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

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

Server Tuning

API Reference

See references/api.md for full endpoint documentation.