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Project description
Supported functions
Speech recognition | Speech synthesis |
---|---|
✔️ | ✔️ |
Speaker identification | Speaker diarization | Speaker verification |
---|---|---|
✔️ | ✔️ | ✔️ |
Spoken Language identification | Audio tagging | Voice activity detection |
---|---|---|
✔️ | ✔️ | ✔️ |
Keyword spotting | Add punctuation |
---|---|
✔️ | ✔️ |
Supported platforms
Architecture | Android | iOS | Windows | macOS | linux |
---|---|---|---|---|---|
x64 | ✔️ | ✔️ | ✔️ | ✔️ | |
x86 | ✔️ | ✔️ | |||
arm64 | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
arm32 | ✔️ | ✔️ | |||
riscv64 | ✔️ |
Supported programming languages
1. C++ | 2. C | 3. Python | 4. JavaScript |
---|---|---|---|
✔️ | ✔️ | ✔️ | ✔️ |
5. Java | 6. C# | 7. Kotlin | 8. Swift |
---|---|---|---|
✔️ | ✔️ | ✔️ | ✔️ |
9. Go | 10. Dart | 11. Rust | 12. Pascal |
---|---|---|---|
✔️ | ✔️ | ✔️ | ✔️ |
For Rust support, please see sherpa-rs
It also supports WebAssembly.
Introduction
This repository supports running the following functions locally
- Speech-to-text (i.e., ASR); both streaming and non-streaming are supported
- Text-to-speech (i.e., TTS)
- Speaker diarization
- Speaker identification
- Speaker verification
- Spoken language identification
- Audio tagging
- VAD (e.g., silero-vad)
- Keyword spotting
on the following platforms and operating systems:
- x86,
x86_64
, 32-bit ARM, 64-bit ARM (arm64, aarch64), RISC-V (riscv64) - Linux, macOS, Windows, openKylin
- Android, WearOS
- iOS
- NodeJS
- WebAssembly
- Raspberry Pi
- RV1126
- LicheePi4A
- VisionFive 2
- 旭日X3派
- 爱芯派
- etc
with the following APIs
- C++, C, Python, Go,
C#
- Java, Kotlin, JavaScript
- Swift, Rust
- Dart, Object Pascal
Links for Huggingface Spaces
You can visit the following Huggingface spaces to try sherpa-onnx without installing anything. All you need is a browser.
Description | URL |
---|---|
Speaker diarization | Click me |
Speech recognition | Click me |
Speech recognition with Whisper | Click me |
Speech synthesis | Click me |
Generate subtitles | Click me |
Audio tagging | Click me |
Spoken language identification with Whisper | Click me |
We also have spaces built using WebAssembly. They are listed below:
Description | Huggingface space | ModelScope space |
---|---|---|
Voice activity detection with silero-vad | Click me | 地址 |
Real-time speech recognition (Chinese + English) with Zipformer | Click me | 地址 |
Real-time speech recognition (Chinese + English) with Paraformer | Click me | 地址 |
Real-time speech recognition (Chinese + English + Cantonese) with Paraformer-large | Click me | 地址 |
Real-time speech recognition (English) | Click me | 地址 |
VAD + speech recognition (Chinese + English + Korean + Japanese + Cantonese) with SenseVoice | Click me | 地址 |
VAD + speech recognition (English) with Whisper tiny.en | Click me | 地址 |
VAD + speech recognition (English) with Zipformer trained with GigaSpeech | Click me | 地址 |
VAD + speech recognition (Chinese) with Zipformer trained with WenetSpeech | Click me | 地址 |
VAD + speech recognition (Japanese) with Zipformer trained with ReazonSpeech | Click me | 地址 |
VAD + speech recognition (Thai) with Zipformer trained with GigaSpeech2 | Click me | 地址 |
VAD + speech recognition (Chinese 多种方言) with a TeleSpeech-ASR CTC model | Click me | 地址 |
VAD + speech recognition (English + Chinese, 及多种中文方言) with Paraformer-large | Click me | 地址 |
VAD + speech recognition (English + Chinese, 及多种中文方言) with Paraformer-small | Click me | 地址 |
Speech synthesis (English) | Click me | 地址 |
Speech synthesis (German) | Click me | 地址 |
Speaker diarization | Click me | 地址 |
Links for pre-built Android APKs
You can find pre-built Android APKs for this repository in the following table
Description | URL | 中国用户 |
---|---|---|
Speaker diarization | Address | 点此 |
Streaming speech recognition | Address | 点此 |
Text-to-speech | Address | 点此 |
Voice activity detection (VAD) | Address | 点此 |
VAD + non-streaming speech recognition | Address | 点此 |
Two-pass speech recognition | Address | 点此 |
Audio tagging | Address | 点此 |
Audio tagging (WearOS) | Address | 点此 |
Speaker identification | Address | 点此 |
Spoken language identification | Address | 点此 |
Keyword spotting | Address | 点此 |
Links for pre-built Flutter APPs
Real-time speech recognition
Description | URL | 中国用户 |
---|---|---|
Streaming speech recognition | Address | 点此 |
Text-to-speech
Description | URL | 中国用户 |
---|---|---|
Android (arm64-v8a, armeabi-v7a, x86_64) | Address | 点此 |
Linux (x64) | Address | 点此 |
macOS (x64) | Address | 点此 |
macOS (arm64) | Address | 点此 |
Windows (x64) | Address | 点此 |
Note: You need to build from source for iOS.
