Update README.md

README.md

@@ -7,6 +7,112 @@ tags:
 - speaker-diarization
 - speaker-change-detection
 - coreml
+base_model:
+- pyannote/speaker-diarization-3.1
+- pyannote/wespeaker-voxceleb-resnet34-LM
 ---
 
-This is still in development.
+# Speaker Diarization CoreML Models
+
+State-of-the-art speaker diarization models optimized for Apple Neural Engine, powering real-time on-device speaker separation with research-competitive performance.
+
+## Model Description
+
+This repository contains CoreML-optimized speaker diarization models specifically converted and optimized for Apple devices (macOS 13.0+, iOS 16.0+). These models enable efficient on-device speaker diarization with minimal power consumption while maintaining state-of-the-art accuracy.
+
+### Key Features
+- **Apple Neural Engine Optimized**: Zero performance trade-offs with maximum efficiency
+- **Real-time Processing**: RTF of 0.02x (50x faster than real-time)
+- **Research-Competitive**: DER of 17.7% on AMI benchmark
+- **Power Efficient**: Designed for maximum performance per watt
+- **Privacy-First**: All processing happens on-device
+
+
+## Intended Uses & Limitations
+
+### Intended Uses
+- **Meeting Transcription**: Real-time speaker identification in meetings
+- **Voice Assistants**: Multi-speaker conversation understanding
+- **Media Production**: Automated speaker labeling for podcasts/interviews
+- **Research**: Academic research in speaker diarization
+- **Privacy-Focused Applications**: On-device processing without cloud dependencies
+
+### Limitations
+- Optimized for 16kHz audio input
+- Best performance with clear audio (no heavy background noise)
+- May struggle with heavily overlapping speech
+- Requires Apple devices with CoreML support
+
+### Technical Specifications
+- **Input**: 16kHz mono audio
+- **Output**: Speaker segments with timestamps and IDs
+- **Framework**: CoreML (converted from PyTorch)
+- **Optimization**: Apple Neural Engine (ANE) optimized operations
+- **Precision**: FP32
+
+## Training Data
+
+These models are converted from open-source variants trained on diverse speaker diarization datasets. The original models were trained on:
+- Multi-speaker conversation datasets
+- Various acoustic conditions
+- Multiple languages and accents
+
+*Note: Specific training data details depend on the original open-source model variant.*
+
+## Usage
+
+See the SDK for more details [https://github.com/FluidInference/FluidAudio](https://github.com/FluidInference/FluidAudio)
+
+### With FluidAudio SDK (Recommended)
+
+Installation
+Add FluidAudio to your project using Swift Package Manager:
+
+```
+dependencies: [
+    .package(url: "https://github.com/FluidInference/FluidAudio.git", from: "0.0.2"),
+],
+```
+
+```swift
+import FluidAudio
+
+Task {
+    let diarizer = DiarizerManager()
+    try await diarizer.initialize()
+    
+    let audioSamples: [Float] = // your 16kHz audio
+    let result = try await diarizer.performCompleteDiarization(
+        audioSamples, 
+        sampleRate: 16000
+    )
+    
+    for segment in result.segments {
+        print("Speaker \(segment.speakerId): \(segment.startTimeSeconds)s - \(segment.endTimeSeconds)s")
+    }
+}
+
+
+### Direct CoreML Usage
+``swift
+import CoreML
+
+// Load the model
+let model = try! SpeakerDiarizationModel(configuration: MLModelConfiguration())
+
+// Prepare input (16kHz audio)
+let input = SpeakerDiarizationModelInput(audioSamples: audioArray)
+
+// Run inference
+let output = try! model.prediction(input: input)
+```
+
+
+## Acknowledgments
+These CoreML models are based on excellent work from:
+
+sherpa-onnx - Foundational diarization algorithms
+pyannote-audio - State-of-the-art diarization research
+wespeaker - Speaker embedding techniques
+
+