Skip to main content

No project description provided

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)
  • Keyword spotting
  • Source separation (e.g., spleeter, UVR)

on the following platforms and operating systems:

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

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

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

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

See also https://github.com/t41372/Open-LLM-VTuber/pull/50

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.

See also

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.

Project details


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-1.12.29.tar.gz (787.3 kB view details)

Uploaded Source

Built Distributions

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

sherpa_onnx-1.12.29-cp314-cp314-win_amd64.whl (2.1 MB view details)

Uploaded CPython 3.14Windows x86-64

sherpa_onnx-1.12.29-cp314-cp314-win32.whl (1.8 MB view details)

Uploaded CPython 3.14Windows x86

sherpa_onnx-1.12.29-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp314-cp314-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp314-cp314-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.14macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp314-cp314-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp314-cp314-linux_armv7l.whl (11.2 MB view details)

Uploaded CPython 3.14

sherpa_onnx-1.12.29-cp313-cp313-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.13Windows x86-64

sherpa_onnx-1.12.29-cp313-cp313-win32.whl (1.7 MB view details)

Uploaded CPython 3.13Windows x86

sherpa_onnx-1.12.29-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp313-cp313-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp313-cp313-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.13macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp313-cp313-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp313-cp313-linux_armv7l.whl (11.2 MB view details)

Uploaded CPython 3.13

sherpa_onnx-1.12.29-cp312-cp312-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.12Windows x86-64

sherpa_onnx-1.12.29-cp312-cp312-win32.whl (1.7 MB view details)

Uploaded CPython 3.12Windows x86

sherpa_onnx-1.12.29-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp312-cp312-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp312-cp312-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.12macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp312-cp312-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp312-cp312-linux_armv7l.whl (11.2 MB view details)

Uploaded CPython 3.12

sherpa_onnx-1.12.29-cp311-cp311-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.11Windows x86-64

sherpa_onnx-1.12.29-cp311-cp311-win32.whl (1.7 MB view details)

Uploaded CPython 3.11Windows x86

sherpa_onnx-1.12.29-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp311-cp311-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp311-cp311-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.11macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp311-cp311-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp311-cp311-linux_armv7l.whl (11.2 MB view details)

Uploaded CPython 3.11

sherpa_onnx-1.12.29-cp310-cp310-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.10Windows x86-64

sherpa_onnx-1.12.29-cp310-cp310-win32.whl (1.7 MB view details)

Uploaded CPython 3.10Windows x86

sherpa_onnx-1.12.29-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp310-cp310-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp310-cp310-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.10macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp310-cp310-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp310-cp310-linux_armv7l.whl (11.1 MB view details)

Uploaded CPython 3.10

sherpa_onnx-1.12.29-cp39-cp39-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.9Windows x86-64

sherpa_onnx-1.12.29-cp39-cp39-win32.whl (1.7 MB view details)

Uploaded CPython 3.9Windows x86

sherpa_onnx-1.12.29-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp39-cp39-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp39-cp39-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.9macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp39-cp39-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp39-cp39-linux_armv7l.whl (11.2 MB view details)

Uploaded CPython 3.9

sherpa_onnx-1.12.29-cp38-cp38-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.8Windows x86-64

sherpa_onnx-1.12.29-cp38-cp38-win32.whl (1.7 MB view details)

Uploaded CPython 3.8Windows x86

sherpa_onnx-1.12.29-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

sherpa_onnx-1.12.29-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (4.1 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

sherpa_onnx-1.12.29-cp38-cp38-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

sherpa_onnx-1.12.29-cp38-cp38-macosx_10_15_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.8macOS 10.15+ x86-64

sherpa_onnx-1.12.29-cp38-cp38-macosx_10_15_universal2.whl (4.3 MB view details)

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

sherpa_onnx-1.12.29-cp38-cp38-linux_armv7l.whl (11.1 MB view details)

Uploaded CPython 3.8

sherpa_onnx-1.12.29-cp37-cp37m-win_amd64.whl (2.1 MB view details)

Uploaded CPython 3.7mWindows x86-64

File details

Details for the file sherpa_onnx-1.12.29.tar.gz.

