Update README.md

README.md

@@ -28,7 +28,7 @@ It achieves the following results on the evaluation set:
 ## Model description
 
 This segmentation model has been trained on English data (Callhome) using [diarizers](https://github.com/huggingface/diarizers/tree/main). 
-It can be loaded with two lines of code using the following snipet: 
+It can be loaded with two lines of code: 
 
 ```python
 from diarizers import SegmentationModel
@@ -36,13 +36,7 @@ from diarizers import SegmentationModel
 segmentation_model = SegmentationModel().from_pretrained('diarizers-community/speaker-segmentation-fine-tuned-callhome-eng')
 ```
 
-To use it within a pyannote speaker diarization pipeline, convert the model to a pyannote compatible format: 
-
-```python
-segmentation_model = segmentation_model.to_pyannote_model()
-```
-
-...load it in the [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) pipeline: 
+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
 
@@ -56,11 +50,12 @@ device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("
 pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
 pipeline.to(device)
 
-# Replace the pre-trained segmentation model with the fine-tuned version:
+# replace the segmentation model with your fine-tuned one
+segmentation_model = segmentation_model.to_pyannote_model()
 pipeline._segmentation.model = model.to(device)
 ```
 
-...and use it on a audio example: 
+You can now use the pipeline on audio examples: 
 
 ```python
 # load dataset example