sherpa-onnx

No project description provided

These details have not been verified by PyPI

Project links

Homepage

Project description

Supported functions

Speech recognition	Speech synthesis	Source separation
✔️	✔️	✔️

Speaker identification	Speaker diarization	Speaker verification
✔️	✔️	✔️

Spoken Language identification	Audio tagging	Voice activity detection
✔️	✔️	✔️

Keyword spotting	Add punctuation	Speech enhancement
✔️	✔️	✔️

Supported platforms

Architecture	Android	iOS	Windows	macOS	linux	HarmonyOS
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
✔️	✔️	✔️	✔️

It also supports WebAssembly.

Supported NPUs

1. Rockchip NPU (RKNN)	2. Qualcomm NPU (QNN)	3. Ascend NPU
✔️	✔️	✔️

4. Axera NPU
✔️

Join our discord

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)
Speech enhancement (e.g., gtcrn, DPDFNet)
Keyword spotting
Source separation (e.g., spleeter, UVR)

on the following platforms and operating systems:

x86, x86_64, 32-bit ARM, 64-bit ARM (arm64, aarch64), RISC-V (riscv64), RK NPU, Ascend NPU
Linux, macOS, Windows, openKylin
Android, WearOS
iOS
HarmonyOS
NodeJS
WebAssembly
NVIDIA Jetson Orin NX (Support running on both CPU and GPU)
NVIDIA Jetson Nano B01 (Support running on both CPU and GPU)
Raspberry Pi
RV1126
LicheePi4A
VisionFive 2
旭日X3派
爱芯派
RK3588
SpacemiT-K1
SpacemiT-K3
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	中国镜像
Speaker diarization	Click me	镜像
Speech recognition	Click me	镜像
Speech recognition with Whisper	Click me	镜像
Speech synthesis	Click me	镜像
Generate subtitles	Click me	镜像
Audio tagging	Click me	镜像
Source separation	Click me	镜像
Spoken language identification with Whisper	Click me	镜像

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) with Zipformer CTC	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 Moonshine tiny	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
VAD + speech recognition (多语种及多种中文方言) with Dolphin-base	Click me	地址
Speech synthesis (Piper, English)	Click me	地址
Speech synthesis (Piper, German)	Click me	地址
Speech synthesis (Matcha, Chinese)	Click me	地址
Speech synthesis (Matcha, English)	Click me	地址
Speech synthesis (Matcha, Chinese+English)	Click me	地址
Speaker diarization	Click me	地址
Voice cloning with ZipVoice (Chinese+English)	Click me	地址
Voice cloning with Pocket TTS (English)	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
Simulated-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
Speaker segmentation	Address
Speech enhancement	Address
Source separation	Address

Some pre-trained ASR models (Streaming)

Please see

for more models. The following table lists only SOME of them.

Name	Supported Languages	Description
sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20	Chinese, English	See also
sherpa-onnx-streaming-zipformer-small-bilingual-zh-en-2023-02-16	Chinese, English	See also
sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23	Chinese	Suitable for Cortex A7 CPU. See also
sherpa-onnx-streaming-zipformer-en-20M-2023-02-17	English	Suitable for Cortex A7 CPU. See also
sherpa-onnx-streaming-zipformer-korean-2024-06-16	Korean	See also
sherpa-onnx-streaming-zipformer-fr-2023-04-14	French	See also

Some pre-trained ASR models (Non-Streaming)

Please see

for more models. The following table lists only SOME of them.

Name	Supported Languages	Description
sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8	English	It is converted from https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2
Whisper tiny.en	English	See also
Moonshine tiny	English	See also
sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03	Chinese	A Zipformer CTC model
sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17	Chinese, Cantonese, English, Korean, Japanese	支持多种中文方言. See also
sherpa-onnx-paraformer-zh-2024-03-09	Chinese, English	也支持多种中文方言. See also
sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01	Japanese	See also
sherpa-onnx-nemo-transducer-giga-am-russian-2024-10-24	Russian	See also
sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24	Russian	See also
sherpa-onnx-zipformer-ru-2024-09-18	Russian	See also
sherpa-onnx-zipformer-korean-2024-06-24	Korean	See also
sherpa-onnx-zipformer-thai-2024-06-20	Thai	See also
sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04	Chinese	支持多种方言. See also

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.

