Skills gipformer

Vietnamese speech-to-text using Gipformer ASR (65M params, Zipformer-RNNT). Accepts audio of any length — the server handles VAD chunking, batching, and returns the transcript. Supports WAV, FLAC, OGG, MP3, M4A. Activated when the user provides an audio file (WAV, MP3, M4A, FLAC, OGG) or asks to transcribe/recognize Vietnamese speech, e.g. "transcribe this audio", "nhận dạng giọng nói", "chuyển audio thành text".

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/ai-ggroup/gipformer" ~/.claude/skills/clawdbot-skills-gipformer && rm -rf "$T"

manifest: skills/ai-ggroup/gipformer/SKILL.md

source content

Gipformer ASR

Vietnamese speech recognition — send audio of any length, get transcript.

Huggingface Model:

g-group-ai-lab/gipformer-65M-rnnt

(65M params, int8/fp32 ONNX)

Architecture

flowchart TD
    A[Audio file] -->|base64 encode| B[POST /transcribe]
    B --> C[Decode & resample to 16kHz]
    C --> D[VAD chunking ≤ 20s]
    D --> E[Batch inference — sherpa-onnx]
    E --> F[Merge chunk texts]
    F --> G["{ transcript, chunks }"]

The client sends base64-encoded audio (any length, any format). The server decodes, chunks with VAD, infers in batches, and returns the full transcript.

Quick Start

1. Install dependencies

pip install -r {baseDir}/requirements.txt

System dependency:

ffmpeg

(required for M4A support).

2. Start the server

python {baseDir}/scripts/serve.py
# or with options:
python {baseDir}/scripts/serve.py --port 8910 --quantize int8 --max-batch-size 32

The server downloads the ASR model + VAD model on first run and listens on

http://127.0.0.1:8910

3. Transcribe audio

# Single file (any format)
python {baseDir}/scripts/transcribe.py audio.wav
python {baseDir}/scripts/transcribe.py recording.mp3

# Multiple files
python {baseDir}/scripts/transcribe.py *.wav

# JSON output with chunk details
python {baseDir}/scripts/transcribe.py audio.wav --json

# Save results
python {baseDir}/scripts/transcribe.py audio.wav -o results.json

4. Direct API call (curl)

# Transcribe (any length, any format)
curl -X POST http://127.0.0.1:8910/transcribe \
  -H "Content-Type: application/json" \
  -d "{\"audio_b64\": \"$(base64 -i audio.wav)\"}"

# Response:
# { "transcript": "full text...", "duration_s": 120.5, "process_time_s": 5.2,
#   "chunks": [{"text": "...", "start_s": 0.0, "end_s": 8.7}, ...] }

# Health check
curl http://127.0.0.1:8910/health

Audio Format

Format	Extension	Support
WAV	.wav	Native (soundfile)
FLAC	.flac	Native (soundfile)
OGG	.ogg	Native (soundfile)
MP3	.mp3	Native (soundfile)
M4A/AAC	.m4a	Via ffmpeg

All formats are converted to WAV 16-bit PCM mono 16kHz internally.

Server Tuning

Flag	Default	Effect
`--quantize`	int8	`fp32` for accuracy, `int8` for speed/size
`--max-batch-size`	16	Higher = more throughput, more latency
`--max-wait-ms`	100	How long to wait before flushing a partial batch
`--num-threads`	4	ONNX runtime threads
`--decoding-method`	modified_beam_search	`greedy_search` for faster speed

API Reference

See references/api.md for full endpoint documentation.