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#!/usr/bin/env python3
import os
import re
from pathlib import Path
from typing import List
BASE_URL = "https://huggingface.co/csukuangfj/sherpa-onnx-apk/resolve/main/"
from dataclasses import dataclass
@dataclass
class APK:
major: int
minor: int
patch: int
arch: str
short_name: str
def __init__(self, s):
# sherpa-onnx-1.9.23-arm64-v8a-vad_asr-en-whisper_tiny.apk
# sherpa-onnx-1.9.23-x86-vad_asr-en-whisper_tiny.apk
s = str(s)[len("vad-asr/") :]
split = s.split("-")
self.major, self.minor, self.patch = list(map(int, split[2].split(".")))
self.arch = split[3]
self.lang = split[5]
self.short_name = split[6]
if "arm" in s:
self.arch += "-" + split[4]
self.lang = split[6]
self.short_name = split[7]
if "armeabi" in self.arch:
self.arch = "y" + self.arch
if "arm64" in self.arch:
self.arch = "z" + self.arch
if "small" in self.short_name:
self.short_name = "zzz" + self.short_name
def sort_by_apk(x):
x = APK(x)
return (x.major, x.minor, x.patch, x.arch, x.lang, x.short_name)
def generate_url(files: List[str]) -> List[str]:
ans = []
base = BASE_URL
for f in files:
ans.append(base + str(f))
return ans
def get_all_files(d: str, suffix: str) -> List[str]:
ans = sorted(Path(d).glob(suffix), key=sort_by_apk, reverse=True)
return list(map(lambda x: BASE_URL + str(x), ans))
def to_file(filename: str, files: List[str]):
content = r"""
<h1> APKs for VAD + non-streaming speech recognition </h1>
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>,
one of the deployment frameworks of <a href="https://github.com/k2-fsa">the Next-gen Kaldi project</a>.
<br/>
The name of an APK has the following rule:
<ul>
<li> sherpa-onnx-{version}-{arch}-vad_asr-{lang}-{model}.apk
</ul>
where
<ul>
<li> version: It specifies the current version, e.g., 1.9.23
<li> arch: The architecture targeted by this APK, e.g., arm64-v8a, armeabi-v7a, x86_64, x86
<li> lang: The lang of the model used in the APK, e.g., en for English, zh for Chinese
<li> model: The name of the model used in the APK
</ul>
<br/>
You can download all supported models from
<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>
<br/>
<br/>
<!--
see https://www.tablesgenerator.com/html_tables#
-->
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}
.tg .tg-0lax{text-align:left;vertical-align:top}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-0pky">APK</th>
<th class="tg-0lax">Comment</th>
<th class="tg-0pky">VAD model</th>
<th class="tg-0pky">Non-streaming ASR model</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-telespeech.apk</td>
<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>
<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-telespeech-ctc-int8-zh-2024-06-04.tar.bz2">sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2</a></td>
</tr>
<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>
<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>
<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-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>
<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>
<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-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>
</tr>
<tr>
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-es-fast_conformer_ctc_1424.apk</td>
<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>
<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-nemo-fast-conformer-transducer-es-1424.tar.bz2">sherpa-onnx-nemo-fast-conformer-transducer-es-1424.tar.bz2</a></td>
</tr>
<tr>
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en-fast_conformer_ctc_24500.apk</td>
<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>
<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>
</tr>
<tr>
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-zipformer.apk</td>
<td class="tg-0lax">It supports only Chinese.</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>
<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>
</tr>
<tr>
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-paraformer.apk</td>
<td class="tg-0lax"><span style="font-weight:400;font-style:normal">It supports both Chinese and English.</span></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>
<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>
</tr>
<tr>
<td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en-whisper_tiny.apk</td>
<td class="tg-0lax">It supports only English.</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>
<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>
</tr>
</tbody>
</table>
<br/>
<br/>
<div/>
"""
if "-cn" not in filename:
content += """
For Chinese users, please <a href="./apk-asr-cn.html">visit this address</a>,
which replaces <a href="huggingface.co">huggingface.co</a> with <a href="hf-mirror.com">hf-mirror.com</a>
<br/>
<br/>
中国用户, 请访问<a href="./apk-asr-cn.html">这个地址</a>
<br/>
<br/>
"""
with open(filename, "w") as f:
print(content, file=f)
for x in files:
name = x.rsplit("/", maxsplit=1)[-1]
print(f'<a href="{x}" />{name}<br/>', file=f)
def main():
apk = get_all_files("vad-asr", suffix="*.apk")
to_file("./apk-vad-asr.html", apk)
# for Chinese users
apk2 = []
for a in apk:
a = a.replace("huggingface.co", "hf-mirror.com")
a = a.replace("resolve", "blob")
apk2.append(a)
to_file("./apk-vad-asr-cn.html", apk2)
if __name__ == "__main__":
main()
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