Projects using sherpa-onnx

Sherpa Voice / @siteed/sherpa-onnx.rn

React Native wrapper and demo app for validating sherpa-onnx on iOS, Android, and Web, including ASR, TTS, VAD, KWS, speaker ID, diarization, language ID, punctuation, audio tagging, and speech enhancement.

Speed of Sound

A voice-typing application for the Linux desktop (GTK4/Adwaita). It captures microphone audio, transcribes it offline using Sherpa ONNX ASR models, optionally polishes the text with an LLM, and types the result into the active window via XDG Remote Desktop Portal keyboard simulation.

VoxSherpa TTS

VoxSherpa TTS is a 100% offline Android Text-to-Speech app powered by Sherpa-ONNX. It supports Kokoro-82M, Piper, and VITS engines with multilingual support including Hindi, English, British English, Japanese, Chinese and 50+ more languages.

Download APK (All Versions)
Android 11+ · 100% offline · No telemetry

Generate	Models	Library	Settings

BreezeApp from MediaTek Research

BreezeAPP is a mobile AI application developed for both Android and iOS platforms. Users can download it directly from the App Store and enjoy a variety of features offline, including speech-to-text, text-to-speech, text-based chatbot interactions, and image question-answering

1	2	3

Open-LLM-VTuber

Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms

voiceapi

Streaming ASR and TTS based on FastAPI

It shows how to use the ASR and TTS Python APIs with FastAPI.

腾讯会议摸鱼工具 TMSpeech

Uses streaming ASR in C# with graphical user interface.

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！

Sherpa-ONNX 语音识别服务器

A server based on nodejs providing Restful API for speech recognition.

QSmartAssistant

一个模块化，全过程可离线，低占用率的对话机器人/智能音箱

It uses QT. Both ASR and TTS are used.

Flutter-EasySpeechRecognition

It extends ./flutter-examples/streaming_asr by downloading models inside the app to reduce the size of the app.

Note: [Team B] Sherpa AI backend also uses sherpa-onnx in a Flutter APP.

sherpa-onnx-unity

sherpa-onnx in Unity. See also #1695, #1892, and #1859

xiaozhi-esp32-server

本项目为xiaozhi-esp32提供后端服务，帮助您快速搭建ESP32设备控制服务器 Backend service for xiaozhi-esp32, helps you quickly build an ESP32 device control server.

KaithemAutomation

Pure Python, GUI-focused home automation/consumer grade SCADA.

It uses TTS from sherpa-onnx. See also ✨ Speak command that uses the new globally configured TTS model.

Open-XiaoAI KWS

Enable custom wake word for XiaoAi Speakers. 让小爱音箱支持自定义唤醒词。

Video demo in Chinese: 小爱同学启动～˶╹ꇴ╹˶！

C++ WebSocket ASR Server

It provides a WebSocket server based on C++ for ASR using sherpa-onnx.

Go WebSocket Server

It provides a WebSocket server based on the Go programming language for sherpa-onnx.

Making robot Paimon, Ep10 "The AI Part 1"

It is a YouTube video, showing how the author tried to use AI so he can have a conversation with Paimon.

It uses sherpa-onnx for speech-to-text and text-to-speech.

1

TtsReader - Desktop application

A desktop text-to-speech application built using Kotlin Multiplatform.

MentraOS

Smart glasses OS, with dozens of built-in apps. Users get AI assistant, notifications, translation, screen mirror, captions, and more. Devs get to write 1 app that runs on any pair of smart glasses.

It uses sherpa-onnx for real-time speech recognition on iOS and Android devices. See also https://github.com/Mentra-Community/MentraOS/pull/861

It uses Swift for iOS and Java for Android.

flet_sherpa_onnx

Flet ASR/STT component based on sherpa-onnx. Example a chat box agent

achatbot-go

a multimodal chatbot based on go with sherpa-onnx's speech lib api.

Local offline voice input plugin for Fcitx5 (Linux input method framework). It uses C++ with offline ASR for speech recognition, supporting push-to-talk, command mode, and optional LLM post-processing.

Video demo in Chinese: fcitx5-vinput

Wake Word

A VS Code extension for hands-free voice-activated coding. It uses sherpa-onnx for real-time keyword spotting (KWS) to detect custom wake phrases and trigger VS Code commands by voice. Audio capture is handled by decibri, a cross-platform Node.js microphone streaming library with prebuilt native binaries.

SmartSub

SmartSub is a local-first cross-platform desktop application for the complete subtitle production pipeline: audio/video transcription, subtitle translation, proofreading, and subtitle burning/muxing.

