File size: 6,374 Bytes
6b31279
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
39b3b3e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6b31279
39b3b3e
 
 
6b31279
39b3b3e
 
6b31279
39b3b3e
 
 
 
 
6b31279
39b3b3e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6b31279
39b3b3e
6b31279
 
 
 
 
 
 
 
09d9587
 
39b3b3e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09d9587
 
39b3b3e
09d9587
39b3b3e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09d9587
39b3b3e
09d9587
 
 
 
 
 
 
 
39b3b3e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from huggingface_hub import hf_hub_download
from functools import lru_cache


from offline_asr import OfflineAsr

sample_rate = 16000


@lru_cache(maxsize=30)
def get_pretrained_model(repo_id: str) -> OfflineAsr:
    if repo_id in chinese_models:
        return chinese_models[repo_id](repo_id)
    elif repo_id in english_models:
        return english_models[repo_id](repo_id)
    elif repo_id in chinese_english_mixed_models:
        chinese_english_mixed_models[repo_id](repo_id)
    else:
        raise ValueError(f"Unsupported repo_id: {repo_id}")


def _get_nn_model_filename(
    repo_id: str,
    filename: str,
    subfolder: str = "exp",
) -> str:
    nn_model_filename = hf_hub_download(
        repo_id=repo_id,
        filename=filename,
        subfolder=subfolder,
    )
    return nn_model_filename


def _get_bpe_model_filename(
    repo_id: str,
    filename: str = "bpe.model",
    subfolder: str = "data/lang_bpe_500",
) -> str:
    bpe_model_filename = hf_hub_download(
        repo_id=repo_id,
        filename=filename,
        subfolder=subfolder,
    )
    return bpe_model_filename


def _get_token_filename(
    repo_id: str,
    filename: str = "tokens.txt",
    subfolder: str = "data/lang_char",
) -> str:
    token_filename = hf_hub_download(
        repo_id=repo_id,
        filename=filename,
        subfolder=subfolder,
    )
    return token_filename


@lru_cache(maxsize=10)
def _get_aishell2_pretrained_model(repo_id: str) -> OfflineAsr:
    assert repo_id in [
        # context-size 1
        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12",  # noqa
        # context-size 2
        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12",  # noqa
    ]

    nn_model_filename = _get_nn_model_filename(
        repo_id=repo_id,
        filename="cpu_jit.pt",
    )
    token_filename = _get_token_filename(repo_id=repo_id)

    return OfflineAsr(
        nn_model_filename=nn_model_filename,
        bpe_model_filename=None,
        token_filename=token_filename,
        sample_rate=sample_rate,
        device="cpu",
    )


@lru_cache(maxsize=10)
def _get_gigaspeech_pre_trained_model(repo_id: str) -> OfflineAsr:
    assert repo_id in [
        "wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
    ]

    nn_model_filename = _get_nn_model_filename(
        # It is converted from https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2  # noqa
        repo_id="csukuangfj/icefall-asr-gigaspeech-pruned-transducer-stateless2",  # noqa
        filename="cpu_jit-epoch-29-avg-11-torch-1.10.0.pt",
    )
    bpe_model_filename = _get_bpe_model_filename(repo_id=repo_id)

    return OfflineAsr(
        nn_model_filename=nn_model_filename,
        bpe_model_filename=bpe_model_filename,
        token_filename=None,
        sample_rate=sample_rate,
        device="cpu",
    )


@lru_cache(maxsize=10)
def _get_librispeech_pre_trained_model(repo_id: str) -> OfflineAsr:
    assert repo_id in [
        "csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13",  # noqa
    ]

    nn_model_filename = _get_nn_model_filename(
        repo_id=repo_id,
        filename="cpu_jit.pt",
    )
    bpe_model_filename = _get_bpe_model_filename(repo_id=repo_id)

    return OfflineAsr(
        nn_model_filename=nn_model_filename,
        bpe_model_filename=bpe_model_filename,
        token_filename=None,
        sample_rate=sample_rate,
        device="cpu",
    )


@lru_cache(maxsize=10)
def _get_wenetspeech_pre_trained_model(repo_id: str):
    assert repo_id in [
        "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
    ]

    nn_model_filename = _get_nn_model_filename(
        repo_id=repo_id,
        filename="cpu_jit_epoch_10_avg_2_torch_1.7.1.pt",
    )
    token_filename = _get_token_filename(repo_id=repo_id)

    return OfflineAsr(
        nn_model_filename=nn_model_filename,
        bpe_model_filename=None,
        token_filename=token_filename,
        sample_rate=sample_rate,
        device="cpu",
    )


@lru_cache(maxsize=10)
def _get_tal_csasr_pre_trained_model(repo_id: str):
    assert repo_id in [
        "luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5",
    ]

    nn_model_filename = _get_nn_model_filename(
        repo_id=repo_id,
        filename="cpu_jit.pt",
    )
    token_filename = _get_token_filename(repo_id=repo_id)

    return OfflineAsr(
        nn_model_filename=nn_model_filename,
        bpe_model_filename=None,
        token_filename=token_filename,
        sample_rate=sample_rate,
        device="cpu",
    )


chinese_models = {
    "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12": _get_aishell2_pretrained_model,  # noqa
    "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12": _get_aishell2_pretrained_model,  # noqa
    "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2": _get_wenetspeech_pre_trained_model,  # noqa
}

english_models = {
    "wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2": _get_gigaspeech_pre_trained_model,  # noqa
    "csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13": _get_librispeech_pre_trained_model,  # noqa
}

chinese_english_mixed_models = {
    "luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5": _get_tal_csasr_pre_trained_model,  # noqa
}

all_models = {
    **chinese_models,
    **english_models,
    **chinese_english_mixed_models,
}

language_to_models = {
    "Chinese": sorted(chinese_models.keys()),
    "English": sorted(english_models.keys()),
    "Chinese+English": sorted(chinese_english_mixed_models.keys()),
}