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#

Rendering diagram...

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#

bash

pip install -r {baseDir}/requirements.txt

System dependency: ffmpeg (required for M4A support).

2. Start the server#

bash

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#

bash

# 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)#

bash

# 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#

FormatExtensionSupportWAV.wavNative (soundfile)FLAC.flacNative (soundfile)OGG.oggNative (soundfile)MP3.mp3Native (soundfile)M4A/AAC.m4aVia ffmpeg

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

Server Tuning#

FlagDefaultEffect--quantizeint8fp32 for accuracy, int8 for speed/size--max-batch-size16Higher = more throughput, more latency--max-wait-ms100How long to wait before flushing a partial batch--num-threads4ONNX runtime threads--decoding-methodmodified_beam_searchgreedy_search for faster speed

API Reference#

See references/api.md for full endpoint documentation.