File metadata

  • Download URL: sherpa_onnx-1.12.29.tar.gz
  • Upload date:
  • Size: 787.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for sherpa_onnx-1.12.29.tar.gz
Algorithm Hash digest
SHA256 6f7dac085a1eecf18bb9a0771d8be15ea1fa66dd5e039e54073e252bf0dbaabf
MD5 99dfc236a7bf026ab1cbf3ffd7b2a84f
BLAKE2b-256 fdd3f708e40632c4230b45917c8757faf82786fcae3107f3e51a1ea92ff51344

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 041f1466af9a6a6488278d5d21e0fe14f985d149f4143a9e67fcc5b5a8cfe3e9
MD5 3d9a016584160a4cd2931f42ba13fb2e
BLAKE2b-256 464c59bb487328ce09f7574e519ae02ff746e1e1b493914806721b274d7edeb6

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp314-cp314-win32.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: CPython 3.14, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-win32.whl
Algorithm Hash digest
SHA256 e901b75cd46853a82b32231d7ea0c12d4a095ecc0b7f854e52aeaed7df3932da
MD5 4f97f016157899aebf0794fa57876242
BLAKE2b-256 a28c8cb0436e5ca549371a660f3e417647255aa53c7fc389130b4a23f6f833d7

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 ccba7b66a79f5df21cfad707394550ae2bace0adc49d1733b4d0ea6a5f1fe29e
MD5 40b17324300aae56738862ba92cd7cf5
BLAKE2b-256 b34ed8c84800b0c9a6a494b7d8ced2b308d6551aac289b45edab660452b4ae4e

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 e44a60539cb655418c3b0bb0655ca3a4ebf7d47854cdf89698db403035e2fcf6
MD5 f69d452d6658978224424c52cb9c1bb0
BLAKE2b-256 c4f95b8fba6f8f48d5d3e6fe8f7086275ec249e6ba592cc5d884b7d99ea29356

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9f6ef139d1636fb5d4e3296ba5160be7dcac52a877cf009ce0fd21a0de049e14
MD5 a98cff5105659613a72181d98249e806
BLAKE2b-256 41e3e0978fe0d6039bae694d846a4dc45b3d6ff1f0ad74b921b97929f69b488c

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 3c9fad61a289742ec6f2521c7fbb3b0249d14daae20d8d833ad62a2d9e1d9e8a
MD5 a324422801c199d1d7e5f376a295bbf6
BLAKE2b-256 547b3471bb6057c4ed3e1fedbde43e2b9f85c3b5211815710f5b89cbfb1b7257

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 26bbf1a6c3a7f6adaec475c03d506dae9b26c929d07474b59880cb1d1fbf832c
MD5 371f62c9bc9250a1cdd045227f106c30
BLAKE2b-256 50c639ed0a0a336d1f8abec10c4f21b64b2beb4fbbb07cbcc7a7172254851845

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp314-cp314-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp314-cp314-linux_armv7l.whl
Algorithm Hash digest
SHA256 50bace59bc1a1a9e3dda979e40e9f13f887abe56e082e7f968a08c5644b31be8
MD5 ef511fa5279acfa082298f96a02e651e
BLAKE2b-256 a1d96fb1db03622cf380bdbdf792e72be44bc54257bfcc777e0e14f94461f26a

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 b34d5c5a76e97e9a511d9d2a855c6400dbc6364464ea0badf67107578f3b00b3
MD5 753bf80e081db8beab13cb8b504b5f16
BLAKE2b-256 4ae779e67ef72ed5d4bad2ecad2842c1d42d5088d8cf15a8c7f461778a92142a

