File size: 6,549 Bytes
2267fac
 
 
 
 
ac87612
2267fac
 
 
d03c698
2267fac
 
 
 
 
 
 
 
 
 
 
168b5c0
2267fac
f0ff987
d03c698
f392af0
d03c698
59cce29
 
 
 
 
 
 
 
 
 
 
 
 
 
7903607
 
 
 
 
 
f392af0
7903607
59cce29
 
 
 
 
 
 
 
 
 
 
d03c698
 
 
 
 
 
59cce29
 
 
 
 
 
 
 
 
 
2267fac
 
 
 
 
168b5c0
 
2267fac
 
 
 
168b5c0
2267fac
168b5c0
2267fac
 
 
 
168b5c0
2267fac
168b5c0
2267fac
 
 
168b5c0
2267fac
 
8dc832e
4281a4a
2267fac
 
3194abe
2267fac
 
f392af0
2267fac
f392af0
2267fac
 
 
 
 
 
 
 
 
 
 
 
 
 
26dfa9a
2267fac
 
 
d03c698
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
168b5c0
 
 
2267fac
ac87612
d03c698
3194abe
168b5c0
2267fac
 
 
 
168b5c0
 
2267fac
ac87612
2267fac
 
ac87612
 
 
d03c698
168b5c0
 
 
 
 
 
d03c698
 
 
 
 
 
168b5c0
d03c698
168b5c0
2267fac
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from enum import Enum
from functools import lru_cache
import os
from pathlib import Path

import huggingface_hub
import sherpa
import sherpa_onnx


class EnumDecodingMethod(Enum):
    greedy_search = "greedy_search"
    modified_beam_search = "modified_beam_search"


model_map = {
    "Chinese": [
        {
            "repo_id": "csukuangfj/wenet-chinese-model",
            "nn_model_file": "final.zip",
            "tokens_file": "units.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer",
            "normalize_samples": False,
        },
        {
            "repo_id": "csukuangfj/sherpa-onnx-paraformer-zh-2024-03-09",
            "nn_model_file": "model.int8.onnx",
            "tokens_file": "tokens.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer_from_paraformer",
        },
        {
            "repo_id": "csukuangfj/sherpa-onnx-paraformer-zh-small-2024-03-09",
            "nn_model_file": "model.int8.onnx",
            "tokens_file": "tokens.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer_from_paraformer",
        },
        {
            "repo_id": "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
            "nn_model_file": "cpu_jit_epoch_10_avg_2_torch_1.7.1.pt",
            "tokens_file": "tokens.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer",
            "normalize_samples": True,
        }
    ],
    "English": [
        {
            "repo_id": "csukuangfj/sherpa-onnx-paraformer-en-2024-03-09",
            "nn_model_file": "model.int8.onnx",
            "tokens_file": "tokens.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer_from_paraformer",
        },
    ],
    "Chinese+English": [
        {
            "repo_id": "csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28",
            "nn_model_file": "model.int8.onnx",
            "tokens_file": "tokens.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer_from_paraformer",
        },
    ],
    "Chinese+Cantonese+English": [
        {
            "repo_id": "csukuangfj/sherpa-onnx-paraformer-trilingual-zh-cantonese-en",
            "nn_model_file": "model.int8.onnx",
            "tokens_file": "tokens.txt",
            "sub_folder": ".",
            "loader": "load_sherpa_offline_recognizer_from_paraformer",
        },
    ]
}


def download_model(repo_id: str,
                   nn_model_file: str,
                   tokens_file: str,
                   sub_folder: str,
                   local_model_dir: str,
                   ):

    nn_model_file = huggingface_hub.hf_hub_download(
        repo_id=repo_id,
        filename=nn_model_file,
        subfolder=sub_folder,
        local_dir=local_model_dir,
    )

    tokens_file = huggingface_hub.hf_hub_download(
        repo_id=repo_id,
        filename=tokens_file,
        subfolder=sub_folder,
        local_dir=local_model_dir,
    )
    return nn_model_file, tokens_file


def load_sherpa_offline_recognizer(nn_model_file: str,
                                   tokens_file: str,
                                   sample_rate: int = 16000,
                                   num_active_paths: int = 2,
                                   decoding_method: str = "greedy_search",
                                   num_mel_bins: int = 80,
                                   frame_dither: int = 0,
                                   normalize_samples: bool = False,
                                   ):
    feat_config = sherpa.FeatureConfig(normalize_samples=normalize_samples)
    feat_config.fbank_opts.frame_opts.samp_freq = sample_rate
    feat_config.fbank_opts.mel_opts.num_bins = num_mel_bins
    feat_config.fbank_opts.frame_opts.dither = frame_dither

    config = sherpa.OfflineRecognizerConfig(
        nn_model=nn_model_file,
        tokens=tokens_file,
        use_gpu=False,
        feat_config=feat_config,
        decoding_method=decoding_method,
        num_active_paths=num_active_paths,
    )

    recognizer = sherpa.OfflineRecognizer(config)

    return recognizer


def load_sherpa_offline_recognizer_from_paraformer(nn_model_file: str,
                                                   tokens_file: str,
                                                   sample_rate: int = 16000,
                                                   decoding_method: str = "greedy_search",
                                                   feature_dim: int = 80,
                                                   num_threads: int = 2,
                                                   ):
    recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
        paraformer=nn_model_file,
        tokens=tokens_file,
        num_threads=num_threads,
        sample_rate=sample_rate,
        feature_dim=feature_dim,
        decoding_method=decoding_method,
        debug=False,
    )
    return recognizer


def load_recognizer(repo_id: str,
                    nn_model_file: str,
                    tokens_file: str,
                    sub_folder: str,
                    local_model_dir: Path,
                    loader: str,
                    decoding_method: str = "greedy_search",
                    num_active_paths: int = 4,
                    ):
    if not os.path.exists(local_model_dir):
        download_model(
            repo_id=repo_id,
            nn_model_file=nn_model_file,
            tokens_file=tokens_file,
            sub_folder=sub_folder,
            local_model_dir=local_model_dir.as_posix(),
        )

    nn_model_file = (local_model_dir / nn_model_file).as_posix()
    tokens_file = (local_model_dir / tokens_file).as_posix()

    if loader == "load_sherpa_offline_recognizer":
        recognizer = load_sherpa_offline_recognizer(
            nn_model_file=nn_model_file,
            tokens_file=tokens_file,
            decoding_method=decoding_method,
            num_active_paths=num_active_paths,
        )
    elif loader == "load_sherpa_offline_recognizer_from_paraformer":
        recognizer = load_sherpa_offline_recognizer_from_paraformer(
            nn_model_file=nn_model_file,
            tokens_file=tokens_file,
            decoding_method=decoding_method,
        )
    else:
        raise NotImplementedError("loader not support: {}".format(loader))
    return recognizer


if __name__ == "__main__":
    pass