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import os |
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import re |
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from pathlib import Path |
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from typing import List |
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BASE_URL = "https://huggingface.co/csukuangfj/sherpa-onnx-apk/resolve/main/" |
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from dataclasses import dataclass |
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@dataclass |
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class APK: |
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major: int |
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minor: int |
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patch: int |
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arch: str |
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short_name: str |
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def __init__(self, s): |
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s = str(s)[len("vad-asr/") :] |
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split = s.split("-") |
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self.major, self.minor, self.patch = list(map(int, split[2].split("."))) |
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self.arch = split[3] |
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self.lang = split[5] |
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self.short_name = split[6] |
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if "arm" in s: |
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self.arch += "-" + split[4] |
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self.lang = split[6] |
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self.short_name = split[7] |
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if "armeabi" in self.arch: |
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self.arch = "y" + self.arch |
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if "arm64" in self.arch: |
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self.arch = "z" + self.arch |
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|
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if "small" in self.short_name: |
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self.short_name = "zzz" + self.short_name |
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def sort_by_apk(x): |
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x = APK(x) |
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return (x.major, x.minor, x.patch, x.arch, x.lang, x.short_name) |
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def generate_url(files: List[str]) -> List[str]: |
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ans = [] |
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base = BASE_URL |
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for f in files: |
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ans.append(base + str(f)) |
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return ans |
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def get_all_files(d: str, suffix: str) -> List[str]: |
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ans = sorted(Path(d).glob(suffix), key=sort_by_apk, reverse=True) |
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return list(map(lambda x: BASE_URL + str(x), ans)) |
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def to_file(filename: str, files: List[str]): |
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content = r""" |
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<h1> APKs for VAD + non-streaming speech recognition </h1> |
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This page lists the <strong>VAD + non-streaming speech recognition</strong> APKs for <a href="http://github.com/k2-fsa/sherpa-onnx">sherpa-onnx</a>, |
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one of the deployment frameworks of <a href="https://github.com/k2-fsa">the Next-gen Kaldi project</a>. |
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<br/> |
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The name of an APK has the following rule: |
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<ul> |
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<li> sherpa-onnx-{version}-{arch}-vad_asr-{lang}-{model}.apk |
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</ul> |
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where |
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<ul> |
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<li> version: It specifies the current version, e.g., 1.9.23 |
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<li> arch: The architecture targeted by this APK, e.g., arm64-v8a, armeabi-v7a, x86_64, x86 |
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<li> lang: The lang of the model used in the APK, e.g., en for English, zh for Chinese |
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<li> model: The name of the model used in the APK |
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</ul> |
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|
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<br/> |
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|
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You can download all supported models from |
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<a href="https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models">https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models</a> |
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|
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<br/> |
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<br/> |
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|
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<strong>Note about the license</strong> The code of Next-gen Kaldi is using |
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<a href="https://www.apache.org/licenses/LICENSE-2.0">Apache-2.0 license</a>. However, |
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we support models from different frameworks. Please check the license of your selected model. |
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|
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<br/> |
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<br/> |
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|
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<!-- |
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see https://www.tablesgenerator.com/html_tables# |
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--> |
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<style type="text/css"> |
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.tg {border-collapse:collapse;border-spacing:0;} |
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.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px; |
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overflow:hidden;padding:10px 5px;word-break:normal;} |
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.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px; |
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font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;} |
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.tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top} |
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.tg .tg-0lax{text-align:left;vertical-align:top} |
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</style> |
|
<table class="tg"> |
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<thead> |
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<tr> |
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<th class="tg-0pky">APK</th> |
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<th class="tg-0lax">Comment</th> |
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<th class="tg-0pky">VAD model</th> |
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<th class="tg-0pky">Non-streaming ASR model</th> |
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</tr> |
|
</thead> |
|
<tbody> |
|
<tr> |