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp313-cp313-win32.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.13, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 429d9d4919792d9249caadc3454ad43b424159b50909eab5fb066941da111fa5
MD5 580ec017eb5807767f035564f70f4218
BLAKE2b-256 8b7d7d9d38a6bb28600e29316d3bb0df00f71f862e2e34e09640628a78d201bd

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 c838c630a4bea6ce0a650914b5e18906c607c8ed448b9a79b2775bdb8b930629
MD5 82329aba080d204dc4991d1797090dcf
BLAKE2b-256 3e88264ef2571458e4fca387726e6b79c8b1f6612d590472a39822a8ee179d41

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 b7a29567b64d198b9ae54e30a3422fbb9fe8208c6f221b82331620fe3b1ccc08
MD5 d609ba705b987779259e9b155bf79bd3
BLAKE2b-256 9f3f1de559af6bfad6275a22f47229cc747b94253a6cd66770a933afec6b9208

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e469588e47252f9d85013417bcb8e81e9ab132950fa0153d1bb81693c48ce544
MD5 6c0048d4e73efdd8e8d2553b9d4033b7
BLAKE2b-256 dca19f4cbdf0ee523a5d89d330fdf85affc813e5dc26ec6b11b2758c1102177a

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 5c8b87f73fa4bc64822a1c833e573e33d05d2b34c17bed7f8f412d17411faedb
MD5 769b8ef1ff461aea5b9be5e9a545432f
BLAKE2b-256 a42d897c4bb98f3d5a450a41305db99ddd43104aa2654adc73f1f7eb7ad0541f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 8674f18dbca3fe471aaf50741cd7b80a9b1202f330830abadbbe2c7c3eba9c29
MD5 ca516948c08b3f0154decb72653d93c6
BLAKE2b-256 841ac011c29089773c0e080207ade9cd7bc4ed6fa2eb3439538afec0b4eb3249

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp313-cp313-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp313-cp313-linux_armv7l.whl
Algorithm Hash digest
SHA256 4b4b350f37d0c6b9d18ff348a34ec863438ca1afd1e4ffc00e064edcca14975c
MD5 c2e6a9da894fdbb65389264e17599c77
BLAKE2b-256 2d0e30caeb4b65d94c84faabdbb2893d67a678a360552ef58cdd1f3636b8b2bc

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 916465bf7d9d21544778fd9b3a645234754e4e9f5702f84b6fff8316050ee517
MD5 38ff3819fc3f859418cc83e3e8f2baea
BLAKE2b-256 93917d097a6775ea6eefc55190666db142289c60b7eb37088f5f0d9641bc9e80

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp312-cp312-win32.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.12, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 f538325a0759fc525be337616c7283b59777c5cc283844fa117e4c46effa5387
MD5 a2635aea3df2d66bc9995f12e67305f2
BLAKE2b-256 65a4d832652f58752206d8aa6b69d389691da8528eafdfdbfa51177133dfe6f8

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 aaf7d26dcdf32ad652cfc4aeb4ee0882b1110ba9d443401411d3d1ec40d57054
MD5 994551a88dd685cc321a4ed2af155c7f
BLAKE2b-256 3c4e9714f1d5c1debf5608c8e78ac76584848a72f4d8d415d03f1ec882d8124f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 58b9d3e24a5fad3d90f1b021bc6a35690fdead21f1896a71974f0b865f59f9aa
MD5 a387be60f5d31bb2f36deb9dc5dcfed1
BLAKE2b-256 9cbed527eacc35e9926c4ea64c37744b79ff76c6a81af9ae13975f4e82c6ef67

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 97053247f61fd13e1f8afba8db5eef7470d82a7224c74bd7fe4917612d650dcb
MD5 1a634165f592a9916e78feaa308e39c3
BLAKE2b-256 4e9ee54950786b84492ebf261cd796edf85ad5920680b54c949355f2ff5eee85

