Merge pull request #160 from alibaba-damo-academy/dev_onnx

zhifu gao

2023-02-27 19467b57f6476cc0ba5493c0dcde3d15a0c88c2c

 funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py

@@ -5,6 +5,7 @@
from pathlib import Path
from typing import List, Union, Tuple

import copy
import librosa
import numpy as np

@@ -13,6 +14,7 @@
                          read_yaml)
from .utils.postprocess_utils import sentence_postprocess
from .utils.frontend import WavFrontend
from funasr.utils.timestamp_tools import time_stamp_lfr6_pl

logging = get_logger()

@@ -134,8 +136,67 @@

        # Change integer-ids to tokens
        token = self.converter.ids2tokens(token_int)
        token = token[:valid_token_num-1]
        # token = token[:valid_token_num-1]
        texts = sentence_postprocess(token)
        text = texts[0]
        # text = self.tokenizer.tokens2text(token)
        return text


class BiCifParaformer(Paraformer):
    def infer(self, feats: np.ndarray,
              feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        am_scores, token_nums, us_alphas, us_cif_peak = self.ort_infer([feats, feats_len])
        return am_scores, token_nums, us_alphas, us_cif_peak
    def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
        waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
        waveform_nums = len(waveform_list)

        asr_res = []
        for beg_idx in range(0, waveform_nums, self.batch_size):
            res = {}
            end_idx = min(waveform_nums, beg_idx + self.batch_size)
            feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
            am_scores, valid_token_lens, us_alphas, us_cif_peak = self.infer(feats, feats_len)

            try:
                am_scores, valid_token_lens, us_alphas, us_cif_peak = self.infer(feats, feats_len)
            except ONNXRuntimeError:
                #logging.warning(traceback.format_exc())
                logging.warning("input wav is silence or noise")
                preds = ['']
            else:
                token = self.decode(am_scores, valid_token_lens)
                timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(token[0]), log=False)
                texts = sentence_postprocess(token[0], timestamp)
                # texts = sentence_postprocess(token[0])
                text = texts[0]
            res['text'] = text
            res['timestamp'] = timestamp
            asr_res.append(res)

        return asr_res

    def decode_one(self,
                   am_score: np.ndarray,
                   valid_token_num: int) -> List[str]:
        yseq = am_score.argmax(axis=-1)
        score = am_score.max(axis=-1)
        score = np.sum(score, axis=-1)

        # pad with mask tokens to ensure compatibility with sos/eos tokens
        # asr_model.sos:1  asr_model.eos:2
        yseq = np.array([1] + yseq.tolist() + [2])
        hyp = Hypothesis(yseq=yseq, score=score)

        # remove sos/eos and get results
        last_pos = -1
        token_int = hyp.yseq[1:last_pos].tolist()

        # remove blank symbol id, which is assumed to be 0
        token_int = list(filter(lambda x: x not in (0, 2), token_int))

        # Change integer-ids to tokens
        token = self.converter.ids2tokens(token_int)
        # token = token[:valid_token_num-1]
        return token