From 544b798b32819fe2ffed1fccb44e8c2620449a53 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 二月 2023 17:30:51 +0800
Subject: [PATCH] Merge branch 'dev_gzf' of github.com:alibaba-damo-academy/FunASR into dev_gzf add
---
funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py | 144 ++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 144 insertions(+), 0 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py
new file mode 100644
index 0000000..d51c6bf
--- /dev/null
+++ b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py
@@ -0,0 +1,144 @@
+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+import os.path
+import traceback
+from pathlib import Path
+from typing import List, Union, Tuple
+
+import librosa
+import numpy as np
+
+from utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
+ OrtInferSession, TokenIDConverter, get_logger,
+ read_yaml)
+from utils.postprocess_utils import sentence_postprocess
+from utils.frontend import WavFrontend
+
+logging = get_logger()
+
+
+class Paraformer():
+ def __init__(self, model_dir: Union[str, Path]=None,
+ batch_size: int = 1,
+ device_id: Union[str, int]="-1",
+ ):
+
+ if not Path(model_dir).exists():
+ raise FileNotFoundError(f'{model_dir} does not exist.')
+
+ model_file = os.path.join(model_dir, 'model.onnx')
+ config_file = os.path.join(model_dir, 'config.yaml')
+ cmvn_file = os.path.join(model_dir, 'am.mvn')
+ config = read_yaml(config_file)
+
+ self.converter = TokenIDConverter(config['token_list'])
+ self.tokenizer = CharTokenizer()
+ self.frontend = WavFrontend(
+ cmvn_file=cmvn_file,
+ **config['frontend_conf']
+ )
+ self.ort_infer = OrtInferSession(model_file, device_id)
+ self.batch_size = batch_size
+
+ def __call__(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
+ waveform_list = self.load_data(wav_content, fs)
+ waveform_nums = len(waveform_list)
+
+ asr_res = []
+ for beg_idx in range(0, waveform_nums, self.batch_size):
+ 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)
+ 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)
+ return asr_res
+
+ def load_data(self,
+ wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
+ def load_wav(path: str) -> np.ndarray:
+ waveform, _ = librosa.load(path, sr=fs)
+ return waveform
+
+ if isinstance(wav_content, np.ndarray):
+ return [wav_content]
+
+ if isinstance(wav_content, str):
+ return [load_wav(wav_content)]
+
+ if isinstance(wav_content, list):
+ return [load_wav(path) for path in wav_content]
+
+ raise TypeError(
+ f'The type of {wav_content} is not in [str, np.ndarray, list]')
+
+ def extract_feat(self,
+ waveform_list: List[np.ndarray]
+ ) -> Tuple[np.ndarray, np.ndarray]:
+ feats, feats_len = [], []
+ for waveform in waveform_list:
+ speech, _ = self.frontend.fbank(waveform)
+ feat, feat_len = self.frontend.lfr_cmvn(speech)
+ feats.append(feat)
+ feats_len.append(feat_len)
+
+ feats = self.pad_feats(feats, np.max(feats_len))
+ feats_len = np.array(feats_len).astype(np.int32)
+ return feats, feats_len
+
+ @staticmethod
+ def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
+ def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
+ pad_width = ((0, max_feat_len - cur_len), (0, 0))
+ return np.pad(feat, pad_width, 'constant', constant_values=0)
+
+ feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
+ feats = np.array(feat_res).astype(np.float32)
+ return feats
+
+ 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
+
+ def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
+ return [self.decode_one(am_score, token_num)
+ for am_score, token_num in zip(am_scores, token_nums)]
+
+ 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]
+ texts = sentence_postprocess(token)
+ text = texts[0]
+ # text = self.tokenizer.tokens2text(token)
+ return text
+
+
--
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