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 d3145e5a515411cbb4d2a2903135944b3117b2df5eaed6c2e97dcbc4d5ff4a46
MD5 834125188c228dadad76feaed5a297fc
BLAKE2b-256 28fb8382a1e24fc01e9a8f8771a39a760e8bb648553960843241c167f4b7d266

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 c54e4a46400ee983f26393627a447718c27b37aec2d8c3ad613ddcbb416e8710
MD5 9995a1293afd271a3951f9d235d49629
BLAKE2b-256 2f64139d48ada0b7687a082277876ace53b18d783acbaab424e16fc2ffc2cc04

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp312-cp312-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp312-cp312-linux_armv7l.whl
Algorithm Hash digest
SHA256 11bc269f80caf29435617911fac5a851154a9957411de7cf60cdfcd4ef322040
MD5 e5592c05fcd0413c1d22303c60c33403
BLAKE2b-256 ba64bfd70f3bdb39a75235aca99421678eec9ff9bac4fb812c30c1553e388dec

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 303a8fb0407b146add3baf3ffcc08b1c2920d74016b0df304d6a01b7ac061090
MD5 bdcb6bcb1e4cf31b2c48eeacf98a486d
BLAKE2b-256 1211e463b5a605aa3242b7849c7bbf1c494350025c6fcbf7d8e6468f8ebdf2b5

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp311-cp311-win32.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 9b3b0cb5475ffc496f75fef05977be83d437a0c9135b7b9d3636e5911998780c
MD5 336c5b23b844ab6582fc837bdb7f6539
BLAKE2b-256 b842d7cd423f53a0f6b7995ac8da6f1c8fe0eab4c821e3e9656ff289d70f3407

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 b08fcd0de5c34feaa51479f9b5f7f4ef7d04f0724d016f6c9e9bf1458ea3ad33
MD5 8a246b6322e390cce5f8c0693a834729
BLAKE2b-256 a00e52da2d8878b0b06b846bdd90c84bc77d27d2df9a8dfb8c65bab895190625

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 fabaeaa2b05b4ec6d75c7950720b1ae1f966c849925452c28cba9f2d132a967d
MD5 dd360021ed7e3731a19569ce61e599c7
BLAKE2b-256 cd713fa362a0e62e6ab481669ae9f9a6533dfec0a0f10a998e3fbfea1496a131

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b2f1e9aefce030d2860eb157cf2b122010effca6f46c4c68a808f02d35ae36ad
MD5 6eee444da615a48205785019367a23e6
BLAKE2b-256 f685397ddd843ac3861e6262db0f7a12719e5bdd9f910fded6e5d473d884614f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 00deacd1e206f6346a7333de52b0f85706b5680687865a6046788172c34fa585
MD5 e6c230e7082303a205f38b388774094e
BLAKE2b-256 b305b9c44a1ffcd05315943437f6dbd366a2b1bb1704b9ab26bb42eb2540ba21

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 b131ac7df22b3dcaf649dd80b0a1e72f236ef00c37290c6bbd75c5b1569788f3
MD5 7a2be92565c4099d91d4f74f4a721961
BLAKE2b-256 d1aa9b43c5a387ee73f3273800575b90dbbd74fda8ba5a4b73d9594ae7be33c3

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp311-cp311-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp311-cp311-linux_armv7l.whl
Algorithm Hash digest
SHA256 f07c74548bd8bf2a6c3815bf356c628baf656eb0d89bec181fcf52b9e9556f0d
MD5 839687e1367704f12c74343b755ab963
BLAKE2b-256 b6eab971d6453b875a371f2f78bb54932bc5d73b4a9e7166f10c3184f0b63f4f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 5e861838878af9138907b2b33de5e6549c73dcd3801357aab47a8512cb8093d3
MD5 ef7f2cc32ad30eab4aecd50cd006aab2
BLAKE2b-256 65c1fb8877b1d2f68a3eaea7c1e285c2fa97aa9457de6cebc2c79a29e891e4b0

