Xiaoyuzhou Asr

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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.

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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

   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) 3. ffmpeg — audio format conversion (brew install ffmpeg) 4. Qwen3-ASR model — download (HF Hub does NOT ship tokenizer.json):

   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:

   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

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).

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:

# 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):

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

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):

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:

# {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

Qwen3-ASR usage and performance: 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

1. xyz API server running — fetches episode data and audio URLs from 小宇宙

   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) 3. ffmpeg — audio format conversion (brew install ffmpeg) 4. Qwen3-ASR model — download (HF Hub does NOT ship tokenizer.json):

   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:

   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.

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