feat: update README

README.md

@@ -15,7 +15,7 @@ tags:
 - resegmentation
 license: mit
 inference: false
-extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening."
+extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening."
 extra_gated_fields:
   Company/university: text
   Website: text
@@ -24,14 +24,33 @@ extra_gated_fields:
 
 We propose (paid) scientific [consulting services](https://herve.niderb.fr/consulting.html) to companies willing to make the most of their data and open-source speech processing toolkits (and `pyannote` in particular). 
 
-# 🎹 Speaker segmentation with powerset encoding
+# 🎹 "Powerset" speaker segmentation
 
 The various concepts behind this model are described in details in this [paper](https://www.isca-speech.org/archive/interspeech_2023/plaquet23_interspeech.html).
 
-It ingests (ideally 10s of) mono audio sampled at 16kHz and outputs speaker diarization as a (num_frames, num_classes) matrix where the 7 classes are _non-speech_, _speaker #1_, _speaker #2_, _speaker #3_, _speakers #1 and #2_, _speakers #1 and #3_, and s_peakers #2 and #3_
-
 It has been trained by Séverin Baroudi with [pyannote.audio](https://github.com/pyannote/pyannote-audio) `3.0.0` using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse.
 
+It ingests (ideally 10s of) mono audio sampled at 16kHz and outputs speaker diarization as a (num_frames, num_classes) matrix where the 7 classes are _non-speech_, _speaker #1_, _speaker #2_, _speaker #3_, _speakers #1 and #2_, _speakers #1 and #3_, and _speakers #2 and #3_.
+
+![Example output](example.png)
+
+```python
+# waveform (first row)
+duration, sample_rate, num_channels = 10, 16000, 1
+waveform = torch.randn(batch_size, num_channels, duration * sample_rate 
+
+# powerset multi-class encoding (second row)
+powerset_encoding = model(waveform)
+
+# multi-label encoding (third row)
+from pyannote.audio.utils.powerset import Powerset
+max_speakers_per_chunk, max_speakers_per_frame = 3, 2
+to_multilabel = Powerset(
+    max_speakers_per_chunk, 
+    max_speakers_per_frame).to_multilabel
+multilabel_encoding = to_multilabel(powerset_encoding)
+```
+
 ## Usage
 
 ```python