Local Qwen3 Asr Aipc

Local Speech Recognition (Windows · Qwen3-ASR · OpenVINO)

Model: snake7gun/Qwen3-ASR-0.6B-fp16-ov (ModelScope FP16) SKILL_VERSION: 'v1.0.1'

First time? Before using this skill, run these two scripts once in a terminal:

```

python setup.py # creates venv, installs dependencies (~5 min)

python download_model.py # downloads the model (~2 GB, resumable)

```

Both scripts are in the skill directory alongside this SKILL.md.

---

Agent Routing (When To Call acoustic_pipeline.py)

Use acoustic_pipeline.py as the default agent entry when any of these are true:

1. Input is a video file (.mp4, .mkv, .webm, .mov, .avi, etc.) 2. User requests folder watch (--watch) or batch transcription (--batch) 3. User requests transcript archiving (--archive txt|json|both) 4. Environment may be missing and auto-bootstrap is needed (--auto-bootstrap)

Use legacy transcribe.py direct flow only for simple single-audio-file requests where no watch/batch/archive/bootstrap behavior is needed.

Recommended defaults:

python acoustic_pipeline.py --file "<FILE_PATH>" --language auto --archive json --auto-bootstrap
python acoustic_pipeline.py --watch "<DIR_PATH>" --language auto --archive both --auto-bootstrap
python acoustic_pipeline.py --batch "<DIR_PATH>" --language auto --archive json --auto-bootstrap

---

Skill Contract (Input / Output)

Accepted Inputs

Any agent should treat this skill as a local audio/video transcription skill.

1. Single file path

• Audio: .wav, .mp3, .flac, .m4a, .ogg, .aac, .wma, .opus
• Video: .mp4, .mkv, .webm, .flv, .mov, .avi, .mts, .m2ts, .ts, .m3u8

2. Folder path

• Watch mode: continuously process new files in folder
• Batch mode: process existing files in folder recursively

3. Runtime options

• language: auto or explicit language name
• archive: none | txt | json | both
• archive_dir: optional output folder for transcript files
• auto_bootstrap: initialize ASR automatically when environment is missing

Output On Success

The result should be returned as a JSON object (or equivalent dictionary) with:

• text: transcription content
• language: detected or requested language
• source_file: original input path
• source_format: source extension
• confidence: optional confidence value (if available)
• archive_files: optional object containing txt/json output paths

Example shape:

{
    "text": "...",
    "language": "Chinese",
    "source_file": "D:\\demo\\meeting.mp4",
    "source_format": ".mp4",
    "confidence": null,
    "archive_files": {
        "json": "D:\\demo\\transcripts\\meeting_20260326_120000.json"
    }
}

Output On Failure

The agent should return a short structured error summary including:

• error: human-readable failure reason
• stage: bootstrap | extract_audio | transcribe | archive
• source_file: input path (if known)
• recoverable: true if retry is reasonable

---

⚠️ Agent instructions

1. Windows / PowerShell only. Never use Linux commands (ls, rm, cat). Never use && or call. 2. Every step reads `state.json` itself — do not pass paths between steps manually. 3. Use `VENV_PY` from state.json for inference calls — never use system python for inference. Exception: check_env.py uses only standard library and can be run with system python. 4. transcribe.py is automatically deployed to ASR_DIR when setup.py runs. If it is missing, re-run setup.py. 5. If `transcribe.py` fails at runtime, do NOT edit it manually. Re-run setup.py to redeploy the original from the repository. 6. Never diagnose "model issues" without running Step 1's check script first. The model layout may use a thinker/ subdirectory — the scripts handle this automatically. 7. Do not generate helper scripts in agent workspace directories. Use repository-managed check_env.py instead. 8. Goal: transcribe the audio file and send the result to the conversation.

Auto-recovery policy — try before asking user: ⚠️ Network handling (proxy-aware)

When running setup.py or download_model.py: 1. Try to read system proxy settings:

• Environment variables: HTTP_PROXY / HTTPS_PROXY
• Windows WinHTTP proxy (netsh winhttp show proxy)

2. If proxy is detected:

• Automatically apply it
• Continue installation / download

3. If no proxy is detected:

• Continue normally (direct connection)

4. If download/setup fails:

• Inform the user whether a proxy was detected
• If no proxy was detected, suggest configuring one
• Then retry

IMPORTANT:

• Many networks (corporate / China mainland / campus) require proxy
• Download supports resume — safe to retry after fixing network

• If STATE=MISSING or VENV_PY=BROKEN: automatically run setup.py (up to 3 attempts). Only ask user to run it manually if all 3 attempts fail.
• If MODEL_STATUS=MISSING: automatically run download_model.py (up to 3 attempts). Only ask user to run it manually if all 3 attempts fail or if a single attempt runs longer than 8 minutes without completing (likely a slow connection — download_model.py supports resume so partial progress is not lost).
• Always announce what you are doing before each attempt: "⚙️ 正在自动安装环境（第 N/3 次尝试）…"

