Alicloud Ai Audio Asr Realtime

Name: Alicloud Ai Audio Asr Realtime
Author: cinience

cinience/alicloud-skills

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

Prepares low-latency realtime speech recognition requests for Alibaba Cloud Model Studio Qwen ASR Realtime models for streaming input and live captions.

About

Builds realtime streaming speech-recognition requests against Alibaba Cloud Model Studio Qwen ASR Realtime models. A developer uses it for low-latency transcription such as live captions or duplex voice agents.

Targets Qwen ASR Realtime streaming models
Includes a py_compile validation step and output evidence dir

Alicloud Ai Audio Asr Realtime by the numbers

95 all-time installs (skills.sh)
Ranked #4,580 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 alicloud-ai-audio-asr-realtime

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Installs	95
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

What it does

Prepares low-latency realtime speech recognition requests for Alibaba Cloud Model Studio Qwen ASR Realtime models for streaming input and live captions.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Qwen ASR Realtime

Validation

mkdir -p output/alicloud-ai-audio-asr-realtime
python -m py_compile skills/ai/audio/alicloud-ai-audio-asr-realtime/scripts/prepare_realtime_asr_request.py && echo "py_compile_ok" > output/alicloud-ai-audio-asr-realtime/validate.txt

Pass criteria: command exits 0 and output/alicloud-ai-audio-asr-realtime/validate.txt is generated.

Output And Evidence

Save session payloads and response samples under output/alicloud-ai-audio-asr-realtime/.

Critical model names

Use one of these exact model strings:

qwen3-asr-flash-realtime
qwen3-asr-flash-realtime-2026-02-10

Use cases

Realtime subtitles and captions
Voice-agent duplex input
Streaming speech-to-text in browser or terminal clients

Prerequisites

Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
Realtime sessions generally require WebSocket or streaming session handling in the client.

Normalized interface (asr.realtime)

Request

model (string, optional): default qwen3-asr-flash-realtime
language_hints (array<string>, optional)
format (string, optional): e.g. pcm, wav
sample_rate (int, optional): e.g. 16000
chunk_ms (int, optional): frame size in milliseconds

Response

text (string): recognized transcript fragment
is_final (bool): finalization marker
usage (object, optional)

Quick start

Generate a request template:

python skills/ai/audio/alicloud-ai-audio-asr-realtime/scripts/prepare_realtime_asr_request.py \
  --output output/alicloud-ai-audio-asr-realtime/request.json

Operational guidance

Prefer 16kHz mono PCM unless your client stack requires another format.
Keep chunks small enough for responsive partial results.
If you only have recorded files, use skills/ai/audio/alicloud-ai-audio-asr/ instead.

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Audio ASR Realtime"
  short_description: "Realtime speech recognition with Qwen ASR Realtime"
  default_prompt: "Use $alicloud-ai-audio-asr-realtime to complete this ai/audio realtime ASR task on Alibaba Cloud."

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

from __future__ import annotations

import argparse
import json
from pathlib import Path

DEFAULT_PAYLOAD = {
    "model": "qwen3-asr-flash-realtime",
    "input_audio": {
        "format": "pcm",
        "sample_rate": 16000,
        "channels": 1,
    },
    "language_hints": ["zh", "en"],
    "chunk_ms": 100,
}


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--output",
        default="output/alicloud-ai-audio-asr-realtime/request.json",
        help="Path to write the sample request payload.",
    )
    args = parser.parse_args()

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


if __name__ == "__main__":
    main()