meetingmind-gpu

MeetingMind GPU Service

GPU-accelerated speaker diarization and embedding extraction for the MeetingMind pipeline. Runs as an HF Inference Endpoint on a T4 GPU with scale-to-zero.

API

GET /health

Returns service status and GPU availability.

curl -H "Authorization: Bearer $HF_TOKEN" $ENDPOINT_URL/health

{"status": "ok", "gpu_available": true}

POST /diarize

Speaker diarization using pyannote v4. Accepts any audio format (FLAC, WAV, MP3, etc.).

curl -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -F [email protected] \
  -F min_speakers=2 \
  -F max_speakers=6 \
  $ENDPOINT_URL/diarize

{
  "segments": [
    {"speaker": "SPEAKER_00", "start": 0.5, "end": 3.2, "duration": 2.7},
    {"speaker": "SPEAKER_01", "start": 3.4, "end": 7.1, "duration": 3.7}
  ]
}

POST /embed

Speaker embedding extraction using FunASR CAM++. Returns L2-normalized 192-dim vectors for voiceprint matching.

curl -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -F [email protected] \
  -F start_time=1.0 \
  -F end_time=5.0 \
  $ENDPOINT_URL/embed

{"embedding": [0.012, -0.034, ...], "dim": 192}

Environment Variables

Architecture

• Base image: pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime
• Diarization: pyannote/speaker-diarization-community-1 (~2GB VRAM)
• Embeddings: FunASR CAM++ sv_zh-cn_16k-common (~200MB)
• Total VRAM: ~3GB (fits T4 16GB with headroom)
• Scale-to-zero: 15 min idle timeout (~$0.60/hr when active)