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 437efe50cf5b811f7c0fef3fd4408bf932a3b8ea538d4dd4add4486e9d3c235e
MD5 d53553247006221ef56fc03c4a89e7fb
BLAKE2b-256 445e86651f29a1c7a05abcfc29af3acfb26f60531cbb088886c44d645df8ba05

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 ea4c19db1b013e7e0342a3edfd3bbe381cc8f723f21c19634918ee754f2089b5
MD5 461a6a85098d07a7c2b571ac3e514fb4
BLAKE2b-256 effb65e805232f54cbcb3c0c4ed1cc0c77e94dce698508fcbcaded67379f371b

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 ab79f962de4c75074b712209f2688d5d97a98b6c6059d680d954dafe6f358d1e
MD5 f3f31e3dbaf1ed8e6325a9b515928bfe
BLAKE2b-256 6b2d2ffd3b375008f898f4c65bfd4f65a088dc5b4252ffaa7db1d1a1b41300ed

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f626270a4dbe74727b4fc8fb7c835c2c677dc92b4ef13d89b1d343138f39d287
MD5 671f41d1b839f5900472d1c7ee7fff2c
BLAKE2b-256 0775b57195b1a7077bdf1c072d4b900a375bcc9173da52f1f13a5c27fa65670e

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 e4a7d0bbd3efb34c0fd50e2fa374790998ab51ddad001ba3354b9daa926739f0
MD5 2e04f739f37547df9e057815e0ed1d2e
BLAKE2b-256 88b7b7bbde83b64f0281e4120de5c30b730ac2747b4f92a22aab0ae18e0ccef1

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 7d2be903077626475aaac0b5c6cbac4846b0625fa67a7823e62ef9f2069d20cd
MD5 83ffa061004263508341848c156f6939
BLAKE2b-256 a13d4e2a4c4a916a1976bc24dfd0e37f505afb78d677835e60be8d902411fe07

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp310-cp310-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp310-cp310-linux_armv7l.whl
Algorithm Hash digest
SHA256 57b5b325b0efa7d0740a0f3514810c129e6e3823687c45defcf3ef6bea4ce278
MD5 afba530028ac1143f6f3627449e86902
BLAKE2b-256 f2ab2c8e737ebddd67efd02d3682206c0ed0f770946251f3799b370327aeb557

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2623d1e8af23a947d38bf60565fb4421f1f79fa988fdf88acc6d86f71b369f65
MD5 ece57d32e0a9dfaf4d94f1397119d907
BLAKE2b-256 d529421140c17c24642b8b0e1d607898876dde5535b36e53d59230a4b3eae85f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 3e0971edb907de826ee523daa5627be107ad64650c66435a8025d24b5e8d926c
MD5 3d821fc2bfe2557c91498d8f6d623e71
BLAKE2b-256 340daf86eeaa95fd44791015c323a9f943a9d38a314bd5dce8b161f66e19a624

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 528028e170f622d1eabbe86fd7217c48db7b8c45b7f2ec9f89fe07134894bfe1
MD5 cb8aaf7783f5526763875648d1d8f1d3
BLAKE2b-256 7443851b17497dcf9ca34169045dfd14f7fcb803d2845875cfd4becf49271e0d

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 3ee19551e1b9dc62686e314c0c78e9d116351c86684ab20b24881b28326104d8
MD5 4ee92a0722a52605c4b7d05f41a3a945
BLAKE2b-256 44b2c98a09e5d30ec4259c0f7206af45a334638dd20d890bdc68eace25ff80fc

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3f9644c748cdeedbf2a9745691a485424fe8643adb5e1f5ec1a84584e99798f6
MD5 9356c4792a8889f85fce396d875e686c
BLAKE2b-256 8e575ddf33dd6fb65fccfd9821984998a0e76a2fc5fa7e8e34b402ed2b6ccdbb

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 d734562f499face25ad20f1113cf499c093eb3a315d9c913bb8547be1602e91c
MD5 249e366ec811ffb5cc8220a3a623c8c2
BLAKE2b-256 fe97f70a009f94f6329d362a5629f30922fa99e019d736119d22619665be984d

