AI Agent Hub
Back to plugins
🤖

xby-asr-zh

Model Inference Updated 2026.09.02

Run the following command in DeepSeek Harness:

dsh plugin install xby-skill/xby-asr-zh

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install xby-skill/xby-asr-zh inside DeepSeek Harness to install this plugin; the source code is available at https://github.com/xby-skill/xby-asr-zh

About this plugin

If your DSH workflow keeps pulling you out of the conversation to transcribe a Chinese audio clip into text, xby-asr-zh closes that loop. One command inside the chat takes audio in and returns clean text, no tab-switching to an external tool required.

Under the hood it runs the Paraformer INT8-quantised model, tuned specifically for Chinese speech, delivering fast inference with a small memory footprint. The plugin accepts audio in three shapes — a plain URL, a Base64-encoded payload, or a local file path — so whatever the audio is sitting in right now, you can hand it straight to the model without a conversion step. The API key you set during the chat is persisted automatically, meaning a restart does not wipe your configuration.

It is a natural fit when you are building voice-assistant pipelines, transcribing meeting recordings, processing podcast episodes, or simply need to turn a Chinese voice memo into searchable text without leaving the DSH environment. Once the plugin is installed and the key is in place, every subsequent recognition call is a single function invocation.

Use Cases

  • Transcribe a Chinese audio clip to text right inside a DSH conversation without switching to an external tool
  • Batch-convert Chinese meeting recordings or podcast episodes into searchable text for easy editing
  • Call a ready-made Chinese ASR endpoint inside a voice-assistant or chatbot pipeline without deploying extra services

Best For

  • Developers who work in DSH and regularly handle Chinese audio content
  • Engineers building Chinese voice assistants or chatbots who want to skip the model deployment step
  • Tech teams that need a plug-and-play speech-to-text capability embedded in a conversational workflow