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Qwen-Audio-3.1-ASR-Message

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Overview

Qwen-Audio-3.1-ASR-Flash-Message is a highly accurate, flexible, and multilingual speech recognition model based on a large language model, featuring long-context capabilities. It not only supports the seamless transcription of ultra-long audio but also allows users to specify multiple languages ​​and specialized industry vocabulary. By integrating two distinct mechanisms for weighting custom keywords, the model achieves precise alignment in complex and specialized scenarios, significantly enhancing speech recognition accuracy in specific domains.

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

  • Input
    $0.93Per 1M tokens
  • Output
    $0.7Per 1M tokens

Rate Limits

  • TPMTokens Per Minute
    6M
  • RPMRequests Per Minute
    1K

API Reference

Call API
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from http import HTTPStatus
import dashscope
from dashscope.audio.asr import Recognition
import os


dashscope.api_key = os.environ.get('DASHSCOPE_API_KEY')
dashscope.base_websocket_api_url='wss://ap-southeast-1.maas.qwencloudapi.com/api-ws/v1/inference'

recognition = Recognition(model='qwen-audio-3.1-asr-flash-message',
                          format='wav',
                          sample_rate=16000,
                          callback=None)
result = recognition.call('{YOUR_AUDIO_FILE}')
if result.status_code == HTTPStatus.OK:
    print('Recognition result: ')
    print(result.get_sentence())
else:
    print('Error: ', result.message)
    
print(
    '[Metric] requestId: {}, first package delay ms: {}, last package delay ms: {}'
    .format(
        recognition.get_last_request_id(),
        recognition.get_first_package_delay(),
        recognition.get_last_package_delay(),
    ))