Pipeline — follow exactly in order, no skipping:

Step 0: parse request       → AUDIO_PATH, LANGUAGE, TOPIC
Step 1: verify environment  → run check_env.py → VENV_PY, ASR_DIR confirmed ready
         ↳ if STATE=MISSING or VENV_BROKEN: auto-run setup.py (3 attempts)
         ↳ if MODEL_STATUS=MISSING: auto-run download_model.py (3 attempts)
Step 2: transcribe + send   → prefer acoustic_pipeline.py; fallback to runtime transcribe.py
         (transcribe.py is auto-deployed by setup.py — no separate agent step needed)

---

Step 0: parse request (LLM only — no tools)

Extract from the user's message:

If no audio file provided, ask the user before continuing.

---

Step 1: verify environment and model

🔍 Step 1/3: checking environment and model…

python "<skill_dir>\check_env.py"

On success: record VENV_PY and ASR_DIR from output, proceed to Step 2.

On failure — auto-recovery (try before asking user):

If STATE=MISSING or VENV_PY=BROKEN → auto-run setup.py

Announce and run (up to 3 attempts):

⚙️ 环境未初始化，正在自动安装（第 1/3 次尝试）…

python "<skill_dir>\setup.py"

After each attempt, re-run check_env.py to verify. If all 3 attempts fail, show manual fallback below.

If MODEL_STATUS=MISSING → auto-run download_model.py

Announce and run (up to 3 attempts, stop if a single attempt exceeds 8 minutes):

📥 模型未找到，正在自动下载（第 1/3 次尝试）…
   预计耗时：100Mbps 约 3 分钟 · 50Mbps 约 5 分钟
   下载支持断点续传，中断后重新运行可继续。

python "<skill_dir>\download_model.py"

After each attempt, re-run check_env.py to verify. If all 3 attempts fail, show manual fallback below.

Manual fallback (only if all 3 auto-attempts fail)

Show user this message:

⚠️ 自动安装失败，需要手动操作。

请打开 Windows 终端（PowerShell 或命令提示符），依次运行：

① 安装环境（如果还未安装）：
   python "<skill_dir>\setup.py"
   预计耗时约 5 分钟，全程自动完成。

② 下载模型（约 2 GB）：
   python "<skill_dir>\download_model.py"
   下载支持断点续传，中断后重新运行可继续。
   预计耗时：100 Mbps → 约 3 分钟 · 50 Mbps → 约 5 分钟

完成后回到这里重新发送请求。

---

Step 2: transcribe and send result

🎤 Step 2/2: transcribing…

Preferred path — use acoustic_pipeline.py for all standard requests:

python "<skill_dir>\acoustic_pipeline.py" --file "AUDIO_PATH" --language auto --archive json --auto-bootstrap

If the user specified a language:

python "<skill_dir>\acoustic_pipeline.py" --file "AUDIO_PATH" --language "LANGUAGE" --archive json --auto-bootstrap

Return the JSON result directly to the conversation and include any archive_files paths.

Fallback — only when direct low-level runtime behavior is explicitly required:

$env:PYTHONUTF8 = "1"
& "<VENV_PY>" "<ASR_DIR>\transcribe.py" --audio "AUDIO_PATH" --language "LANGUAGE" --topic "TOPIC"

Pass: stdout contains [SUCCESS] and [RESULT]. Record:

• OUTPUT_PATH — path from [SUCCESS] line
• TRANSCRIPT — text after [RESULT]
• LANG — language from [INFO] line

Send via message tool:

action: "send"  filePath: "OUTPUT_PATH"  message: "✅ TOPIC | LANG\n\nTRANSCRIPT"

---

Troubleshooting

---

LLM API Usage

For agents that prefer a Python import interface over shell commands:

from asr_api import transcribe_audio

# Basic usage — auto language detection
result = transcribe_audio("C:\\meeting.mp4")
print(result["text"])

# With options
result = transcribe_audio(
    file_path="C:\\lecture.mp4",
    language="Chinese",
    archive_mode="json",       # save transcript to file
    auto_bootstrap=True,       # auto-install env if missing
)
print(result["text"])
print(result.get("archive_files"))  # path to saved json

asr_api.py is in the skill directory and wraps acoustic_pipeline.py internally.

---

Workspace Hygiene

• Do not generate helper scripts in agent workspace directories (e.g. .openclaw\workspace\check_asr_env.ps1).
• Use repository-managed check_env.py — it is versioned, auditable, and repeatable.
• If the execution environment forces a temporary file, treat it as disposable and remove it after the command completes.