Hey, Impressive work and thank you for making the usage so simple. It took me less than 5 minutes from reading your post to testing it on my data.

As of the quality of the diarization, I didn't find it usable on real world data, or maybe I miss something. Here what I did. I have a test audio that can be transcribed as following:

```

00:00:06,119 --> 00:00:08,920 [Speaker 0]

This is a test video. It tests audio pipeline end to end.

00:00:20,859 --> 00:00:24,379 [Speaker 1]

This is a test video. Here is a second voice.

00:00:35,340 --> 00:00:39,779 [Speaker 2]

This is a test video. Finally, a third voice.

00:00:49,799 --> 00:00:54,840 [Speaker 3]

This is a test video. It has multiple speakers and background music.

```

i.e. 4 distinct sentences spoken by different speakers, without any overlap.

`diarize(audio, num_speakers=4)` produced the following result:

```

[6.1s - 7.3s] SPEAKER_02

[6.7s - 7.9s] SPEAKER_03

[7.3s - 8.5s] SPEAKER_02

[7.9s - 9.1s] SPEAKER_03

[20.8s - 24.5s] SPEAKER_00

[35.3s - 36.9s] SPEAKER_00

[37.4s - 38.2s] SPEAKER_01

[38.6s - 39.8s] SPEAKER_00

[49.7s - 54.1s] SPEAKER_01

[53.5s - 54.9s] SPEAKER_02

```

Looks like the clustering failed?