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add backend inference and inferface output
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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
import json
import math
import numpy as np
def _load_json_cmvn(json_cmvn_file):
"""Load the json format cmvn stats file and calculate cmvn
Args:
json_cmvn_file: cmvn stats file in json format
Returns:
a numpy array of [means, vars]
"""
with open(json_cmvn_file) as f:
cmvn_stats = json.load(f)
means = cmvn_stats["mean_stat"]
variance = cmvn_stats["var_stat"]
count = cmvn_stats["frame_num"]
for i in range(len(means)):
means[i] /= count
variance[i] = variance[i] / count - means[i] * means[i]
if variance[i] < 1.0e-20:
variance[i] = 1.0e-20
variance[i] = 1.0 / math.sqrt(variance[i])
cmvn = np.array([means, variance])
return cmvn
def _load_kaldi_cmvn(kaldi_cmvn_file):
"""Load the kaldi format cmvn stats file and calculate cmvn
Args:
kaldi_cmvn_file: kaldi text style global cmvn file, which
is generated by:
compute-cmvn-stats --binary=false scp:feats.scp global_cmvn
Returns:
a numpy array of [means, vars]
"""
means = []
variance = []
with open(kaldi_cmvn_file, "r") as fid:
# kaldi binary file start with '\0B'
if fid.read(2) == "\0B":
logging.error(
"kaldi cmvn binary file is not supported, please "
"recompute it by: compute-cmvn-stats --binary=false "
" scp:feats.scp global_cmvn"
)
sys.exit(1)
fid.seek(0)
arr = fid.read().split()
assert arr[0] == "["
assert arr[-2] == "0"
assert arr[-1] == "]"
feat_dim = int((len(arr) - 2 - 2) / 2)
for i in range(1, feat_dim + 1):
means.append(float(arr[i]))
count = float(arr[feat_dim + 1])
for i in range(feat_dim + 2, 2 * feat_dim + 2):
variance.append(float(arr[i]))
for i in range(len(means)):
means[i] /= count
variance[i] = variance[i] / count - means[i] * means[i]
if variance[i] < 1.0e-20:
variance[i] = 1.0e-20
variance[i] = 1.0 / math.sqrt(variance[i])
cmvn = np.array([means, variance])
return cmvn
def load_cmvn(cmvn_file, is_json):
if is_json:
cmvn = _load_json_cmvn(cmvn_file)
else:
cmvn = _load_kaldi_cmvn(cmvn_file)
return cmvn[0], cmvn[1]