Update README.md

README.md

@@ -9,9 +9,9 @@ pinned: false
 
 [**pyannote.audio**](https://github.com/pyannote/pyannote-audio) is an open-source toolkit for speaker diarization.
 
-Pretrained pipelines reach state-of-the-art performance on most academic benchmarks and are used [in production by dozens of companies](https://herve.niderb.fr/consulting.html).
+Pretrained pipelines reach state-of-the-art performance on most academic benchmarks and are used [in production by dozens of companies](https://www.pyannote.ai).
 
-| Benchmark              | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) | [Premium](https://forms.office.com/e/GdqwVgkZ5C) |
+| Benchmark              | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) | [pyannoteAI (03-2024)](https://www.pyannote.ai) |
 | ---------------------- | ------ | ------ | --------- |
 | [AISHELL-4](https://arxiv.org/abs/2104.03603)              |  14.1  |  12.2  | 11.2      |
 | [AliMeeting](https://www.openslr.org/119/) (channel 1) |  27.4  |  24.4  | 19.3      |
@@ -28,7 +28,7 @@ Pretrained pipelines reach state-of-the-art performance on most academic benchma
 | [VoxConverse](https://github.com/joonson/voxconverse) (v0.3)     |  11.2  |  11.3  |  9.8      |
 [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,
+Using high-end NVIDIA hardware,
 * [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 35s to process 1h of audio 
+* On-premise [pyannoteAI](https://www.pyannote.ai) takes less than 30s to process 1h of audio