From 19467b57f6476cc0ba5493c0dcde3d15a0c88c2c Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 27 二月 2023 17:04:19 +0800
Subject: [PATCH] Merge pull request #160 from alibaba-damo-academy/dev_onnx
---
funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py | 63 +++++++++++++++++++++++++++++++
1 files changed, 62 insertions(+), 1 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
index a786ef0..d77bcf7 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
+++ b/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
\ No newline at end of file
--
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