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| | | # -*- encoding: utf-8 -*- |
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| | | # @Author: SWHL |
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| | | # @Contact: liekkaskono@163.com |
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| | | import os.path |
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| | | import traceback |
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| | | from pathlib import Path |
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| | | from typing import List, Union, Tuple |
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| | | |
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| | | import librosa |
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| | | import numpy as np |
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| | | |
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| | | from utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError, |
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| | | OrtInferSession, TokenIDConverter, get_logger, |
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| | | read_yaml) |
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| | | from utils.postprocess_utils import sentence_postprocess |
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| | | from utils.frontend import WavFrontend |
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| | | |
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| | | logging = get_logger() |
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| | | |
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| | | class Paraformer(): |
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| | | def __init__(self, model_dir: Union[str, Path]=None, |
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| | | batch_size: int = 1, |
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| | | device_id: Union[str, int]="-1", |
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| | | ): |
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| | | |
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| | | if not Path(model_dir).exists(): |
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| | | raise FileNotFoundError(f'{model_dir} does not exist.') |
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| | | |
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| | | model_file = os.path.join(model_dir, 'model.onnx') |
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| | | config_file = os.path.join(model_dir, 'config.yaml') |
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| | | cmvn_file = os.path.join(model_dir, 'am.mvn') |
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| | | config = read_yaml(config_file) |
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| | | |
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| | | self.converter = TokenIDConverter(config['token_list']) |
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| | | self.tokenizer = CharTokenizer() |
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| | | self.frontend = WavFrontend( |
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| | | cmvn_file=cmvn_file, |
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| | | **config['frontend_conf'] |
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| | | ) |
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| | | self.ort_infer = OrtInferSession(model_file, device_id) |
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| | | self.batch_size = batch_size |
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| | | |
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| | | def __call__(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List: |
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| | | waveform_list = self.load_data(wav_content, fs) |
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| | | waveform_nums = len(waveform_list) |
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| | | |
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| | | asr_res = [] |
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| | | for beg_idx in range(0, waveform_nums, self.batch_size): |
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| | | end_idx = min(waveform_nums, beg_idx + self.batch_size) |
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| | | |
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| | | feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx]) |
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| | | |
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| | | try: |
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| | | am_scores, valid_token_lens = self.infer(feats, feats_len) |
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| | | except ONNXRuntimeError: |
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| | | #logging.warning(traceback.format_exc()) |
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| | | logging.warning("input wav is silence or noise") |
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| | | preds = [''] |
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| | | else: |
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| | | preds = self.decode(am_scores, valid_token_lens) |
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| | | |
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| | | asr_res.extend(preds) |
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| | | return asr_res |
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| | | |
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| | | def load_data(self, |
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| | | wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List: |
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| | | def load_wav(path: str) -> np.ndarray: |
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| | | waveform, _ = librosa.load(path, sr=fs) |
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| | | return waveform |
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| | | |
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| | | if isinstance(wav_content, np.ndarray): |
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| | | return [wav_content] |
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| | | |
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| | | if isinstance(wav_content, str): |
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| | | return [load_wav(wav_content)] |
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| | | |
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| | | if isinstance(wav_content, list): |
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| | | return [load_wav(path) for path in wav_content] |
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| | | |
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| | | raise TypeError( |
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| | | f'The type of {wav_content} is not in [str, np.ndarray, list]') |
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| | | |
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| | | def extract_feat(self, |
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| | | waveform_list: List[np.ndarray] |
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| | | ) -> Tuple[np.ndarray, np.ndarray]: |
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| | | feats, feats_len = [], [] |
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| | | for waveform in waveform_list: |
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| | | speech, _ = self.frontend.fbank(waveform) |
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| | | feat, feat_len = self.frontend.lfr_cmvn(speech) |
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| | | feats.append(feat) |
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| | | feats_len.append(feat_len) |
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| | | |
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| | | feats = self.pad_feats(feats, np.max(feats_len)) |
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| | | feats_len = np.array(feats_len).astype(np.int32) |
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| | | return feats, feats_len |
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| | | |
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| | | @staticmethod |
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| | | def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray: |
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| | | def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray: |
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| | | pad_width = ((0, max_feat_len - cur_len), (0, 0)) |
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| | | return np.pad(feat, pad_width, 'constant', constant_values=0) |
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| | | |
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| | | feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats] |
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| | | feats = np.array(feat_res).astype(np.float32) |
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| | | return feats |
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| | | |
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| | | def infer(self, feats: np.ndarray, |
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| | | feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: |
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| | | am_scores, token_nums = self.ort_infer([feats, feats_len]) |
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| | | return am_scores, token_nums |
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| | | |
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| | | def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]: |
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| | | return [self.decode_one(am_score, token_num) |
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| | | for am_score, token_num in zip(am_scores, token_nums)] |
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| | | |
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| | | def decode_one(self, |
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| | | am_score: np.ndarray, |
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| | | valid_token_num: int) -> List[str]: |
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| | | yseq = am_score.argmax(axis=-1) |
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| | | score = am_score.max(axis=-1) |
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| | | score = np.sum(score, axis=-1) |
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| | | |
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| | | # pad with mask tokens to ensure compatibility with sos/eos tokens |
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| | | # asr_model.sos:1 asr_model.eos:2 |
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| | | yseq = np.array([1] + yseq.tolist() + [2]) |
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| | | hyp = Hypothesis(yseq=yseq, score=score) |
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| | | |
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| | | # remove sos/eos and get results |
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| | | last_pos = -1 |
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| | | token_int = hyp.yseq[1:last_pos].tolist() |
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| | | |
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| | | # remove blank symbol id, which is assumed to be 0 |
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| | | token_int = list(filter(lambda x: x not in (0, 2), token_int)) |
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| | | |
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| | | # Change integer-ids to tokens |
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| | | token = self.converter.ids2tokens(token_int) |
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| | | token = token[:valid_token_num-1] |
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| | | texts = sentence_postprocess(token) |
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| | | text = texts[0] |
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| | | # text = self.tokenizer.tokens2text(token) |
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| | | return text |
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