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Project description
Supported functions
Speech recognition | Speech synthesis | Speaker verification | Speaker identification |
---|---|---|---|
✔️ | ✔️ | ✔️ | ✔️ |
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 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 |
---|---|
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 |
Links for pre-built Android APKs
Description | URL | 中国用户 |
---|---|---|
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
Generating subtitles
Description | URL | 中国用户 |
---|---|---|
Generate subtitles (生成字幕) | Address | 点此 |
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 |
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.
Project details
Release history Release notifications | RSS feed
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Algorithm | Hash digest | |
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SHA256 | 0e4f0d81ab4c49af1b90e271e99ae6c9c68ef7d7fccf695c9164f7e58578d7a7 |
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MD5 | 9cb16772b6346e885b584f4f56fba507 |
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BLAKE2b-256 | 804eb5bafc7996ec243c8a95534f716d28dd4a84f12a4b69eb23db3a95f5160b |
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Hashes for sherpa_onnx-1.10.25-cp38-cp38-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 637c74ad149eea1cf515a7c4f42fc81c22a4faa29404c65183dad86d6f7e6c76 |
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MD5 | 94056e1c4825fd17977eb3644f8804ff |
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BLAKE2b-256 | 1a61c89356ce9d5827a326ffd99171384402a3344c2bca2b81e6e7cc4072af93 |
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Hashes for sherpa_onnx-1.10.25-cp38-cp38-macosx_11_0_universal2.whl
Algorithm | Hash digest | |
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SHA256 | 9862f4de949741d00eb407d7aedb7264e213ecf3646a4f005f068c3c18e0d6d8 |
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MD5 | 00886801efbea59f0fb90aaec2900942 |
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BLAKE2b-256 | 6be4efb6e41e8b52ae574e27ca3258e160470f6e4751744ecb74719cc2f66c19 |
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Hashes for sherpa_onnx-1.10.25-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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SHA256 | 2850dbf36509df3f95005112361d9b19363947370fba24002975f61ef1414a3d |
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MD5 | 3f7609181beade209fa2344f2a74d131 |
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BLAKE2b-256 | 64c3473cc04ab7992d75172740b631d4e570deeb1985e40c0cfacdf1a77594ff |
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Hashes for sherpa_onnx-1.10.25-cp38-cp38-linux_armv7l.whl
Algorithm | Hash digest | |
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SHA256 | 35b74a21ca792f123a0bed32da70982aa25af4c3306a6fb66ee42864a0de9a23 |
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MD5 | a69b635a77b6950d7aa15e8bf9fbfb3b |
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BLAKE2b-256 | 881933b5df4d12f0ccfe0bf651b337a10e31da107e2db848a2f38340e096e4ba |
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Hashes for sherpa_onnx-1.10.25-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | deefbcd44e2d9ad934ee2d37350bda0ce355e66c23a1b82f2a04cc0dc35e95df |
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MD5 | c28a432403763c8b10d30aa9d799df8f |
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BLAKE2b-256 | 5781a93d508d84b44d99d9c4a7c80b1e1d35b61d1955691dc1e5e263a2a8fbca |
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Hashes for sherpa_onnx-1.10.25-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
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SHA256 | 5f867dc8b53a5a33c1adb31f392c3e8a8c281f59024fbb84c39a71f61e69b8ac |
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MD5 | df0bf67189824d855b7dd00df94f4a91 |
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BLAKE2b-256 | bde7786a1f027d5fb60bb643505762e9b959d475411fbbc97066be9c32202308 |
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Hashes for sherpa_onnx-1.10.25-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 0c74c880a2f3b1b68cdbb42048c6e6e4ddaaecbe2d5ad49f01765eb586ed53b2 |
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MD5 | 7f0f26cbf14614be8ca792e744c216f6 |
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BLAKE2b-256 | 2c0d4acbe871372bd95ff9e0d7159e989494e760c473e9024225b0c8f310f07b |
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Hashes for sherpa_onnx-1.10.25-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 428d60883ac378993cb220bb680da43b0873d7c30b6b6f0d9ebf250fe2255450 |
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MD5 | 144e98824d0aef0cd222dd47751e513c |
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BLAKE2b-256 | 260526bb0aaecf98bead4225a6a3433b43609d68d4a757224d357b4d6b831ac3 |
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Hashes for sherpa_onnx-1.10.25-cp37-cp37m-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 842305071a0e5e2fc6060ca733bc148d32518064a76409c1c85a3ec5fd080a38 |
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MD5 | 8da45684ba9340248ba8822ad830a3cd |
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BLAKE2b-256 | a083c68064e733a28d943703e25eaf6ed6128b3c277dbdcd9260b6f4d2ee067a |
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Hashes for sherpa_onnx-1.10.25-cp37-cp37m-linux_armv7l.whl
Algorithm | Hash digest | |
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SHA256 | 8ce902220bdbb154ca4880f9804063f2def4ff4e48e59c4e3707999fc73b8e79 |
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MD5 | b3120de6c893d9e4d3c8c688dad5e23c |
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BLAKE2b-256 | 8e7897e758af1e4fcb8acf8b1fc86eb69bfd57bbdf4beb705853ac985291241f |