#!/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"""
APK | Comment | VAD model | Non-streaming ASR model |
---|---|---|---|
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ja-zipformer_reazonspeech.apk | It supports only Japanese. It is from https://github.com/reazon-research/ReazonSpeech | silero_vad.onnx | sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh_en_ko_ja_yue-sense_voice.apk | It supports Chinese, Cantonese, English, Korean, and Japanese (中、英、粤、日、韩5种语音). It is converted from https://github.com/FunAudioLLM/SenseVoice | silero_vad.onnx | sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-telespeech.apk | 支持非常多种中文方言. It is converted from https://github.com/Tele-AI/TeleSpeech-ASR | silero_vad.onnx | sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-th-zipformer.apk | It supports only Thai. It is converted from https://huggingface.co/yfyeung/icefall-asr-gigaspeech2-th-zipformer-2024-06-20/tree/main | silero_vad.onnx | sherpa-onnx-zipformer-thai-2024-06-20.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ko-zipformer.apk | It supports only Korean. It is converted from https://huggingface.co/johnBamma/icefall-asr-ksponspeech-zipformer-2024-06-24 | silero_vad.onnx | sherpa-onnx-zipformer-korean-2024-06-24.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-be_de_en_es_fr_hr_it_pl_ru_uk-fast_conformer_ctc_20k.apk | It supports 10 languages: Belarusian, German, English, Spanish, French, Croatian, Italian, Polish, Russian, and Ukrainian. It is converted from STT Multilingual FastConformer Hybrid Transducer-CTC Large P&C from NVIDIA/NeMo. Note that only the CTC branch is used. It is trained on ~20000 hours of data. | silero_vad.onnx | sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en_des_es_fr-fast_conformer_ctc_14288.apk | It supports 4 languages: German, English, Spanish, and French . It is converted from STT European FastConformer Hybrid Transducer-CTC Large P&C from NVIDIA/NeMo. Note that only the CTC branch is used. It is trained on 14288 hours of data. | silero_vad.onnx | sherpa-onnx-nemo-fast-conformer-transducer-en-de-es-fr-14288.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-es-fast_conformer_ctc_1424.apk | It supports only Spanish. It is converted from STT Es FastConformer Hybrid Transducer-CTC Large P&C from NVIDIA/NeMo. Note that only the CTC branch is used. It is trained on 1424 hours of data. | silero_vad.onnx | sherpa-onnx-nemo-fast-conformer-transducer-es-1424.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en-fast_conformer_ctc_24500.apk | It supports only English. It is converted from STT En FastConformer Hybrid Transducer-CTC Large P&C from NVIDIA/NeMo. Note that only the CTC branch is used. It is trained on 8500 hours of data. | silero_vad.onnx | sherpa-onnx-nemo-fast-conformer-transducer-en-24500.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-zipformer.apk | It supports only Chinese. | silero_vad.onnx | icefall-asr-zipformer-wenetspeech-20230615 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-zh-paraformer.apk | It supports both Chinese and English. | silero_vad.onnx | sherpa-onnx-paraformer-zh-2023-03-28 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-en-whisper_tiny.apk | It supports only English. | silero_vad.onnx | sherpa-onnx-whisper-tiny.en |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ru-nemo_transducer_giga_am.apk | It supports only Russian. | silero_vad.onnx | sherpa-onnx-nemo-transducer-giga-am-russian-2024-10-24.tar.bz2 Please see also https://github.com/salute-developers/GigaAM |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ru-nemo_ctc_giga_am.apk | It supports only Russian. | silero_vad.onnx | sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24.tar.bz2 Please see also https://github.com/salute-developers/GigaAM |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ru-small_zipformer.apk | It supports only Russian. | silero_vad.onnx | sherpa-onnx-small-zipformer-ru-2024-09-18.tar.bz2 |
sherpa-onnx-x.y.z-arm64-v8a-vad_asr-ru-zipformer.apk | It supports only Russian. | silero_vad.onnx | sherpa-onnx-zipformer-ru-2024-09-18.tar.bz2 |