It natively integrates sherpa-onnx to power three offline ASR engines — FunASR, Qwen3-ASR, and FireRedASR — delivering high-accuracy Chinese and multilingual speech recognition entirely on-device, with no file uploads required.

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

This version

1.13.4

Jul 7, 2026

1.13.3

Jun 15, 2026

1.13.2

May 13, 2026

1.13.1

May 8, 2026

1.13.0

Apr 28, 2026

1.12.40

Apr 24, 2026

1.12.39

Apr 17, 2026

1.12.38

Apr 12, 2026

1.12.37

Apr 10, 2026

1.12.36

Apr 8, 2026

1.12.35

Apr 3, 2026

1.12.34

Mar 26, 2026

1.12.33

Mar 24, 2026

1.12.32

Mar 22, 2026

1.12.31

Mar 20, 2026

1.12.30

Mar 19, 2026

1.12.29

Mar 12, 2026

1.12.28

Feb 28, 2026

1.12.27

Feb 26, 2026

1.12.26

Feb 24, 2026

1.10.46

Feb 26, 2025

1.10.45

Feb 17, 2025

1.9.30

Jun 16, 2024

1.8.11

Nov 21, 2023

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sherpa_onnx-1.13.4.tar.gz (982.8 kB view details)

Uploaded Jul 7, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

sherpa_onnx-1.13.4-cp314-cp314-win_amd64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.14Windows x86-64

sherpa_onnx-1.13.4-cp314-cp314-win32.whl (2.0 MB view details)

Uploaded Jul 7, 2026 CPython 3.14Windows x86

sherpa_onnx-1.13.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.14manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.14manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp314-cp314-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.14macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp314-cp314-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.14macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp314-cp314-macosx_10_15_universal2.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.14macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp314-cp314-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.14

sherpa_onnx-1.13.4-cp313-cp313-win_amd64.whl (2.2 MB view details)

Uploaded Jul 7, 2026 CPython 3.13Windows x86-64

sherpa_onnx-1.13.4-cp313-cp313-win32.whl (1.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.13Windows x86

sherpa_onnx-1.13.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.13manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.13manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp313-cp313-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.13macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp313-cp313-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.13macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp313-cp313-macosx_10_15_universal2.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.13macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp313-cp313-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.13

sherpa_onnx-1.13.4-cp312-cp312-win_amd64.whl (2.2 MB view details)

Uploaded Jul 7, 2026 CPython 3.12Windows x86-64

sherpa_onnx-1.13.4-cp312-cp312-win32.whl (1.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.12Windows x86

sherpa_onnx-1.13.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.12manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.12manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp312-cp312-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.12macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp312-cp312-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.12macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp312-cp312-macosx_10_15_universal2.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.12macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp312-cp312-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.12

sherpa_onnx-1.13.4-cp311-cp311-win_amd64.whl (2.2 MB view details)

Uploaded Jul 7, 2026 CPython 3.11Windows x86-64

sherpa_onnx-1.13.4-cp311-cp311-win32.whl (1.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.11Windows x86

sherpa_onnx-1.13.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.11manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.11manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp311-cp311-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.11macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp311-cp311-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.11macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp311-cp311-macosx_10_15_universal2.whl (4.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.11macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp311-cp311-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.11

sherpa_onnx-1.13.4-cp310-cp310-win_amd64.whl (2.2 MB view details)

Uploaded Jul 7, 2026 CPython 3.10Windows x86-64

sherpa_onnx-1.13.4-cp310-cp310-win32.whl (1.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.10Windows x86

sherpa_onnx-1.13.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.10manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.10manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp310-cp310-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.10macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp310-cp310-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.10macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp310-cp310-macosx_10_15_universal2.whl (4.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.10macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp310-cp310-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.10

sherpa_onnx-1.13.4-cp39-cp39-win_amd64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.9Windows x86-64

sherpa_onnx-1.13.4-cp39-cp39-win32.whl (1.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.9Windows x86

sherpa_onnx-1.13.4-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.9manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.9manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp39-cp39-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.9macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp39-cp39-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.9macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp39-cp39-macosx_10_15_universal2.whl (4.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.9macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp39-cp39-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.9

sherpa_onnx-1.13.4-cp38-cp38-win_amd64.whl (2.2 MB view details)

