Model Marketplace

Fun-ASR

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Overview

The Fun ASR version, updated in April 2026, fully supports the seven major dialect systems of traditional Chinese (Mandarin/Wu/Xiang/Gan/Hakka/Min/Yue) and is compatible with over 20 regional Mandarin accents. It features specific optimizations for the rhythm, meter, and literary expression characteristics of classical Chinese poetry, improving the accuracy of poetry recognition and making it suitable for cultural heritage, educational explanations, and audiobooks. Improved punctuation prediction and text normalization capabilities make the output text more consistent with written expression habits; numbers, dates, and amounts are automatically converted to standard formats, enhancing readability and professionalism. The language support has been expanded to include English, Japanese, Korean, Vietnamese, Thai, Indonesian, Malay, Filipino, Hindi, Arabic, French, German, Spanish, Portuguese, Russian, Italian, Dutch, Swedish, Danish, Finnish, Norwegian, Greek, Polish, Czech, Hungarian, Romanian, Bulgarian, Croatian, and Slovak, totaling 30 languages. This version is equivalent to the snapshot released on November 7, 2025.

Input

Audio

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Audio Duration
    $0.000035Per second

Rate Limits

  • RPMRequests Per Minute
    600

API Reference

Call API
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from http import HTTPStatus
from dashscope.audio.asr import Transcription
import dashscope
import os
import json

dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")

task_response = Transcription.async_call(
    model='fun-asr',
    file_urls=['https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav',
               'https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav']
)

transcribe_response = Transcription.wait(task=task_response.output.task_id)
if transcribe_response.status_code == HTTPStatus.OK:
    print(json.dumps(transcribe_response.output, indent=4, ensure_ascii=False))
    print('transcription done!')