Aliyun Qwen Livetranslate

Name: Aliyun Qwen Livetranslate
Author: cinience

cinience/alicloud-skills

54 installs
396 repo stars
Updated July 18, 2026
cinience/alicloud-skills

Run live speech translation with Alibaba Cloud Model Studio Qwen LiveTranslate models for bilingual meetings and realtime interpretation.

About

Uses Model Studio Qwen LiveTranslate models for realtime speech-to-speech or speech-to-text translation. A developer uses it for bilingual meetings and live interpretation flows.

qwen3-livetranslate-flash and realtime variants
Targets meetings, interpretation, and live translation

Aliyun Qwen Livetranslate by the numbers

54 all-time installs (skills.sh)
Ranked #6,877 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
Data as of Aug 5, 2026 (Skillselion catalog sync)

npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-qwen-livetranslate

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Installs	54
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

What it does

Run live speech translation with Alibaba Cloud Model Studio Qwen LiveTranslate models for bilingual meetings and realtime interpretation.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Qwen LiveTranslate

Validation

mkdir -p output/aliyun-qwen-livetranslate
python -m py_compile skills/ai/audio/aliyun-qwen-livetranslate/scripts/prepare_livetranslate_request.py && echo "py_compile_ok" > output/aliyun-qwen-livetranslate/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-livetranslate/validate.txt is generated.

Output And Evidence

Save translation session payloads and response summaries under output/aliyun-qwen-livetranslate/.

Critical model names

Use one of these exact model strings:

qwen3-livetranslate-flash
qwen3-livetranslate-flash-realtime

Typical use

Chinese/English meeting interpretation
Live subtitles in another language
Call-center agent assist with translated captions

Normalized interface (audio.livetranslate)

Request

model (string, optional): default qwen3-livetranslate-flash
source_language (string, required)
target_language (string, required)
audio_format (string, optional): e.g. pcm
sample_rate (int, optional): e.g. 16000

Response

translated_text (string)
source_text (string, optional)
audio_url or audio_chunk (optional, model dependent)

Quick start

python skills/ai/audio/aliyun-qwen-livetranslate/scripts/prepare_livetranslate_request.py \
  --source-language zh \
  --target-language en \
  --output output/aliyun-qwen-livetranslate/request.json

Notes

Prefer the realtime model for continuous streaming sessions.
Prefer the non-realtime flash model for simpler integration and lower client complexity.

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Audio LiveTranslate"
  short_description: "Realtime speech translation with Qwen LiveTranslate"
  default_prompt: "Use $aliyun-qwen-livetranslate to complete this ai/audio translation task on Alibaba Cloud."

#!/usr/bin/env python3
"""Prepare a minimal request payload for Qwen LiveTranslate."""

from __future__ import annotations

import argparse
import json
from pathlib import Path


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--source-language", default="zh")
    parser.add_argument("--target-language", default="en")
    parser.add_argument(
        "--output",
        default="output/aliyun-qwen-livetranslate/request.json",
    )
    args = parser.parse_args()

    payload = {
        "model": "qwen3-livetranslate-flash",
        "source_language": args.source_language,
        "target_language": args.target_language,
        "audio": {
            "format": "pcm",
            "sample_rate": 16000,
            "channels": 1,
        },
    }

    output = Path(args.output)
    output.parent.mkdir(parents=True, exist_ok=True)
    output.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
    print(output)


if __name__ == "__main__":
    main()