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| | | # -*- encoding: utf-8 -*- |
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| | | import os.path |
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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 copy |
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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, |
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| | | 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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| | | from .utils.timestamp_utils import time_stamp_lfr6_onnx |
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| | | logging = get_logger() |
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| | | |
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| | | import torch |
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| | | |
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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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| | | plot_timestamp_to: str = "", |
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| | | pred_bias: int = 1, |
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| | | quantize: bool = False, |
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| | | intra_op_num_threads: 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.torchscripts') |
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| | | if quantize: |
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| | | model_file = os.path.join(model_dir, 'model_quant.torchscripts') |
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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 = torch.jit.load(model_file) |
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| | | self.batch_size = batch_size |
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| | | self.device_id = device_id |
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| | | self.plot_timestamp_to = plot_timestamp_to |
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| | | self.pred_bias = pred_bias |
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| | | |
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| | | def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List: |
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| | | waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq) |
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| | | waveform_nums = len(waveform_list) |
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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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| | | |
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| | | end_idx = min(waveform_nums, beg_idx + self.batch_size) |
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| | | feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx]) |
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| | | try: |
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| | | with torch.no_grad(): |
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| | | if int(self.device_id) == -1: |
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| | | outputs = self.ort_infer(feats, feats_len) |
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| | | am_scores, valid_token_lens = outputs[0], outputs[1] |
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| | | else: |
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| | | outputs = self.ort_infer(feats.cuda(), feats_len.cuda()) |
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| | | am_scores, valid_token_lens = outputs[0].cpu(), outputs[1].cpu() |
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| | | if len(outputs) == 4: |
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| | | # for BiCifParaformer Inference |
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| | | us_alphas, us_peaks = outputs[2], outputs[3] |
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| | | else: |
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| | | us_alphas, us_peaks = None, None |
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| | | except: |
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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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| | | if us_peaks is None: |
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| | | for pred in preds: |
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| | | pred = sentence_postprocess(pred) |
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| | | asr_res.append({'preds': pred}) |
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| | | else: |
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| | | for pred, us_peaks_ in zip(preds, us_peaks): |
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| | | raw_tokens = pred |
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| | | timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens)) |
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| | | text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw) |
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| | | # logging.warning(timestamp) |
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| | | if len(self.plot_timestamp_to): |
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| | | self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to) |
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| | | asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens}) |
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| | | return asr_res |
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| | | |
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| | | def plot_wave_timestamp(self, wav, text_timestamp, dest): |
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| | | # TODO: Plot the wav and timestamp results with matplotlib |
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| | | import matplotlib |
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| | | matplotlib.use('Agg') |
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| | | matplotlib.rc("font", family='Alibaba PuHuiTi') # set it to a font that your system supports |
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| | | import matplotlib.pyplot as plt |
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| | | fig, ax1 = plt.subplots(figsize=(11, 3.5), dpi=320) |
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| | | ax2 = ax1.twinx() |
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| | | ax2.set_ylim([0, 2.0]) |
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| | | # plot waveform |
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| | | ax1.set_ylim([-0.3, 0.3]) |
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| | | time = np.arange(wav.shape[0]) / 16000 |
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| | | ax1.plot(time, wav/wav.max()*0.3, color='gray', alpha=0.4) |
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| | | # plot lines and text |
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| | | for (char, start, end) in text_timestamp: |
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| | | ax1.vlines(start, -0.3, 0.3, ls='--') |
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| | | ax1.vlines(end, -0.3, 0.3, ls='--') |
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| | | x_adj = 0.045 if char != '<sil>' else 0.12 |
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| | | ax1.text((start + end) * 0.5 - x_adj, 0, char) |
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| | | # plt.legend() |
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| | | plotname = "{}/timestamp.png".format(dest) |
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| | | plt.savefig(plotname, bbox_inches='tight') |
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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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| | | feats = torch.from_numpy(feats).type(torch.float32) |
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| | | feats_len = torch.from_numpy(feats_len).type(torch.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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| | | outputs = self.ort_infer([feats, feats_len]) |
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| | | return outputs |
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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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| | | # 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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| | | # 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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| | | # 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-self.pred_bias] |
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| | | # texts = sentence_postprocess(token) |
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| | | return token |
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