Update README.md

README.md

@@ -27,7 +27,53 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+
+This segmentation model has been trained on Japanese data (Callhome) using [diarizers](https://github.com/huggingface/diarizers/tree/main). 
+It can be loaded with two lines of code:  
+
+```python
+from diarizers import SegmentationModel
+
+segmentation_model = SegmentationModel().from_pretrained('diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn')
+```
+
+To use it within a pyannote speaker diarization pipeline, load the [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) pipeline, and convert the model to a pyannote compatible format: 
+
+```python
+
+from pyannote.audio import Pipeline
+import torch
+
+device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+
+# load the pre-trained pyannote pipeline
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+pipeline.to(device)
+
+# replace the segmentation model with your fine-tuned one
+segmentation_model = segmentation_model.to_pyannote_model()
+pipeline._segmentation.model = model.to(device)
+```
+
+You can now use the pipeline on audio examples: 
+
+```python
+from datasets import load_dataset
+# load dataset example
+dataset = load_dataset("diarizers-community/callhome", "jpn", split="data")
+sample = dataset[0]["audio"]
+
+# pre-process inputs
+sample["waveform"] = torch.from_numpy(sample.pop("array")[None, :]).to(device, dtype=model.dtype)
+sample["sample_rate"] = sample.pop("sampling_rate")
+
+# perform inference
+diarization = pipeline(sample)
+
+# dump the diarization output to disk using RTTM format
+with open("audio.rttm", "w") as rttm:
+    diarization.write_rttm(rttm)
+```
 
 ## Intended uses & limitations