Update README.md

README.md

@@ -1,201 +1,12 @@
 ---
-title: RVC-v2 + Beatrice-v2 Voice Conversion + training
-emoji: 🎤
-colorFrom: red
-colorTo: yellow
+title: Internal Engine
+emoji: ⚙️
+colorFrom: gray
+colorTo: gray
 sdk: gradio
 python_version: "3.10"
 sdk_version: 6.3.0
 app_file: app.py
 pinned: false
 license: mit
-tags:
-  - voice-conversion
-  - rvc
-  - beatrice
-  - beatrice-v2
-  - audio
-  - mcp-server
-short_description: RVC-v2 Beatrice-v2 - CPU inference + training
----
-
-# RVC + Beatrice Voice Conversion
-
-**CPU Inference + Training** - Single-file app for HuggingFace Spaces.
-
-## Features
-
-- **Voice Conversion** - RVC v2 (.pth) + Beatrice v2 (.pt.gz)
-- **Training** - Train both model types (GPU recommended, CPU works but slow)
-- **Single File** - Everything in one `app.py`
-- **CLI Support** - Command-line interface for batch processing
-
-## Voice Conversion
-
-1. Upload source audio (any format)
-2. Select **Model Type**: RVC v2 or Beatrice v2
-3. Upload model file (.pth for RVC, .pt.gz for Beatrice)
-4. Adjust pitch shift if needed
-5. Click Convert
-
-**Default model:** [audo/Benee-RVC](https://huggingface.co/audo/Benee-RVC) (RVC v2)
-
-## Training
-
-1. Select **Trainer**: RVC v2 or Beatrice v2
-2. Upload training audio (10+ minutes recommended)
-3. Enter model name
-4. Adjust epochs and batch size
-5. Click Start Training
-
-**Note:** CPU training works but is slow. For faster training, clone locally with CUDA GPU.
-
-## Compatibility
-
-- **RVC v1** (256-dim HuBERT) - with f0 or no-f0
-- **RVC v2** (768-dim HuBERT) - with f0 or no-f0
-- **Beatrice v2** - 16kHz input, 24kHz output, per-speaker VQ
-- **Index retrieval** (.index files) for RVC voice matching
-
-Model version and f0 flag are auto-detected from the checkpoint.
-
-Find models: [HuggingFace](https://huggingface.co/models?search=rvc) | [Weights.gg](https://weights.gg)
-
----
-
-## API
-
-### Python Client - Voice Conversion
-
-```python
-from gradio_client import Client, handle_file
-
-client = Client("Luminia/rvc-voice-conversion")
-
-# RVC v2 inference
-result = client.predict(
-    source_audio=handle_file("voice.wav"),
-    model_type="RVC v2",
-    model_file=handle_file("model.pth"),
-    index_file=None,                  # Optional .index file
-    beatrice_model_file=None,         # Not used for RVC
-    beatrice_target_speaker=0,        # Not used for RVC
-    beatrice_formant_shift=0.0,       # Not used for RVC
-    pitch_shift=0,                    # -12 to 12 semitones
-    f0_method="pm",                   # "pm" or "harvest"
-    index_rate=0.75,                  # 0-1, voice retrieval strength
-    protect=0.33,                     # 0-0.5, voiceless consonant protection
-    api_name="/convert"
-)
-print(result)  # (output_path, status_message)
-
-# Beatrice v2 inference
-result = client.predict(
-    source_audio=handle_file("voice.wav"),
-    model_type="Beatrice v2",
-    model_file=None,                  # Not used for Beatrice
-    index_file=None,                  # Not used for Beatrice
-    beatrice_model_file=handle_file("beatrice_model.pt.gz"),
-    beatrice_target_speaker=0,        # Speaker index
-    beatrice_formant_shift=0.0,       # -2 to 2
-    pitch_shift=0,                    # -12 to 12 semitones
-    f0_method="pm",                   # Ignored for Beatrice
-    index_rate=0.75,                  # Ignored for Beatrice
-    protect=0.33,                     # Ignored for Beatrice
-    api_name="/convert"
-)
-print(result)  # (output_path, status_message)
-```
-
-### Python Client - Training
-
-```python
-from gradio_client import Client, handle_file
-
-client = Client("Luminia/rvc-voice-conversion")
-
-# RVC v2 training
-result = client.predict(
-    trainer="RVC v2",
-    train_audio=handle_file("voice.wav"),
-    train_model_name="my_voice",
-    train_epochs=50,                  # 50-500
-    train_batch=2,                    # Batch size
-    train_sr=40000,                   # 32000, 40000, or 48000
-    beatrice_epochs=30,               # Ignored for RVC
-    beatrice_batch=8,                 # Ignored for RVC
-    beatrice_resume=False,            # Ignored for RVC
-    api_name="/train"
-)
-print(result)  # (model_path, status_log)
-
-# Beatrice v2 training
-result = client.predict(
-    trainer="Beatrice v2",
-    train_audio=handle_file("voice.wav"),
-    train_model_name="my_voice",
-    train_epochs=50,                  # Ignored for Beatrice
-    train_batch=2,                    # Ignored for Beatrice
-    train_sr=40000,                   # Ignored for Beatrice
-    beatrice_epochs=30,               # 20-50 recommended
-    beatrice_batch=8,                 # Batch size
-    beatrice_resume=False,            # Resume from checkpoint
-    api_name="/train"
-)
-print(result)  # (model_path, status_log)
-```
-
-### MCP (Model Context Protocol)
-
-This Space supports MCP for AI assistants (Claude Desktop, Cursor, VS Code).
-
-1. Click **MCP** badge → **Add to MCP tools**
-2. The `convert` and `train` tools become available
-
-**MCP Config:**
-```json
-{
-  "mcpServers": {
-    "rvc": {"url": "https://luminia-rvc-voice-conversion.hf.space/gradio_api/mcp/"}
-  }
-}
-```
-
----
-
-## CLI Usage
-
-### Inference
-```bash
-# RVC v2
-python app.py infer -i voice.wav -m model.pth -o output.wav
-
-# Beatrice v2 (auto-detected from .pt.gz extension)
-python app.py infer -i voice.wav -m beatrice_model.pt.gz -o output.wav
-
-# With pitch shift
-python app.py infer -i voice.wav -m model.pth -p 2 -o output.wav
-
-# Beatrice with speaker/formant options
-python app.py infer -i voice.wav -m beatrice.pt.gz --speaker 0 --formant-shift 1.0 -o output.wav
-```
-
-### Training
-```bash
-# RVC v2 training
-python app.py train -a voice.mp3 -o ./my_model --epochs 100
-
-# Beatrice v2 training
-python app.py train-beatrice -a voice.mp3 -o ./beatrice_model --epochs 30
-
-# Beatrice resume training
-python app.py train-beatrice -a voice.mp3 -o ./beatrice_model --epochs 30 --resume
-```
-
----
-
-- Local real-time model usage: https://huggingface.co/wok000/vcclient000/tree/main 
-
-## Credits
-
-Based on [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)-[Mangio-UI-Fork](https://github.com/Mangio621/Mangio-RVC-Fork), [Applio](https://github.com/IAHispano/Applio) data processing, and [Beatrice v2](https://huggingface.co/fierce-cats/beatrice-trainer)
+---