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README.md

@@ -1,56 +1,56 @@
-# VibeVoice 1.5 CoreML
-
-VibeVoice 1.5B text-to-speech model converted to Apple CoreML format for on-device inference on macOS and iOS.
-
-## Model Components
-
-| Component | File | Description |
-|-----------|------|-------------|
-| Acoustic Encoder | `vibevoice_acoustic_encoder.mlpackage` | Encodes audio to acoustic latent (fixed input: 24000 samples @ 24kHz) |
-| Acoustic Decoder | `vibevoice_acoustic_decoder.mlpackage` | Decodes acoustic latent to audio |
-| Semantic Encoder | `vibevoice_semantic_encoder.mlpackage` | Encodes audio to semantic latent (fixed input: 24000 samples) |
-| Acoustic Connector | `vibevoice_acoustic_connector.mlpackage` | Projects acoustic latent to LLM hidden space |
-| Semantic Connector | `vibevoice_semantic_connector.mlpackage` | Projects semantic latent to LLM hidden space |
-| LLM | `vibevoice_llm.mlpackage` | Qwen2-1.5B-based language model |
-| Diffusion Head | `vibevoice_diffusion_head.mlpackage` | Single-step diffusion denoising |
-
-## Requirements
-
-- **Platform**: macOS 14.0+ or iOS 17.0+
-- **Framework**: CoreML (Apple Silicon or Intel with Neural Engine recommended)
-
-## Usage
-
-### Swift (iOS/macOS)
-
-Use `VibeVoicePipeline.swift` with a directory containing all `.mlpackage` files:
-
-```swift
-let modelDir = URL(fileURLWithPath: "/path/to/models")
-let pipeline = try VibeVoicePipeline(modelDirectory: modelDir)
-// Encode, run LLM, diffusion, decode...
-```
-
-### Python (macOS only)
-
-CoreML models require macOS to load and run:
-
-```bash
-python inference.py --models-dir ./models --text "Hello world"
-```
-
-## Configuration
-
-- **Audio**: 24 kHz, 1 channel; encoder input fixed at 24000 samples (1 second). Trim or pad input before encoding.
-- **Diffusion**: 20 steps, cosine beta schedule, v_prediction.
-- **LLM**: 1536 hidden size, 28 layers (Qwen2-1.5B-based).
-
-See `vibevoice_config.json` and `vibevoice_pipeline_config.json` for full settings.
-
-## Conversion Notes
-
-Conversion was done with coremltools 9.0. Acoustic and semantic encoders use fixed-length (24000 samples) inputs; the LLM was exported via `torch.export` + custom op registration. See `CONVERSION_RESULTS.md` for details.
-
-## License
-
-Refer to the original VibeVoice model license.
+# VibeVoice 1.5 CoreML
+
+VibeVoice 1.5B text-to-speech model converted to Apple CoreML format for on-device inference on macOS and iOS.
+
+## Model Components
+
+| Component | File | Description |
+|-----------|------|-------------|
+| Acoustic Encoder | `vibevoice_acoustic_encoder.mlpackage` | Encodes audio to acoustic latent (fixed input: 24000 samples @ 24kHz) |
+| Acoustic Decoder | `vibevoice_acoustic_decoder.mlpackage` | Decodes acoustic latent to audio |
+| Semantic Encoder | `vibevoice_semantic_encoder.mlpackage` | Encodes audio to semantic latent (fixed input: 24000 samples) |
+| Acoustic Connector | `vibevoice_acoustic_connector.mlpackage` | Projects acoustic latent to LLM hidden space |
+| Semantic Connector | `vibevoice_semantic_connector.mlpackage` | Projects semantic latent to LLM hidden space |
+| LLM | `vibevoice_llm.mlpackage` | Qwen2-1.5B-based language model |
+| Diffusion Head | `vibevoice_diffusion_head.mlpackage` | Single-step diffusion denoising |
+
+## Requirements
+
+- **Platform**: macOS 14.0+ or iOS 17.0+
+- **Framework**: CoreML (Apple Silicon or Intel with Neural Engine recommended)
+
+## Usage
+
+### Swift (iOS/macOS)
+
+Use `VibeVoicePipeline.swift` with a directory containing all `.mlpackage` files:
+
+```swift
+let modelDir = URL(fileURLWithPath: "/path/to/models")
+let pipeline = try VibeVoicePipeline(modelDirectory: modelDir)
+// Encode, run LLM, diffusion, decode...
+```
+
+### Python (macOS only)
+
+CoreML models require macOS to load and run:
+
+```bash
+python inference.py --models-dir ./models --text "Hello world"
+```
+
+## Configuration
+
+- **Audio**: 24 kHz, 1 channel; encoder input fixed at 24000 samples (1 second). Trim or pad input before encoding.
+- **Diffusion**: 20 steps, cosine beta schedule, v_prediction.
+- **LLM**: 1536 hidden size, 28 layers (Qwen2-1.5B-based).
+
+See `vibevoice_config.json` and `vibevoice_pipeline_config.json` for full settings.
+
+## Conversion Notes
+
+Conversion was done with coremltools 9.0. Acoustic and semantic encoders use fixed-length (24000 samples) inputs; the LLM was exported via `torch.export` + custom op registration. See `CONVERSION_RESULTS.md` for details.
+
+## License
+
+Refer to the original VibeVoice model license.