# Deploy ModelScope FunASR Model on Arm Servers

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/funasr/)
- [Introduction to Automatic Speech Recognition](https://learn.arm.com/learning-paths/servers-and-cloud-computing/funasr/1_asr/)
- [ModelScope - an Open Source Pre-trained AI Models Hub](https://learn.arm.com/learning-paths/servers-and-cloud-computing/funasr/2_modelscope/)
- [Building ASR Applications with ModelScope](https://learn.arm.com/learning-paths/servers-and-cloud-computing/funasr/3_funasr/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/funasr/_next-steps/)

## About this Learning Path

| Skill level:       | Introductory         |
|---------------------|---------------------|
| Reading time:       | 1 hr                |
| Last updated:       | 11 Sep 2026         |

| Author:             | Odin Shen, Arm [GitHub](https://github.com/odincodeshen) [LinkedIn](https://linkedin.com/in/odin-shen-lmshen) |
|---------------------|---------------------|
| Arm IP:             | [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |
| Tags:               | [ML](https://learn.arm.com/tag/ml), [AWS Graviton](https://learn.arm.com/tag/aws-graviton), [Microsoft Azure Cobalt](https://learn.arm.com/tag/microsoft-azure-cobalt), [Google Axion](https://learn.arm.com/tag/google-axion), [Oracle Cloud Infrastructure (OCI) Ampere Compute](https://learn.arm.com/tag/oracle-cloud-infrastructure-oci-ampere-compute), [Linux](https://learn.arm.com/tag/linux), [ModelScope](https://learn.arm.com/tag/modelscope), [FunASR](https://learn.arm.com/tag/funasr), [LLM](https://learn.arm.com/tag/llm), [Generative AI](https://learn.arm.com/tag/generative-ai), [Python](https://learn.arm.com/tag/python) |

## Who is this for?
This is an introductory topic for developers interested in learning how to deploy the ModelScope FunASR Chinese Automatic Speech Recognition (ASR) model on Arm-based servers.

## What will you learn?
Upon completion of this Learning Path, you will be able to:
- Leverage open-source large language models and tools to build Chinese ASR applications.
- Deploy real-time Chinese speech recognition, punctuation restoration, and sentiment analysis using FunASR.
- Describe how to accelerate ModelScope models on Arm-based servers for enhanced performance and efficiency.

## Prerequisites
Before starting, you will need the following:
- An [Arm-based instance](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB of RAM.

## Summary
You’ll deploy a Chinese speech-recognition workflow on Arm-based Linux servers with ModelScope and FunASR. First, you’ll prepare an Arm Ubuntu environment, install the pinned FunASR release, load a pretrained model, and run speech-to-text. Next, you’ll optionally enable punctuation restoration and sentiment analysis, then review the transcription and analysis output from the completed pipeline.

## Frequently asked questions
<details>
<summary>Which FunASR version should I use for the examples?</summary>
Use `funasr==1.2.3`. Results might vary with other versions.
</details>
<details>
<summary>What should I check on my server before installing anything?</summary>
Verify that you’re on an Arm-based machine running Ubuntu 22.04 LTS or later with at least 8 cores, 16GB RAM, and 30GB of free disk space.
</details>
<details>
<summary>What result should I expect when the ASR pipeline runs successfully?</summary>
You should see Chinese speech transcribed to text, with optional punctuation restoration and sentiment analysis outputs.
</details>
<details>
<summary>Where do the models used in the examples come from?</summary>
The models are pre-trained and come from ModelScope, an open-source platform designed to simplify the integration of AI models into applications.
</details>
<details>
<summary>Which Python version does the optimized PyTorch setup require?</summary>
Use Python 3.10. If your current version is lower or higher, install `python3.10`, configure the `python3` alternatives, and confirm the active version with `python --version`.
</details>