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 cf158511f786ab1936414c9f902b4b3e42e1226829742eb17a109ad39db58454
MD5 5ebbd1c0f377bef92a10fb0d74cfd507
BLAKE2b-256 fc2b2c6b173c6953c98231ee6d4c2a5430fcfe28859c14c1968d2d66dada9c2f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp39-cp39-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp39-cp39-linux_armv7l.whl
Algorithm Hash digest
SHA256 e1115305ff8e08756964d193224111591d14db94854bc5776793fc3d596f1314
MD5 13c2c64a63316cb740053cc944828bea
BLAKE2b-256 40c4e481a77eecd482ad295c7a5430b3479a90e77bb22253c0eab519f8c47d38

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 20d3203b9a5d25857b3847cace0e22a18f64fae7d0332aab95761d6a807b9df3
MD5 7c5e82888a0b6fbcdbfc5c22b6b60c61
BLAKE2b-256 32bbda9bd0c1bbcfbe41555a4231de8f53476074abec8124e093d17f3c8845d7

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-win32.whl.

File metadata

  • Download URL: sherpa_onnx-1.12.29-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.10

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 54829b0849d6ac8e78c687a2aa2333b2eb812e406f8b3d720e10e6ced1339864
MD5 518812a598b20fd084a40414e85256fd
BLAKE2b-256 61d0b3f7c8708224df9cca52c37721071c0d3605de14479db77fd8a2ee2d800d

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 50afc65c5d397f1c9ff1ae3787dbb72d896625a355e2f2af411d0b734c309e9f
MD5 bf90b2e75a189b02f40a57441d3b7cb3
BLAKE2b-256 7f36885295fdd55b88aa49446fc5feda70903075b98e180ff8de6a874472300a

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 ac8493aa0252aa1428b0ea4c8d6363843cd6b3139f206a020b5843a347214885
MD5 0901661f89f834e79738f6a5eac72e2c
BLAKE2b-256 6f8a51a41c656efb4f8265fc9113110b318cfc0018eec42bcda68f75eb216f1e

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c349d307028daa1df26fb7d76916cc7287d0992f55b33b35c1dcc31efbf16662
MD5 037de638f659051c48a79bde817774c1
BLAKE2b-256 81b753b72ad4adab6fce5f4f8f5bf9a8642cb8af965e50092864b8f070279efa

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 09e4481c6e4360a86417fc82ac45da679c38cf40ded64f381c70bd58dcac53eb
MD5 40e81f9acb545296112ca5fe650b9578
BLAKE2b-256 bc53dabea6aac4d46685746136e3cd1bd3b17bef3a8e378e88455b2dc1bd242f

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 7e436fbc9899e8e4ea9e1b5ba2603f520edf13f93f67317289e06f961ef8a6e1
MD5 8f019ea0842c0369d5793bdb78c5169a
BLAKE2b-256 cb90cbc4e654986591a2591ef941a7333496c59dd75a2e8de0e9e8deb5992156

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp38-cp38-linux_armv7l.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp38-cp38-linux_armv7l.whl
Algorithm Hash digest
SHA256 a1f387eb32ee9ddffcf9dd657bc486ed8b4a49c5c836a7eafa4d71219d57525a
MD5 800c4c16a71089d085b5d8d65296178b
BLAKE2b-256 8e37ebe568e1404095671eb98f9a39857c4c3c0de7a421f6e40d08ec79cef137

See more details on using hashes here.

File details

Details for the file sherpa_onnx-1.12.29-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for sherpa_onnx-1.12.29-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 64fb8b215b7316e6465b0b3c5b867bee4a923cd744ed468006d06a3d7a50bfbd
MD5 63547c1d84eb0fc207ab27453c587d5d
BLAKE2b-256 da44274401fbc6657c57069e95bd1a44f9577e5afa29c10624043ee6bce15009

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page