feat: update with latest premium pipeline (WIP)

README.md

@@ -13,22 +13,22 @@ Pretrained pipelines reach state-of-the-art performance on most academic benchma
 
 | Benchmark              | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) | [Premium](https://forms.gle/eKhn7H2zTa68sMMx8) |
 | ---------------------- | ------ | ------ | --------- |
-| [AISHELL-4](https://arxiv.org/abs/2104.03603)              |  14.1  |  12.2  | 11.9      |
-| [AliMeeting](https://www.openslr.org/119/) (channel 1) |  27.4  |  24.4  | 22.5      |
-| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM)              |  18.9  |  18.8  | 16.6      |
-| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (SDM)              |  27.1  |  22.4  | 20.9      |
-| [AVA-AVD](https://arxiv.org/abs/2111.14448)                |  66.3  |  50.0  | 39.8      |
-| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) ([part 2](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1))      |  31.6  |  28.4  | 22.2      |
-| [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477))        |  26.9  |  21.7  | 17.2      |
-| [Earnings21](https://github.com/revdotcom/speech-datasets)   | 17.0 | 9.4 | 9.0 |
-| [Ego4D](https://arxiv.org/abs/2110.07058) (dev.)           |  61.5  |  51.2  | 43.8      |
-| [MSDWild](https://github.com/X-LANCE/MSDWILD)                |  32.8  |  25.3  | 19.8      |
+| [AISHELL-4](https://arxiv.org/abs/2104.03603)              |  14.1  |  12.2  | 11.1      |
+| [AliMeeting](https://www.openslr.org/119/) (channel 1) |  27.4  |  24.4  | 20.3      |
+| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM)              |  18.9  |  18.8  | 15.0      |
+| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (SDM)              |  27.1  |  22.4  | 18.9      |
+| [AVA-AVD](https://arxiv.org/abs/2111.14448)                |  66.3  |  50.0  | 42.7      |
+| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) ([part 2](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1))      |  31.6  |  28.4  | 21.7      |
+| [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477))        |  26.9  |  21.7  | 17.1      |
+| [Earnings21](https://github.com/revdotcom/speech-datasets)   | 17.0 | 9.4 | 9.1 |
+| [Ego4D](https://arxiv.org/abs/2110.07058) (dev.)           |  61.5  |  51.2  | 46.0      |
+| [MSDWild](https://github.com/X-LANCE/MSDWILD)                |  32.8  |  25.3  | 19.3      |
 | [RAMC](https://www.openslr.org/123/)                   |  22.5  |  22.2  | 18.4      |
-| [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2)        |   8.2  |   7.8  |  7.6      |
-| [VoxConverse](https://github.com/joonson/voxconverse) (v0.3)     |  11.2  |  11.3  |  9.4      |
+| [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2)        |   8.2  |   7.8  |  8.3      |
+| [VoxConverse](https://github.com/joonson/voxconverse) (v0.3)     |  11.2  |  11.3  |  9.5      |
 [Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %)
 
 Using one Nvidia Tesla V100 SXM2 GPU and one Intel Cascade Lake 6248 CPU,
 * [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) takes around 1m30s to process 1h of audio
 * [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) takes around 1m20s to process 1h of audio
-* [Premium](https://forms.gle/eKhn7H2zTa68sMMx8) takes less than 45s to process 1h of audio 
+* [Premium](https://forms.gle/eKhn7H2zTa68sMMx8) takes less than 35s to process 1h of audio