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README.md

@@ -2,19 +2,15 @@
 tags:
   - audio
   - speech-to-text
-  - speaker-diarization
-  - speaker-embedding
   - voxtral
-  - pyannote
-  - funasr
   - meetingmind
 library_name: custom
 pipeline_tag: automatic-speech-recognition
 ---
 
-# MeetingMind GPU Endpoint
+# MeetingMind Voxtral Transcription Endpoint
 
-GPU-accelerated speech-to-text, speaker diarization, and embedding extraction for the MeetingMind pipeline. Runs as an HF Inference Endpoint on a T4 GPU with scale-to-zero.
+GPU-accelerated speech-to-text for the MeetingMind pipeline using Voxtral Realtime 4B. Runs as an HF Inference Endpoint on a T4 GPU with scale-to-zero.
 
 **Model weights**: [`mistral-hackaton-2026/voxtral_model`](https://huggingface.co/mistral-hackaton-2026/voxtral_model) — Voxtral Realtime 4B (BF16 safetensors, loaded from `/repository/voxtral-model/`)
 
@@ -60,58 +56,15 @@ curl -X POST \
 
 Events: `token` (partial), `done` (final text), `error`.
 
-### `POST /diarize`
-
-Speaker diarization using pyannote v4. Accepts any audio format (FLAC, WAV, MP3, etc.).
-
-```bash
-curl -X POST \
-  -H "Authorization: Bearer $HF_TOKEN" \
-  -F audio=@meeting.flac \
-  -F min_speakers=2 \
-  -F max_speakers=6 \
-  $ENDPOINT_URL/diarize
-```
-
-```json
-{
-  "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.
-
-```bash
-curl -X POST \
-  -H "Authorization: Bearer $HF_TOKEN" \
-  -F audio=@meeting.flac \
-  -F start_time=1.0 \
-  -F end_time=5.0 \
-  $ENDPOINT_URL/embed
-```
-
-```json
-{"embedding": [0.012, -0.034, ...], "dim": 192}
-```
-
 ## Environment Variables
 
 | Variable | Default | Description |
 |---|---|---|
-| `HF_TOKEN` | (required) | Hugging Face token for pyannote model access |
 | `VOXTRAL_MODEL_DIR` | `/repository/voxtral-model` | Path to Voxtral model weights |
-| `PYANNOTE_MIN_SPEAKERS` | `1` | Minimum speakers for diarization |
-| `PYANNOTE_MAX_SPEAKERS` | `10` | Maximum speakers for diarization |
 
 ## Architecture
 
 - **Base image**: `pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime`
 - **Transcription**: Voxtral Realtime 4B via direct safetensors loading (~8GB VRAM)
-- **Diarization**: pyannote/speaker-diarization-community-1 (~2GB VRAM)
-- **Embeddings**: FunASR CAM++ sv_zh-cn_16k-common (~200MB)
 - **Scale-to-zero**: 15 min idle timeout (~$0.60/hr when active)
+- **Diarization & embeddings**: Served separately by the GPU service on machine "tanti"