Update README.md

README.md

@@ -33,6 +33,8 @@ Detection (VAD) model, optimized for Apple platforms
 (iOS/macOS). This repository contains pre-converted
 CoreML models ready for use in Swift applications.
 
+See FluidAudio Repo link at the top for more information
+
 ## Model Description
 
 **Developed by:** Silero Team (original), converted by
@@ -52,6 +54,14 @@ FluidAudio
 - **Output:** Voice activity probability (0.0-1.0)
 - **Memory:** ~2MB total model size
 
+```
+  | Metric              | FP16 Baseline | 4-Bit Quantized |
+  |---------------------|---------------|-----------------|
+  | Correlation         | -             | 0.9999          |
+  | Mean Absolute Error | -             | 0.0045          |
+  | Model Size          | 0.90 MB       | 0.21 MB         |
+```
+
 ## Intended Use
 
 ### Primary Use Cases
@@ -62,99 +72,6 @@ applications
 
 ## How to Use
 
-### Swift Integration
-
-```swift
-import FluidAudio
-
-let config = VADConfig(
-    threshold: 0.3,
-    chunkSize: 512, // 512 being the most optimal
-    sampleRate: 16000
-)
-
-let vadManager = VADManager(config: config)
-try await vadManager.initialize()
-
-// Process audio chunk
-let result = try await
-vadManager.processChunk(audioChunk)
-print("Voice probability: \(result.probability)")
-print("Is voice active: \(result.isVoiceActive)")
-```
-
-Installation
-
-Add FluidAudio to your Swift project:
-
-dependencies: [
-    .package(url:
-"https://github.com/FluidAudio/FluidAudioSwift.git",
-from: "1.0.0")
-]
-
-Performance
-
-Benchmarks on Apple Silicon (M1/M2)
-
-| Metric           | Value               |
-|------------------|---------------------|
-| Latency          | <2ms per 32ms chunk |
-| Real-time Factor | 0.02x               |
-| Memory Usage     | ~15MB               |
-| CPU Usage        | <5% (single core)   |
-
-Accuracy Metrics
-
-Evaluated on common speech datasets:
-- Precision: 94.2%
-- Recall: 92.8%
-- F1-Score: 93.5%
-
-Model Files
-
-This repository contains three CoreML models that work
-together:
-
-- silero_stft.mlmodel (650KB) - STFT feature extraction
-- silero_encoder.mlmodel (254KB) - Feature encoding
-- silero_rnn_decoder.mlmodel (527KB) - RNN-based
-classification
-
-Training Data
-
-The original Silero VAD model was trained on a diverse
-dataset including:
-- Clean speech audio
-- Noisy speech with various background conditions
-- Music and non-speech audio for negative samples
-
-Limitations and Bias
-
-Known Limitations
-
-- Optimized for 16kHz sample rate (other rates may reduce
- accuracy)
-- May struggle with very quiet speech (<-30dB SNR)
-- Performance varies with microphone quality and
-recording conditions
-
-
-Technical Details
-
-Model Architecture
-
-Audio Input (512 samples, 16kHz)
-    ↓
-STFT Model (spectral features)
-    ↓
-Encoder Model (feature compression)
-    ↓
-RNN Decoder (temporal modeling)
-    ↓
-Voice Probability Output
-
-
 Citation
 
 @misc{silero-vad-coreml,
@@ -172,32 +89,6 @@ url={https://huggingface.co/alexwengg/coreml-silero-vad}
   url={https://github.com/snakers4/silero-vad}
 }
 
-Related Models
-
-Check out other CoreML audio models in the
-https://huggingface.co/collections/bweng/coreml-685b12fd2
-51f80552c08e2b9:
-
-- https://huggingface.co/alexwengg/coreml_speaker_diariza
-tion - Identify "who spoke when"
-- https://huggingface.co/collections/bweng/coreml-685b12f
-d251f80552c08e2b9 - Speech-to-text for Apple platforms
-
-Repository and Support
 
 - GitHub: https://github.com/FluidAudio/FluidAudioSwift
-- Documentation:
-https://github.com/FluidAudio/FluidAudioSwift/wiki
-- Issues:
-https://github.com/FluidAudio/FluidAudioSwift/issues
-- Community:
-https://github.com/FluidAudio/FluidAudioSwift/discussions
-
-License
-
-This project is licensed under the MIT License - see the
-LICENSE file for details.
 
-The original Silero VAD model is also under MIT license.
-See https://github.com/snakers4/silero-vad/blob/master/LI
-CENSE for details.