Aye10032/Qwen3-ASR-Refiner-4B

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 23, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Aye10032/Qwen3-ASR-Refiner-4B is a 4 billion parameter model from the Qwen3 family, developed by Aye10032. It is specifically fine-tuned to convert Chinese Automatic Speech Recognition (ASR) transcripts and other spoken-style text into formal, natural written Chinese. This model excels at refining informal spoken language into concise written text while strictly preserving the original meaning, making it ideal for post-processing ASR outputs.

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Overview

Aye10032/Qwen3-ASR-Refiner-4B is a specialized 4 billion parameter model built upon the Qwen3-4B base architecture. Developed by Aye10032, this model is part of a family designed for a unique natural language processing task: refining Chinese spoken-style text into formal, natural written Chinese. It ensures that the original meaning is preserved without introducing new information.

Key Capabilities

  • Chinese ASR Transcript Refinement: Transforms raw ASR outputs into polished, written Chinese.
  • Spoken-to-Written Conversion: Converts informal spoken Chinese into a more formal and natural written style.
  • Meaning Preservation: Explicitly designed to maintain the original intent and information content of the input text.
  • Concise Output: Focuses on generating concise written text, removing conversational fillers and redundancies.

Training and Implementation

This model, along with its 0.6B and 1.7B variants, was fine-tuned on the Aye10032/WenetSpeech-Formal-Text dataset. The training recipe and task definition are consistent across all variants. The LoRA adapter used during fine-tuning has been merged into the base model, allowing it to be loaded directly as complete BF16 Transformers weights without requiring PEFT.

Use Cases

This model is particularly well-suited for applications requiring the conversion of spoken Chinese into high-quality written text, such as:

  • Post-processing for Automatic Speech Recognition (ASR) systems.
  • Generating formal summaries or reports from spoken dialogues.
  • Enhancing the readability of transcribed interviews or meetings.

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