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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. The 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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SHA256 | fe8c2be30a1454565a36190403ef7b3b20bac15c2aeb4df59ccaca18d43014dc |
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MD5 | bda8b056f2796ed28f3ee57c30c3f956 |
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BLAKE2b-256 | a063eb663fcb7e6fb8d4aadacb7cdc5bda35934384ae80f960bbe745ee9e9ab1 |
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Hashes for sherpa_onnx-1.10.23-cp38-cp38-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | a179ae68716f9ae8722a20b054837545e12373adc69bf1a16ac300ad92e01c40 |
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MD5 | 1705367e2dfac87747aa639fcb275d73 |
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BLAKE2b-256 | e5f2c185a7dcb6a99971cc9fb25523cb7e14d6178b3cb4378d9795d50ee38b4b |
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Hashes for sherpa_onnx-1.10.23-cp38-cp38-macosx_11_0_universal2.whl
Algorithm | Hash digest | |
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SHA256 | 91f710a436f22b2bc7251d50eabb7e2782ded81a7369acb01a0cf40557ededfc |
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MD5 | 165d25e96bc6d163ebe9b4db97942ef4 |
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BLAKE2b-256 | ef8d8845766b32591e64ed6d3759f8cfa0a8447d37796bf7c28689ac9ad5706f |
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Hashes for sherpa_onnx-1.10.23-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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SHA256 | a1db5bee56103e3704d6771e35bc34a17b847bd77df7d0e3e6a64a539f2667c6 |
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MD5 | 105f451c34acfb5d7e062d1adf6e324c |
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BLAKE2b-256 | f2bb3a6d6d07171a75a24febec56d694329a5c6e7bde92178cee64cddf504a9d |
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Hashes for sherpa_onnx-1.10.23-cp38-cp38-linux_armv7l.whl
Algorithm | Hash digest | |
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SHA256 | 8eb3eae75ce79ae721acbababfb992d0bbec8cc043f58efb588f79b1d5d71543 |
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MD5 | b11d2b804bda873aee054991436e5c46 |
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BLAKE2b-256 | 220f8d984c38420ebf308f0b84e09c8dbe76f8b387b76281e5bd6db4134a8aff |
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Hashes for sherpa_onnx-1.10.23-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | 60b705c379a5ee687d2b5119d4e6f0d1a18813fa0038a966d8b1f074a06c2c35 |
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MD5 | ace5144296ed46635da8160b6d9ea97f |
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BLAKE2b-256 | 442196be33757697f4a6d33887c10077b9ca03e4dea4072aba4ea7ed92bc199f |
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Hashes for sherpa_onnx-1.10.23-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
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SHA256 | 6a1a013953941504011d34121b53c50ba1e03f4454ea346f504c09bb7e3d55d1 |
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MD5 | 80bfdd5fab170776016411bd575dd31a |
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BLAKE2b-256 | 06b25525d2b2faa76ff262704dee9bd87416fd39fd0fc1fa8aa23e2aeddb9556 |
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Hashes for sherpa_onnx-1.10.23-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 63d3436ec52cf456445b8792a5f4de77fafa46a4c2327713c2a401abd920dacc |
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MD5 | 83e92a68b102272f8d673002843d11a5 |
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BLAKE2b-256 | e5f7a6d5ba39abc6301ff0ef4d7ee340453905305c9fdeb7ec94a6406670d385 |
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Hashes for sherpa_onnx-1.10.23-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 9192117b6d04fb1247568ec1b144c40173d32570d583ebf6cb88ff2af376c131 |
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MD5 | f07a4f0004171db87a06688b10d32793 |
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BLAKE2b-256 | e1caf3317b65c6deba0893556fb2478671460e34a1558b7bf2579ec3e0074c6c |
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Hashes for sherpa_onnx-1.10.23-cp37-cp37m-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | eca919015622a4dce9c70a65fe06c2ca954f2170fac16b5cd5a80bb071092af0 |
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MD5 | 92c91f5549704b0e9fc0990e68b5750e |
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BLAKE2b-256 | 37795b8fccc7d7b668f2cca60664c45ebf219260482e145edd5ff1f9eedf5832 |
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Hashes for sherpa_onnx-1.10.23-cp37-cp37m-linux_armv7l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3a900eec4a4d0c565a61faa0c38576dd7719a9c62a82893655ee3b3e1202fbd1 |
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MD5 | d2cc6698d7bd46381be504c243ac228b |
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BLAKE2b-256 | c866ef379ba1c8c50cdd79c59400e7d3581ba8c7ee5be4237d66e4816f923875 |