yujinqiu commited on
Commit
d9097f0
1 Parent(s): 416bccf

Fix conflict

Browse files
add-model-metadata.py ADDED
@@ -0,0 +1,87 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
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+
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+ # Copyright (c) 2023 Xiaomi Corporation
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+ # Author: Fangjun Kuang
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+
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+ from typing import Dict
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+
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+ import numpy as np
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+ import onnx
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+
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+
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+ def load_cmvn():
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+ neg_mean = None
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+ inv_stddev = None
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+
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+ with open("am.mvn") as f:
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+ for line in f:
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+ if not line.startswith("<LearnRateCoef>"):
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+ continue
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+ t = line.split()[3:-1]
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+
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+ if neg_mean is None:
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+ neg_mean = ",".join(t)
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+ else:
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+ inv_stddev = ",".join(t)
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+
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+ return neg_mean, inv_stddev
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+
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+
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+ def load_lfr_params():
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+ with open("config.yaml") as f:
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+ for line in f:
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+ if "lfr_m" in line:
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+ lfr_m = int(line.split()[-1])
35
+ elif "lfr_n" in line:
36
+ lfr_n = int(line.split()[-1])
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+ break
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+ lfr_window_size = lfr_m
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+ lfr_window_shift = lfr_n
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+ return lfr_window_size, lfr_window_shift
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+
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+
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+ def get_vocab_size():
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+ with open("tokens.txt") as f:
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+ return len(f.readlines())
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+
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+
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+ def add_meta_data(filename: str, meta_data: Dict[str, str]):
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+ """Add meta data to an ONNX model. It is changed in-place.
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+
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+ Args:
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+ filename:
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+ Filename of the ONNX model to be changed.
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+ meta_data:
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+ Key-value pairs.
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+ """
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+ model = onnx.load(filename)
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+ for key, value in meta_data.items():
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+ meta = model.metadata_props.add()
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+ meta.key = key
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+ meta.value = value
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+
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+ onnx.save(model, filename)
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+ print(f"Updated {filename}")
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+
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+
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+ def main():
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+ lfr_window_size, lfr_window_shift = load_lfr_params()
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+ neg_mean, inv_stddev = load_cmvn()
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+ vocab_size = get_vocab_size()
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+
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+ meta_data = {
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+ "lfr_window_size": str(lfr_window_size),
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+ "lfr_window_shift": str(lfr_window_shift),
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+ "neg_mean": neg_mean,
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+ "inv_stddev": inv_stddev,
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+ "model_type": "paraformer",
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+ "version": "1",
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+ "model_author": "damo",
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+ "vocab_size": str(vocab_size),
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+ "comment": "speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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+ }
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+ add_meta_data("model.onnx", meta_data)
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+
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+
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+ if __name__ == "__main__":
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+ main()
am.mvn ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ <Nnet>
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+ <Splice> 560 560
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+ [ 0 ]
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+ <AddShift> 560 560
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+ <LearnRateCoef> 0 [ -8.311879 -8.600912 -9.615928 -10.43595 -11.21292 -11.88333 -12.36243 -12.63706 -12.8818 -12.83066 -12.89103 -12.95666 -13.19763 -13.40598 -13.49113 -13.5546 -13.55639 -13.51915 -13.68284 -13.53289 -13.42107 -13.65519 -13.50713 -13.75251 -13.76715 -13.87408 -13.73109 -13.70412 -13.56073 -13.53488 -13.54895 -13.56228 -13.59408 -13.62047 -13.64198 -13.66109 -13.62669 -13.58297 -13.57387 -13.4739 -13.53063 -13.48348 -13.61047 -13.64716 -13.71546 -13.79184 -13.90614 -14.03098 -14.18205 -14.35881 -14.48419 -14.60172 -14.70591 -14.83362 -14.92122 -15.00622 -15.05122 -15.03119 -14.99028 -14.92302 -14.86927 -14.82691 -14.7972 -14.76909 -14.71356 -14.61277 -14.51696 -14.42252 -14.36405 -14.30451 -14.23161 -14.19851 -14.16633 -14.15649 -14.10504 -13.99518 -13.79562 -13.3996 -12.7767 -11.71208 -8.311879 -8.600912 -9.615928 -10.43595 -11.21292 -11.88333 -12.36243 -12.63706 -12.8818 -12.83066 -12.89103 -12.95666 -13.19763 -13.40598 -13.49113 -13.5546 -13.55639 -13.51915 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6
+ <Rescale> 560 560
7
+ <LearnRateCoef> 0 [ 0.155775 0.154484 0.1527379 0.1518718 0.1506028 0.1489256 0.147067 0.1447061 0.1436307 0.1443568 0.1451849 0.1455157 0.1452821 0.1445717 0.1439195 0.1435867 0.1436018 0.1438781 0.1442086 0.1448844 0.1454756 0.145663 0.146268 0.1467386 0.1472724 0.147664 0.1480913 0.1483739 0.1488841 0.1493636 0.1497088 0.1500379 0.1502916 0.1505389 0.1506787 0.1507102 0.1505992 0.1505445 0.1505938 0.1508133 0.1509569 0.1512396 0.1514625 0.1516195 0.1516156 0.1515561 0.1514966 0.1513976 0.1512612 0.151076 0.1510596 0.1510431 0.151077 0.1511168 0.1511917 0.151023 0.1508045 0.1505885 0.1503493 0.1502373 0.1501726 0.1500762 0.1500065 0.1499782 0.150057 0.1502658 0.150469 0.1505335 0.1505505 0.1505328 0.1504275 0.1502438 0.1499674 0.1497118 0.1494661 0.1493102 0.1493681 0.1495501 0.1499738 0.1509654 0.155775 0.154484 0.1527379 0.1518718 0.1506028 0.1489256 0.147067 0.1447061 0.1436307 0.1443568 0.1451849 0.1455157 0.1452821 0.1445717 0.1439195 0.1435867 0.1436018 0.1438781 0.1442086 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8
+ </Nnet>
config.yaml ADDED
The diff for this file is too large to render. See raw diff
 
