generate-vad-asr.py

#!/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)
        s = s.split("/")[-1]
        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 get_all_files(d_list: List[str], suffix: str) -> List[str]:
    if isinstance(d_list, str):
        d_list = [d_list]
    min_major = 1
    min_minor = 9
    min_patch = 10

    ss = []
    for d in d_list:
        for root, _, files in os.walk(d):
            for f in files:
                if f.endswith(suffix):
                    major, minor, patch = list(map(int, f.split("-")[2].split(".")))
                    if major >= min_major and minor >= min_minor and patch >= min_patch:
                        ss.append(os.path.join(root, f))

    ans = sorted(ss, 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/>

<strong>Note about the license</strong> The code of Next-gen Kaldi is using
<a href="https://www.apache.org/licenses/LICENSE-2.0">Apache-2.0 license</a>. However,
we support models from different frameworks. Please check the license of your selected model.

<br/>
<br/>

<!--
see https://www.tablesgenerator.com/html_tables#
-->

<style type="text/css">
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<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-th-zipformer.apk</td>
    <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>
    <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-zipformer-thai-2024-06-20.tar.bz2">sherpa-onnx-zipformer-thai-2024-06-20.tar.bz2</a></td>
  </tr>
  <tr>
    <td class="tg-0pky">sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ko-zipformer.apk</td>
    <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>
    <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-zipformer-korean-2024-06-24.tar.bz2">sherpa-onnx-zipformer-korean-2024-06-24.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()