Introduction

sherpa is the deployment framework of the Next-gen Kaldi project.

sherpa supports deploying speech related pre-trained models on various platforms with various language bindings.

If you are interested in how to train your own model or fine tune a pre-trained model, please refer to icefall.

At present, sherpa has the following sub-projects:

The differences are compared below:

k2-fsa/sherpa

k2-fsa/sherpa-onnx

k2-fsa/sherpa-ncnn

Installation difficulty

hard

easy

super easy

NN lib

PyTorch

onnxruntime

ncnn

CPU Support

x86, x86_64
x86, x86_64,
arm32, arm64
**RISC-V**
x86, x86_64,
arm32, arm64,
**RISC-V**

GPU Support
Yes
(with CUDA
for NVIDIA GPUs)

Yes
Yes
(with Vulkan
for ARM GPUs)

OS Support
Linux, Windows,
macOS
Linux, Windows,
macOS, iOS,
Android
Linux, Windows,
macOS, iOS,
Android
Support
batch_size > 1

Yes

Yes

No

Support RKNN

No

Yes

Yes

Provided APIs

C++, Python
C, C++, Python,
C#, Java, Kotlin,
Swift, Go,
JavaScript, Dart
Pascal, Rust
C, C++, Python,
C#, Kotlin,
Swift, Go

Supported functions
streaming ASR
non-streaming ASR
streaming ASR,
non-streaming ASR,
text-to-speech,
speaker diarization,
speaker identification,
speaker verification,
spoken language
identification,
speech denoising,
speech enhancement,
audio tagging,
VAD,
keyword spotting,
streaming ASR,
non-streaming ASR,
text-to-speech,
VAD,

We also support Triton. Please see Triton.