Uploaded Jul 7, 2026 CPython 3.8Windows x86-64

sherpa_onnx-1.13.4-cp38-cp38-win32.whl (1.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.8Windows x86

sherpa_onnx-1.13.4-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.8manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.13.4-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.8manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.13.4-cp38-cp38-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Jul 7, 2026 CPython 3.8macOS 11.0+ ARM64

sherpa_onnx-1.13.4-cp38-cp38-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded Jul 7, 2026 CPython 3.8macOS 10.15+ x86-64

sherpa_onnx-1.13.4-cp38-cp38-macosx_10_15_universal2.whl (4.4 MB view details)

Uploaded Jul 7, 2026 CPython 3.8macOS 10.15+ universal2 (ARM64, x86-64)

sherpa_onnx-1.13.4-cp38-cp38-linux_armv7l.whl (11.9 MB view details)

Uploaded Jul 7, 2026 CPython 3.8

sherpa_onnx-1.13.4-cp37-cp37m-win_amd64.whl (2.2 MB view details)

Uploaded Jul 7, 2026 CPython 3.7mWindows x86-64

File details

Details for the file sherpa_onnx-1.13.4.tar.gz.

File metadata

Download URL: sherpa_onnx-1.13.4.tar.gz
Upload date: Jul 7, 2026
Size: 982.8 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for sherpa_onnx-1.13.4.tar.gz
Algorithm	Hash digest
SHA256	`29547692418513ad88034c2b5f98985e33042b2351e4ab375469f19a8de18c5f`
MD5	`9934e9397d62f76211fa3bd5243c81db`
BLAKE2b-256	`caf8735244770b4bc63f85fabdad0e46d6ec1f4cc24e64f6e082c2e0fea92b8c`

Algorithm	Hash digest
SHA256	`cb1834182c4047b8edb1dceeed8d5cf7d6e10295a4079e5e0fea674b4314db06`
MD5	`1b24f6922b679a9d5ce120633c347f99`
BLAKE2b-256	`89021e10f71be635a3f9ef793b07ab88ddb7afa82ab722f3ea275a4877da1bcb`

Algorithm	Hash digest
SHA256	`39af016b9fb7c053da2270e44182fb8932eeab991dcdc963bbc4366324e9ec51`
MD5	`70282eea31a0cb6e5af9f18b8351778f`
BLAKE2b-256	`961ca80bfad89846ccb1da4d18ef46bf364c83cff9514a0e298110c8de480e14`

Algorithm	Hash digest
SHA256	`16b046f1c7ceecf666947d652c928278c377847db7f697c9e94af9feaf20ac2c`
MD5	`dd5612956ca0e33ddbfb8410703c4477`
BLAKE2b-256	`5a4b49b4be95af2e12bfa8a92a7eba720cc68e4da178847ffa4ddb66479d4e9c`

Algorithm	Hash digest
SHA256	`7a6bf37e45727b0e94661a29b34927e9edc4b3849411cc38036e30d81df48d31`
MD5	`6b4c65be9255d06f57a53f12fcd63ce2`
BLAKE2b-256	`8ce58c601626448358ac8571100db051f35af5ef02c1d12ab1cb026436474a22`

Algorithm	Hash digest
SHA256	`ff38ac153527fda0f6158e7c097b66e980a6840455d908e037ce09a4a4c1ce14`
MD5	`aea37240b87e2274af675ccb1d7c9e6e`
BLAKE2b-256	`1fb529997d7de29cae8e3f54e8a993fcf14d48d5a7960deab10df479fcbc7d64`

Algorithm	Hash digest
SHA256	`972137cbf19d501ea6a51857528b340ce1c3d204571e3d56dcc72eded5827418`
MD5	`22765179ffc9750c0e57035db9055021`
BLAKE2b-256	`9a90191b3b73af1b54584f9f96e256d9ca9f6f3781cacdb4287c7efea6b25094`

Algorithm	Hash digest
SHA256	`ff9237b98c173dabf8f5f6317c4ead54afd4b35179b03be652f65e1e121bee74`
MD5	`0b6529970e236e3b6fbf43fd36f2f4e0`
BLAKE2b-256	`cc0f7fbed45ef8437d20967f4577514e8903b7f4a87a8cea9b9fed9e2b18fa45`

