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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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| | | from cgitb import text |
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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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| | | 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, 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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| | | 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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| | | from .utils.timestamp_utils import time_stamp_lfr6_onnx |
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| | | |
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| | | logging = get_logger() |
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| | | class Paraformer(): |
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| | | def __init__(self, model_dir: Union[str, Path]=None, |
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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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| | | 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 = 4, |
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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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| | | if quantize: |
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| | | model_file = os.path.join(model_dir, 'model_quant.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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| | | 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.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads) |
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| | | self.batch_size = batch_size |
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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.samp_freq) |
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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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| | | |
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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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| | | am_scores, valid_token_lens = self.infer(feats, feats_len) |
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| | | outputs = self.infer(feats, feats_len) |
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| | | am_scores, valid_token_lens = outputs[0], outputs[1] |
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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 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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| | | 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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| | | 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 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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| | | 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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| | | # 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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| | | 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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