info(model_conf) model_class = dynamic_import(model_type, model_alias) self.model = model_class.from_config(model_conf) self.model.eval() # load model params_path = self.ckpt_path + ".pdparams" model_dict = paddle.load(params_path) self.model.set_state_dict(model_dict) # set kaldi bins self._set_kaldi_bins() def preprocess(self, wav_file: Union[str, os.PathLike], model_type: str): """ Input preprocess and return paddle.Tensor stored in self.input. Input content can be a file(wav). """ audio_file = os.path.abspath(wav_file) logger.info("Preprocess audio_file:" + audio_file) if model_type == "fat_st": cmvn = self.config.collator.cmvn_path utt_name = "_tmp" # Get the object for feature extraction fbank_extract_command = [ "compute-fbank-feats", "--num-mel-bins=80", "--verbose=2", "--sample-frequency=16000", "scp:-", "ark:-" ] fbank_extract_process = subprocess.Popen( fbank_extract_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) fbank_extract_process.stdin.write( f"{utt_name} {wav_file}".encode("utf8")) fbank_extract_process.stdin.close() fbank_feat = dict( kaldi_io.read_mat_ark(fbank_extract_process.stdout))[utt_name] extract_command = ["compute-kaldi-pitch-feats", "scp:-", "ark:-"] pitch_extract_process = subprocess.Popen( extract_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) pitch_extract_process.stdin.write( f"{utt_name} {wav_file}".encode("utf8")) process_command = ["process-kaldi-pitch-feats", "ark:", "ark:-"] pitch_process = subprocess.Popen( process_command, stdin=pitch_extract_process.stdout, stdout=subprocess.PIPE) pitch_extract_process.stdin.close() pitch_feat = dict( kaldi_io.read_mat_ark(pitch_process.stdout))[utt_name] concated_feat = np.concatenate((fbank_feat, pitch_feat), axis=1) raw_feat = f"{utt_name}.raw" with WriteHelper( f"ark,scp:{raw_feat}.ark,{raw_feat}.scp") as writer: writer(utt_name, concated_feat) cmvn_command = [ "apply-cmvn", "--norm-vars=true", cmvn, f"scp:{raw_feat}.scp", "ark:-" ] cmvn_process = subprocess.Popen( cmvn_command, stdout=subprocess.PIPE) process_command = [ "copy-feats", "--compress=true", "ark:-", "ark:-" ] process = subprocess.Popen( process_command, stdin=cmvn_process.stdout, stdout=subprocess.PIPE) norm_feat = dict(kaldi_io.read_mat_ark(process.stdout))[utt_name] self.audio = paddle.to_tensor(norm_feat).unsqueeze(0) self.audio_len = paddle.to_tensor( self.audio.shape[1], dtype="int64") logger.info(f"audio feat shape: {self.audio.shape}") else: raise ValueError("Wrong model type.") @paddle.no_grad() def infer(self, model_type: str): """ Model inference and result stored in self.output. """ cfg = self.config.decoding audio = self.audio audio_len = self.audio_len if model_type == "fat_st": hyps = self.model.decode( audio, audio_len, text_feature=self.text_feature, decoding_method=cfg.decoding_method, lang_model_path=None, beam_alpha=cfg.alpha, beam_beta=cfg.beta, beam_size=cfg.beam_size, cutoff_prob=cfg.cutoff_prob, cutoff_top_n=cfg.cutoff_top_n, num_processes=cfg.num_proc_bsearch, ctc_weight=cfg.ctc_weight, word_reward=cfg.word_reward, decoding_chunk_size=cfg.decoding_chunk_size, num_decoding_left_chunks=cfg.num_decoding_left_chunks, simulate_streaming=cfg.simulate_streaming) self.result_transcripts = hyps else: raise ValueError("Wrong model type.") def postprocess(self, model_type: str) -> Union[str, os.PathLike]: """ Output postprocess and return human-readable results such as texts and audio files. """ if model_type == "fat_st": return self.result_transcripts else: raise ValueError("Wrong model type.") 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 config = parser_args.config ckpt_path = parser_args.ckpt_path audio_file = parser_args.input device = parser_args.device try: res = self(model, lang, config, ckpt_path, audio_file, device) logger.info('ST Result: {}'.format(res)) return True except Exception as e: print(e) return False def __call__(self, model, lang, config, ckpt_path, audio_file, device): """ Python API to call an executor. """ audio_file = os.path.abspath(audio_file) paddle.set_device(device) self._init_from_path(model, lang, config, ckpt_path) self.preprocess(audio_file, model) self.infer(model) res = self.postprocess(model) return res