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<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-telespeech.apk</td> |
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<td class="tg-0lax">支持非常多种中文方言. It is converted from <a href="https://github.com/Tele-AI/TeleSpeech-ASR">https://github.com/Tele-AI/TeleSpeech-ASR</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2">sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2</a></td> |
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</tr> |
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<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-th-zipformer.apk</td> |
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<td class="tg-0lax">It supports only Thai. It is converted from <a href="https://huggingface.co/yfyeung/icefall-asr-gigaspeech2-th-zipformer-2024-06-20/tree/main">https://huggingface.co/yfyeung/icefall-asr-gigaspeech2-th-zipformer-2024-06-20/tree/main</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-thai-2024-06-20.tar.bz2">sherpa-onnx-zipformer-thai-2024-06-20.tar.bz2</a></td> |
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</tr> |
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<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ko-zipformer.apk</td> |
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<td class="tg-0lax">It supports only Korean. It is converted from <a href="https://huggingface.co/johnBamma/icefall-asr-ksponspeech-zipformer-2024-06-24">https://huggingface.co/johnBamma/icefall-asr-ksponspeech-zipformer-2024-06-24</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-korean-2024-06-24.tar.bz2">sherpa-onnx-zipformer-korean-2024-06-24.tar.bz2</a></td> |
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</tr> |
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<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-be_de_en_es_fr_hr_it_pl_ru_uk-fast_conformer_ctc_20k.apk</td> |
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<td class="tg-0lax">It supports <span style="color:red;">10 languages</span>: Belarusian, German, English, Spanish, French, Croatian, Italian, Polish, Russian, and Ukrainian. It is converted from <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc">STT Multilingual FastConformer Hybrid Transducer-CTC Large P&C</a> from <a href="https://github.com/NVIDIA/NeMo/">NVIDIA/NeMo</a>. Note that only the CTC branch is used. It is trained on ~20000 hours of data.</td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k.tar.bz2">sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k.tar.bz2</a></td> |
|
</tr> |
|
<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en_des_es_fr-fast_conformer_ctc_14288.apk</td> |
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<td class="tg-0lax">It supports <span style="color:red;">4 languages</span>: German, English, Spanish, and French . It is converted from <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu">STT European FastConformer Hybrid Transducer-CTC Large P&C</a> from <a href="https://github.com/NVIDIA/NeMo/">NVIDIA/NeMo</a>. Note that only the CTC branch is used. It is trained on 14288 hours of data.</td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-fast-conformer-transducer-en-de-es-fr-14288.tar.bz2">sherpa-onnx-nemo-fast-conformer-transducer-en-de-es-fr-14288.tar.bz2</a></td> |
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</tr> |
|
<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-es-fast_conformer_ctc_1424.apk</td> |
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<td class="tg-0lax">It supports only Spanish. It is converted from <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_fastconformer_hybrid_large_pc">STT Es FastConformer Hybrid Transducer-CTC Large P&C</a> from <a href="https://github.com/NVIDIA/NeMo/">NVIDIA/NeMo</a>. Note that only the CTC branch is used. It is trained on 1424 hours of data.</td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-fast-conformer-transducer-es-1424.tar.bz2">sherpa-onnx-nemo-fast-conformer-transducer-es-1424.tar.bz2</a></td> |
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</tr> |
|
<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en-fast_conformer_ctc_24500.apk</td> |
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<td class="tg-0lax">It supports only English. It is converted from <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_pc">STT En FastConformer Hybrid Transducer-CTC Large P&C</a> from <a href="https://github.com/NVIDIA/NeMo/">NVIDIA/NeMo</a>. Note that only the CTC branch is used. It is trained on 8500 hours of data.</td> |
|
<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-fast-conformer-transducer-en-24500.tar.bz2">sherpa-onnx-nemo-fast-conformer-transducer-en-24500.tar.bz2</a></td> |
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</tr> |
|
<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-zipformer.apk</td> |
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<td class="tg-0lax">It supports only Chinese.</td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
|
<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/icefall-asr-zipformer-wenetspeech-20230615.tar.bz2">icefall-asr-zipformer-wenetspeech-20230615</a></td> |
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</tr> |
|
<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-paraformer.apk</td> |
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<td class="tg-0lax"><span style="font-weight:400;font-style:normal">It supports both Chinese and English.</span></td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
|
<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-paraformer-zh-2023-03-28.tar.bz2">sherpa-onnx-paraformer-zh-2023-03-28</a></td> |
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</tr> |
|
<tr> |
|
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en-whisper_tiny.apk</td> |
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<td class="tg-0lax">It supports only English.</td> |
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<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx">silero_vad.onnx</a></td> |
|
<td class="tg-0pky"><a href="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2">sherpa-onnx-whisper-tiny.en</a></td> |
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</tr> |
|
</tbody> |
|
</table> |
|
|
|
<br/> |
|
<br/> |
|
|
|
<div/> |
|
""" |
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if "-cn" not in filename: |
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content += """ |
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For Chinese users, please <a href="./apk-asr-cn.html">visit this address</a>, |
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which replaces <a href="huggingface.co">huggingface.co</a> with <a href="hf-mirror.com">hf-mirror.com</a> |
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<br/> |
|
<br/> |
|
中国用户, 请访问<a href="./apk-asr-cn.html">这个地址</a> |
|
<br/> |
|
<br/> |
|
""" |
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|
|
with open(filename, "w") as f: |
|
print(content, file=f) |
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for x in files: |
|
name = x.rsplit("/", maxsplit=1)[-1] |
|
print(f'<a href="{x}" />{name}<br/>', file=f) |
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|
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|
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def main(): |
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apk = get_all_files("vad-asr", suffix="*.apk") |
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to_file("./apk-vad-asr.html", apk) |
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apk2 = [] |
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for a in apk: |
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a = a.replace("huggingface.co", "hf-mirror.com") |
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a = a.replace("resolve", "blob") |
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apk2.append(a) |
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to_file("./apk-vad-asr-cn.html", apk2) |
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|
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if __name__ == "__main__": |
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main() |
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|