convert-tokens.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ import sys
3
+ from typing import Dict
4
+
5
+
6
+ def load_tokens():
7
+ ans = dict()
8
+ i = 0
9
+ with open("tokens.txt", encoding="utf-8") as f:
10
+ for line in f:
11
+ if len(line.strip().split()) == 2:
12
+ sys.exit("Already converted!\nExiting")
13
+
14
+ ans[i] = line.strip()
15
+ i += 1
16
+ return ans
17
+
18
+
19
+ def write_tokens(tokens: Dict[int, str]):
20
+ with open("new_tokens.txt", "w", encoding="utf-8") as f:
21
+ for idx, s in tokens.items():
22
+ f.write(f"{s} {idx}\n")
23
+
24
+
25
+ def main():
26
+ tokens = load_tokens()
27
+ write_tokens(tokens)
28
+
29
+
30
+ if __name__ == "__main__":
31
+ main()
jfk.wav ADDED
Binary file (352 kB). View file
 
model.int8.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2a63ecbd790d027e8554995d7f5d66b7e1629d360a2d1be947263de973c8b913
3
+ size 241595433
model.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6e89731feec68c775f8f756acd960e72ef17c8c87feec9c7b112cbeb0bcff75
3
+ size 868256686
new_tokens.txt ADDED
The diff for this file is too large to render. See raw diff
 
