Update README.md

README.md

@@ -1,4 +1,81 @@
 ---
 datasets:
 - edinburghcstr/ami
----
+base_model:
+- MIT/ast-finetuned-audioset-10-10-0.4593
+---
+# AST-based Speaker Identification on AMI
+
+## Model description
+
+This model is a **fine-tuned** version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)  
+for speaker classification on the AMI Meeting Corpus. It was trained on **50** speakers (adjust `num_labels` if different), using 128-bin mel-spectrograms of 1024 frames.
+
+- **Base architecture**: Audio Spectrogram Transformer (AST)  
+- **Training**: ~10 epochs, batch size=4, learning rate=1e-5, AdamW optimizer, mixed precision  
+- **Data**: Stratified samples from AMI train/validation/test splits  
+- **Performance**: Not good, this was just a small experiment for diarization
+
+
+
+## How to use
+
+```python
+from transformers import AutoProcessor, ASTForAudioClassification
+import torch
+import numpy as np
+
+# 1) Load the model and processor
+MODEL_ID = "agutig/AST_diarizer"
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model     = ASTForAudioClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+# 2) Prepare a 1-second audio sample (or load your own)
+sr = 16000
+audio = np.random.randn(sr).astype(np.float32)
+# Alternatively:
+# import librosa
+# audio, _ = librosa.load("your_audio.wav", sr=sr)
+
+# 3) Preprocess and run inference
+inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits  # shape [1, num_labels]
+    probs  = torch.softmax(logits, dim=-1)[0]
+    pred_i = int(probs.argmax())
+
+print(f"Predicted speaker index: {pred_i}")
+```
+
+## Usage with `pipeline`
+
+```python
+from transformers import pipeline
+
+speaker_id = pipeline(
+    task="audio-classification",
+    model="agutig/AST_diarizer",
+    return_all_scores=True
+)
+
+results = speaker_id("path/to/audio.wav")
+print(results)
+```
+
+## Evaluation & Benchmarks
+
+| Metric                   | Value   |
+|--------------------------|---------|
+| Accuracy (test)          | 0.XX    |
+| Adjusted Rand Index (ARI)| 0.YY    |
+| Normalized Mutual Info   | 0.ZZ    |
+
+_Fill in actual values._
+
+
+
+## License
+
+- **Model**: Apache 2.0  
+- **Base code (AST AudioSet)**: MIT License