Links for pre-built Lazarus APPs
Links for pre-trained models
Description | URL |
---|---|
Speech recognition (speech to text, ASR) | Address |
Text-to-speech (TTS) | Address |
VAD | Address |
Keyword spotting | Address |
Audio tagging | Address |
Speaker identification (Speaker ID) | Address |
Spoken language identification (Language ID) | See multi-lingual Whisper ASR models from Speech recognition |
Punctuation | Address |
Speaker segmentation | Address |
Useful links
- Documentation: https://k2-fsa.github.io/sherpa/onnx/
- Bilibili 演示视频: https://search.bilibili.com/all?keyword=%E6%96%B0%E4%B8%80%E4%BB%A3Kaldi
How to reach us
Please see https://k2-fsa.github.io/sherpa/social-groups.html for 新一代 Kaldi 微信交流群 and QQ 交流群.
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Video demo in Chinese: 【开源】Windows实时字幕软件(网课/开会必备)
lol互动助手
It uses the JavaScript API of sherpa-onnx along with Electron
Video demo in Chinese: 爆了!炫神教你开打字挂!真正影响胜率的英雄联盟工具!英雄联盟的最后一块拼图!和游戏中的每个人无障碍沟通!
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SHA256 | 94e1729cfff16a3835517d07c873fa73946766e5219819eb6f84e15eed88d991 |
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MD5 | ffee59dddcc21217f03f71cd872b3b6a |
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BLAKE2b-256 | abd232f9a12f1bd64dd546161864bc837aaea7c51718cd151a9e49da2c2802b8 |
Hashes for sherpa_onnx-1.10.29-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 8528ddacee63c1dfa7b946398a112d18147caf9644c91ceb31e18a98d99b24de |
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MD5 | a917c386582eee09f2aba476e16376ab |
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BLAKE2b-256 | 0a8d3b0ccee4a48781e7d2dbd66ecf41e11edef777979a5de830029799fba904 |
Hashes for sherpa_onnx-1.10.29-cp38-cp38-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | bff0d3721732b444985abbf2496544bfdc73a0ae1823d483f9418f2537a77be6 |
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MD5 | a1661341eb20295c5575e728720b7bab |
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BLAKE2b-256 | 0f3ef21652c6020408f042cdfe057e79e8f925c6a1caf1c5dc1836eddc381490 |
Hashes for sherpa_onnx-1.10.29-cp38-cp38-macosx_11_0_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3ed0afd0b9fbf47027ed6806d61a75db3fe66a8f4b921cacee486dfa65569929 |
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MD5 | 5fa7ae8598e67ea2c7a3a9775e5dcb32 |
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BLAKE2b-256 | 17a9c9fbee78039517a5d92c8f3333116947c902010c31758acac481f8e4bfdc |
Hashes for sherpa_onnx-1.10.29-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 010d395d39853382aeb33e1daaa43a27c886674efff333367ea4a0d31c679f48 |
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MD5 | 669de7e84e80b1c796fca1b4e22aadfd |
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BLAKE2b-256 | 5fa92d00ec6dbbe9762ba68baf12144e7855ca6497a81643e4a15b54abca907d |
Hashes for sherpa_onnx-1.10.29-cp38-cp38-linux_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a43a0f647e380b43ff178a28c5a452bf13087cc4eaa13d99bbd40e9c642a2d9f |
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MD5 | 985bc56741d1693e11b4cbbc9b219dcd |
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BLAKE2b-256 | 36394989840b0fd3f92a961233ad786c7dcc34525bc0f170197313c5b62e14ee |
Hashes for sherpa_onnx-1.10.29-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 69d7838b59e6e5308552091f2a10eb285ea2042d67108f09f04eb3578de4f6f3 |
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MD5 | b5801c7bff2987bc8ad8dbc0fbb47119 |
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BLAKE2b-256 | ec2d4ae589bac6d9a94b9b4b240f1afef114d1957b3889208b4bb3eeb86f5c82 |
Hashes for sherpa_onnx-1.10.29-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 940469d5499e6285f9debf03a13d61539f5c29364b84f7fbcdcc0733e59dd4b3 |
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MD5 | fd451c207a41a7fc90aa287fa06100fd |
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BLAKE2b-256 | 1b5db384499b26a134c1fea81e6bee4509c7e70ecbdeb5cfe51a1c68b6650bf9 |
Hashes for sherpa_onnx-1.10.29-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ed8314b77823704f8956606a0fc9e3fe7b275c26e3997e45f6cf9f3b6fb2e018 |
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MD5 | ce678c592f9ea2583bef8ced3403eca0 |
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BLAKE2b-256 | eaa1a5bed572c81efe5d69cb31bc6ed3a68b208368a0c238735dba658dca5b3b |
Hashes for sherpa_onnx-1.10.29-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bc40eee2499b10abcf268b4304b131ec0ff7e95a6f0bf6a5b66278fd930d5ca1 |
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MD5 | 3a99c1fb4ac68aaa7ea7dd7bb3bf259e |
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BLAKE2b-256 | 7bcc5e2ee294a14b47ab9b2e216054bfbe4e152c6c6f2f25c4e3ae63f8956f0d |
Hashes for sherpa_onnx-1.10.29-cp37-cp37m-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 886a0d867613543d25821eea059ee7f018d14546a9acfa7109d4407328623a24 |
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MD5 | 8a09e2b04a20740f74cae707f87831ae |
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BLAKE2b-256 | 220bdf18c2fe42186608a8fd2d3a7b42d7de119889786001826d76a75026a367 |
Hashes for sherpa_onnx-1.10.29-cp37-cp37m-linux_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6eea3b7b78ffef29deda009a7bed0f51d875265d95ac9f13392e7a27ab62572e |
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MD5 | 501fe6a2323570a9a55ec6f557a2273a |
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BLAKE2b-256 | a4cb2de66f1c9191a63df6c74cfed853a41043a817c10e10cc5975f420d14756 |