Algorithm	Hash digest
SHA256	`7ddb3d46fe0d6cc745d222e055844dbb24fd4e66935d4ead67d1315911ea7a46`
MD5	`5cfc4db6f0f83b06bd5f086e75b05a31`
BLAKE2b-256	`8e9ecb97cf04c0c4de0a1d43463952e387a938eea8f6ab94e2eb95af21162f57`

Algorithm	Hash digest
SHA256	`17050fdfb48d37ae996364f697c554a1399740d18e5a56b143c011d00cfed3e0`
MD5	`ca94d0254e7e6a80e8af2c6130efb787`
BLAKE2b-256	`8240ee8a0a8c83fc6d7f5245a5a031e471d3b115e20cce867e7abb2f9d4185c9`

Algorithm	Hash digest
SHA256	`0cabb508a15be22138f9fb7695d7ec5f3893ecd088ee419b9df559fed7e8f649`
MD5	`d44e357ce1cbf1ec63a498acf9a58626`
BLAKE2b-256	`449084205ff383ba9335c3821c2cd6d514350f52199cb640ec094517f1f911a0`

Algorithm	Hash digest
SHA256	`88af596be24eac32982dd64fcac30af99d9130ca498bfa1a0064189c8498195b`
MD5	`5bc6079330197a8555b6efe711140914`
BLAKE2b-256	`db47da3ea14ab647a4f6580227853fe29353e1173ff77064d42c0bb31d01b453`

Algorithm	Hash digest
SHA256	`a39352ceb2ec6671a1f252fb768fff75bb2f0bc849cca5f66f490e89910a860d`
MD5	`6b8110f73a699c3a1b7f6b0e98296a37`
BLAKE2b-256	`6dc2281d84dc9e448ea99d7fb77708cbe1cc7cfd8c7d669727dc94385a9e4ca5`

Algorithm	Hash digest
SHA256	`8ec5394b4ea73bf01e6883cf078348f87350f4eb3567d51d92cae77ea2582403`
MD5	`e0eed218408c975090d4b68244cf67f3`
BLAKE2b-256	`21edd07787dd4be4119e6587c840f6b417c2d57c14d694d334af609d68cb5a41`

Algorithm	Hash digest
SHA256	`b4f54363b264b16148a724b4442f00cda97fcd4e9beeda3d75637753910e8557`
MD5	`4e2f9210f2d80b8ebe731616e761e38c`
BLAKE2b-256	`622809aa9461e8bdf894ba8466e047e40fb5da8aaa6d68c19cf1e2aabe01e706`

Algorithm	Hash digest
SHA256	`083747c2d0362ead0501cc773a618be19862a800b3f8f259d3bd3486f1494af4`
MD5	`63e8c36dfb9ff996cbfa2eba9b2d0ced`
BLAKE2b-256	`68d71e9a7dedab2da8af1a8417b4f4d5f496bd7700a71b59c5e085de5e10761b`

Algorithm	Hash digest
SHA256	`9e98dc5e0559ad953f227fc884958c71b10c65a93667331405e7d4441ed5f76d`
MD5	`8828175fcceef6806add25e9764ae923`
BLAKE2b-256	`4b3645b17335f041f1383f6fd142ab57c2d8a337ba2386b7547b125ec9d780af`

Algorithm	Hash digest
SHA256	`b8436ffe2763b3fd522fbac8fe53f47d611721c84819c241acfb65d122403d7d`
MD5	`f44172e1fe90b0478328b42052096dc5`
BLAKE2b-256	`bbbb1e723ab703a1e354f390de19981ec0c347576f87be01915d826dc6fc9f41`

Algorithm	Hash digest
SHA256	`d49928a3455bae1dd4e93f6b013cfbd2c3ccb5cde74aabae3710b656e7d79b6b`
MD5	`bf3eab1f9c08e8ea9c548e388c74fed6`
BLAKE2b-256	`d404cfd543933ae24430d124e533847c73a84a5f0efd60f07bbc6403032f9624`

Algorithm	Hash digest
SHA256	`5f0158f3513d3adab1ebba0c26f0c815e53ba13b96846d92ef095ae25d648860`
MD5	`cc0deea79dc26e556f0b8a262eede86b`
BLAKE2b-256	`ccb18dfe5d1d72c92ea1c95db999a95b61bfbb9769f1c569f06e572eda095c52`

