torchscripts

游雁

2023-03-02 ec3ccbea9ff1d869becaa2b13255d0da1e4bf3ca

 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()

@@ -46,20 +48,29 @@

        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])

            try:
                am_scores, valid_token_lens = self.infer(feats, feats_len)
                outputs = self.infer(feats, feats_len)
                am_scores, valid_token_lens = outputs[0], outputs[1]
                if len(outputs) == 4:
                    # for BiCifParaformer Inference
                    us_alphas, us_cif_peak = outputs[2], outputs[3]
                else:
                    us_alphas, us_cif_peak = None, None
            except ONNXRuntimeError:
                #logging.warning(traceback.format_exc())
                logging.warning("input wav is silence or noise")
                preds = ['']
            else:
                preds = self.decode(am_scores, valid_token_lens)

            asr_res.extend(preds)
                preds, raw_token = self.decode(am_scores, valid_token_lens)[0]
                res['preds'] = preds
                if us_cif_peak is not None:
                    timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(raw_token), log=False)
                    res['timestamp'] = timestamp
            asr_res.append(res)
        return asr_res

    def load_data(self,
@@ -106,8 +117,8 @@

    def infer(self, feats: np.ndarray,
              feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        am_scores, token_nums = self.ort_infer([feats, feats_len])
        return am_scores, token_nums
        outputs = self.ort_infer([feats, feats_len])
        return outputs

    def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
        return [self.decode_one(am_score, token_num)
@@ -134,8 +145,9 @@

        # 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
        return text, token