quantize-model.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+
3
+ import onnx
4
+ from onnxruntime.quantization import QuantType, quantize_dynamic
5
+
6
+
7
+ def main():
8
+ onnx_model = onnx.load("model.onnx")
9
+ nodes = [n.name for n in onnx_model.graph.node]
10
+ nodes_to_exclude = [m for m in nodes if "output" in m]
11
+ print(nodes_to_exclude)
12
+ quantize_dynamic(
13
+ model_input="model.onnx",
14
+ model_output="model.int8.onnx",
15
+ op_types_to_quantize=["MatMul"],
16
+ per_channel=True,
17
+ weight_type=QuantType.QUInt8,
18
+ nodes_to_exclude=nodes_to_exclude,
19
+ )
20
+
21
+
22
+ if __name__ == "__main__":
23
+ main()
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ kaldi-native-fbank
2
+ librosa
3
+ onnxruntime
test-paraformer-onnx.py ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ #!/usr/bin/env python3
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+
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+ # Copyright (c) 2023 Xiaomi Corporation
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+ # Author: Fangjun Kuang
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+
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+ import kaldi_native_fbank as knf
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+ import librosa
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+ import numpy as np
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+ import onnxruntime
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+
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+
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+ def load_cmvn():
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+ neg_mean = None
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+ inv_std = None
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+
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+ with open("am.mvn") as f:
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+ for line in f:
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+ if not line.startswith("<LearnRateCoef>"):
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+ continue
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+ t = line.split()[3:-1]
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+ t = list(map(lambda x: float(x), t))
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+
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+ if neg_mean is None:
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+ neg_mean = np.array(t, dtype=np.float32)
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+ else:
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+ inv_std = np.array(t, dtype=np.float32)
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+
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+ return neg_mean, inv_std
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+
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+
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+ def compute_feat():
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+ sample_rate = 16000
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+ samples, _ = librosa.load("jfk.wav", sr=sample_rate)
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+ opts = knf.FbankOptions()
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+ opts.frame_opts.dither = 0
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+ opts.frame_opts.snip_edges = False
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+ opts.frame_opts.samp_freq = sample_rate
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+ opts.mel_opts.num_bins = 80
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+
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+ online_fbank = knf.OnlineFbank(opts)
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+ online_fbank.accept_waveform(sample_rate, (samples * 32768).tolist())
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+ online_fbank.input_finished()
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+
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+ features = np.stack(
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+ [online_fbank.get_frame(i) for i in range(online_fbank.num_frames_ready)]
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+ )
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+ assert features.data.contiguous is True
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+ assert features.dtype == np.float32, features.dtype
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+
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+ window_size = 7 # lfr_m
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+ window_shift = 6 # lfr_n
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+
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+ T = (features.shape[0] - window_size) // window_shift + 1
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+ features = np.lib.stride_tricks.as_strided(
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+ features,
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+ shape=(T, features.shape[1] * window_size),
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+ strides=((window_shift * features.shape[1]) * 4, 4),
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+ )
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+ neg_mean, inv_std = load_cmvn()
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+ features = (features + neg_mean) * inv_std
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+ return features
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+
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+
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+ # tokens.txt in paraformer has only one column
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+ # while it has two columns ins sherpa-onnx.
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+ # This function can handle tokens.txt from both paraformer and sherpa-onnx
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+ def load_tokens():
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+ ans = dict()
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+ i = 0
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+ with open("tokens.txt", encoding="utf-8") as f:
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+ for line in f:
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+ ans[i] = line.strip().split()[0]
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+ i += 1
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+ return ans
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+
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+
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+ def main():
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+ features = compute_feat()
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+ features = np.expand_dims(features, axis=0)
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+ features_length = np.array([features.shape[1]], dtype=np.int32)
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+
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+ session_opts = onnxruntime.SessionOptions()
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+ session_opts.log_severity_level = 3 # error level
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+ sess = onnxruntime.InferenceSession("model.onnx", session_opts)
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+
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+ inputs = {
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+ "speech": features,
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+ "speech_lengths": features_length,
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+ }
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+ output_names = ["logits"]
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+
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+ try:
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+ outputs = sess.run(output_names, input_feed=inputs)
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+ except ONNXRuntimeError:
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+ print("Input wav is silence or noise")
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+ return
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+
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+ log_probs = outputs[0].squeeze(0)
99
+ y = log_probs.argmax(axis=-1)
100
+
101
+ tokens = load_tokens()
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+ text = "".join([tokens[i] for i in y if i not in (0, 2)])
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+ print(text)
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+
105
+
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+ if __name__ == "__main__":
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+ main()
tokens.txt ADDED
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