xiaoyuzhou-asr

by worldwonderer

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SKILL.md About

---
name: xiaoyuzhou-asr
description: >
  Transcribe 小宇宙 (Xiaoyuzhou) podcast episodes to text using local Qwen3-ASR speech recognition.
  Combines xyz API (小宇宙FM API) to fetch episode metadata and audio URLs with qwen3-asr-rs for
  local GPU-accelerated speech-to-text. Use when user wants to: (1) transcribe a 小宇宙 podcast episode,
  (2) download podcast audio from 小宇宙, (3) search and transcribe podcast content, (4) batch transcribe
  multiple episodes. Triggers: 小宇宙, podcast transcription, 播客转文字, ASR, speech recognition,
  episode transcript, 节目转录, qwen3-asr, 小宇宙转录.
---

# xiaoyuzhou-asr

Transcribe 小宇宙 podcast episodes to text using local Qwen3-ASR (Metal/CUDA accelerated).

## Prerequisites

1. **xyz API server** running — fetches episode data and audio URLs from 小宇宙
   ```bash
   git clone https://github.com/ultrazg/xyz.git && cd xyz && go run .
   # Default port: 23020, change with -p
   ```
2. **Access token** — login via `POST /sendCode` then `POST /login` (see [references/xyz-api.md](references/xyz-api.md))
3. **ffmpeg** — audio format conversion (`brew install ffmpeg`)
4. **Qwen3-ASR model** — download (HF Hub does NOT ship tokenizer.json):
   ```bash
   python3 -c "
   from huggingface_hub import snapshot_download
   snapshot_download('Qwen/Qwen3-ASR-0.6B', local_dir='models/0.6B')
   "
   ```
5. **qwen3-asr-rs** — build from source:
   ```bash
   git clone https://github.com/alan890104/qwen3-asr-rs.git && cd qwen3-asr-rs
   cargo build --release --example local_transcribe
   ```
6. **tokenizer.json** — auto-generated by the transcription script on first run (from vocab.json + merges.txt). No manual step needed.

## Workflow

### Step 1: Find Episode

```bash
TOKEN="$XYZ_ACCESS_TOKEN"
BASE="http://localhost:23020"

# Search episodes by keyword
curl -s -X POST $BASE/search \
  -H "x-jike-access-token: $TOKEN" -H "Content-Type: application/json" \
  -d '{"keyword":"关键词","type":"EPISODE"}'

# Get episode detail (contains audio URL)
curl -s -X POST $BASE/episode_detail \
  -H "x-jike-access-token: $TOKEN" -H "Content-Type: application/json" \
  -d '{"eid":"EPISODE_ID"}'

# List episodes of a podcast
curl -s -X POST $BASE/episode_list \
  -H "x-jike-access-token: $TOKEN" -H "Content-Type: application/json" \
  -d '{"pid":"PODCAST_ID","order":"desc"}'
```

### Step 2: Download and Convert Audio

Audio URL is in `data.data.media.source.url` (m4a format).

```bash
mkdir -p /tmp/xiaoyuzhou-audio
curl -L -o /tmp/xiaoyuzhou-audio/episode.m4a "$AUDIO_URL"
ffmpeg -y -i /tmp/xiaoyuzhou-audio/episode.m4a -ar 16000 -ac 1 /tmp/xiaoyuzhou-audio/episode.wav
```

### Step 3: Split Long Audio (REQUIRED for >3 min)

Podcasts are continuous speech with few silence gaps. Use fixed-interval splitting:
```bash
# Split into 3-minute segments (must split at ≥2 min for Metal GPU memory)
ffmpeg -y -i episode.wav -f segment -segment_time 180 -ar 16000 -ac 1 seg_%03d.wav
```

Or try silence-based splitting (may find no gaps in continuous podcasts):
```bash
ffmpeg -i episode.wav -af "silencedetect=noise=-30dB:d=2" -f null - 2>&1 | grep silence_end
ffmpeg -i episode.wav -f segment -segment_times T1,T2 -ar 16000 -ac 1 seg_%03d.wav
```

### Step 4: Transcribe

```bash
MODEL_DIR="/path/to/models/0.6B"
ASR_BIN="qwen3-asr-rs/target/release/examples/local_transcribe"

# Transcribe each segment
for seg in seg_*.wav; do
  $ASR_BIN $MODEL_DIR $seg 2>/dev/null | grep "^Text     :" | sed 's/^Text     : //'
done
```

For efficiency (load model once in Rust):
```rust
use qwen3_asr::{AsrInference, TranscribeOptions, best_device};
let engine = AsrInference::load("models/0.6B", best_device())?;
for seg in segments {
    let result = engine.transcribe(&seg, TranscribeOptions::default())?;
    output.push(result.text);
}
```

### Step 5: Format Output

Combine transcript with metadata as markdown:
```markdown
# {title}
**节目**: {podcast.title} | **日期**: {pubDate} | **时长**: {duration}s

## 转录文本
{transcript}
```

## References

- **xyz API endpoints and auth**: [references/xyz-api.md](references/xyz-api.md)
- **Qwen3-ASR usage and performance**: [references/qwen3-asr.md](references/qwen3-asr.md)

## Token Management

- Tokens expire. If API returns 401, refresh: `POST /refresh_token`
- Store in env: `XYZ_ACCESS_TOKEN`, `XYZ_REFRESH_TOKEN`
- Prompt user to login if no valid token

## Constraints

- **MUST split audio** into ≤3-minute segments for Metal GPU stability
- Audio must be WAV 16kHz mono
- tokenizer.json must be generated manually (not included in HF download)
- `local_transcribe` binary needed (demo binary only runs built-in test samples)
- xyz API requires Chinese phone number (+86) login
- All processing is local — audio never leaves the machine

_meta.json Metadata

{
  "ownerId": "kn7e14qz6v4n71xmjegh68jtts80dp5r",
  "publishedAt": 1777652716315,
  "slug": "xiaoyuzhou-asr",
  "version": "1.0.0"
}