Algorithm	Hash digest
SHA256	`f709e6dd02ebf7d37dcb02d5eadc5fb66c9922dd5809df770c1ef5d625ae7a44`
MD5	`769fabbdb259d4508c262ac244e5a0d2`
BLAKE2b-256	`f4909b67ed3e7adc79daf0ba49c4936a691521488125b04fe469b64a8b5398ff`

sherpa-onnx 1.13.4

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Supported functions

Supported platforms

Supported programming languages

Supported NPUs

Introduction

Links for Huggingface Spaces

Links for pre-built Android APKs

Links for pre-built Flutter APPs

Real-time speech recognition

Text-to-speech

Links for pre-built Lazarus APPs

Generating subtitles

Links for pre-trained models

Some pre-trained ASR models (Streaming)

Some pre-trained ASR models (Non-Streaming)

Useful links

How to reach us

Projects using sherpa-onnx

BreezeApp from MediaTek Research

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

Algorithm	Hash digest
SHA256	`84e58b5a074b97c5307c9b6221d1d20fbf412a1a5dff4960ca9c32bb5184219f`
MD5	`4c2c1f9ae82f4c31bde2dfc303fbf988`
BLAKE2b-256	`fc79ee999f0c3b7789077d0939716a38234573d139f851a31409aa028fe2c610`

Algorithm	Hash digest
SHA256	`02b57dc2c829976eb842e6aee6a0e4ac3b9991aeb5afa89fd44eb71d848a4ecd`
MD5	`8372dae9777e3acf96f4f0f8412e5a6d`
BLAKE2b-256	`823707f03e97f157b206f6e62d722ec7c5ff41c7e9dc6aa2dc7de69b57e39b5a`

Algorithm	Hash digest
SHA256	`2257545ea170f58b7977309793979d6d078761b7fdb0528561285f8ead4169db`
MD5	`5d6495c82c560f00c8cac64075431f1d`
BLAKE2b-256	`e834b6d3483b08ec8a4a141e978c4b92530fd0a61dd571a575c1fe24bee300d7`

Algorithm	Hash digest
SHA256	`bcf64f2d853a1afe236e9e220df62f2f53ef6ad792ca7e406d6173ec003319b8`
MD5	`01f91a59d7015a9332f081644458bed6`
BLAKE2b-256	`8e57179e3a6c1fec33aa6535051feddd5da36e5622d35630b12a67a2805b76b3`

Algorithm	Hash digest
SHA256	`04a82d79c13a4ce2bd9ccf51de93e83cbfc7bc50520c53e5e967565100d0724d`
MD5	`3e8130353550d9b48d72fc805f603df7`
BLAKE2b-256	`7eb1ae1c113ac9c67dcabbed559f50950c7220a62e49e6b5acb4c2219ab22409`

Algorithm	Hash digest
SHA256	`1d746b8c6ed1ce9eb94868d71b5ea9c22274b8cb166420bd1772f7df470b753a`
MD5	`ae34f8162bf2fbbd1a7682a0952738b4`
BLAKE2b-256	`b5732fff5d28669e91851e981d223c50aa21f90d712a4551dcb51c231b3a27fe`

Algorithm	Hash digest
SHA256	`fbfc385b98d730080e1b12dc94c604be3867e4fb7bd6b15b830eb33cbf390111`
MD5	`3340c40be1a4201fc9ac9c734ca062b6`
BLAKE2b-256	`e0ac88b4e1ce614ddebe2484e95cad4b19d7db24f3b489d04f9877667cb48ccb`

Algorithm	Hash digest
SHA256	`7c4a1c178eb801af92f70120128c1f956b5388b9d684f0af2a08614e05dc3047`
MD5	`c9d29fd774c00a15d3530c0c6c306dd4`
BLAKE2b-256	`eacbc80832f800719c72fc5805a94fe489e805fc67156e102927609917ad8f67`

Algorithm	Hash digest
SHA256	`c2d4337b80b54dd68f566cab941a2ad47ab6cfabb68e88002ee0c920493c16d3`
MD5	`c54c52972f0e2df0ff6872d9a5fe6074`
BLAKE2b-256	`e8934385fcdb1f197521fe13fa887893cc200d27569d3cbcccf0d7b92d6a9e62`

Algorithm	Hash digest
SHA256	`c3e9f96a07570faefca8e3aabcfb78690e680d188d5d44d06fd8711185fe37d6`
MD5	`76dee40f5317ecc5964253ebd537bbdc`
BLAKE2b-256	`41c8a2ff828ce9a2702b607c25f6303b21d7d41b6ad1520f660b95a0113f4051`