Update README.md

README.md

@@ -67,18 +67,7 @@ for turn, _, speaker in diarization.itertracks(yield_label=True):
 
 Out of the box, <img src="https://avatars.githubusercontent.com/u/7559051" width="20" style="vertical-align:text-bottom;" /> `pyannote.audio` speaker diarization pipeline v4.0 is expected to be much better than v3.1.
 
-We report [diarization error rates](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %) on large collection of academic benchmarks.
-
-Processing is fully automated:
-
-- no manual voice activity detection (as is sometimes the case in the literature)
-- no manual number of speakers (though it is possible to provide it to the pipeline)
-- no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset
-
-... with the least forgiving diarization error rate (DER) setup (named _"Full"_ in [this paper](https://doi.org/10.1016/j.csl.2021.101254)):
-
-- no forgiveness collar
-- evaluation of overlapped speech
+We report [diarization error rates](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %) on large collection of academic benchmarks (fully automatic processing, no forgiveness collar, nor skipping overlapping speech).
 
 | Benchmark (last updated in 2025-08) | <a href="https://hf.co/pyannote/speaker-diarization-3.1"><img src="https://avatars.githubusercontent.com/u/7559051" width="32" /><br/>v3.1</a> | <a href="https://hf.co/pyannote/speaker-diarization-4.0"><img src="https://avatars.githubusercontent.com/u/7559051" width="32" /><br/> v4.0</a> | <a href="https://docs.pyannote.ai"><img src="https://avatars.githubusercontent.com/u/162698670" width="32" /><br/>API</a> | <a href="https://docs.pyannote.ai"><img src="https://avatars.githubusercontent.com/u/162698670" width="32" /><br/>labs</a> | 
 | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | -------------------------------------------------| ------------------------------------------------ | --- |
@@ -95,16 +84,13 @@ Processing is fully automated:
 | [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2)                                                       | 7.9  |  8.9 |  7.3 |  7.4 |
 | [VoxConverse](https://github.com/joonson/voxconverse) (v0.3)                                                                | 11.2 | 11.2 |  9.0 |  8.5 |
 
-__[Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %, the lower, the better)__
+We also report processing speed on a NVIDIA H100 80GB HBM3:
 
 | Benchmark (last updated in 2025-08) | <img src="https://avatars.githubusercontent.com/u/7559051" width="32" /> | <a href="https://docs.pyannote.ai"><img src="https://avatars.githubusercontent.com/u/162698670" width="32" /></a>  | Speed up
 | -------------- | ----------- | ----------- | ------ |
 | [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM), ~1h files                                                     | 31s per hour of audio | 14s per hour of audio | 2.2x faster
 | [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477)), ~5min files        | 37s per hour of audio | 14s per hour of audio | 2.6x faster
 
-__Processing speed on a NVIDIA H100 80GB HBM3__
-
-
 <img src="https://avatars.githubusercontent.com/u/162698670" width="20" style="vertical-align:text-bottom;" /> `pyannoteAI` premium models are even better (and also 2x faster). <img src="https://avatars.githubusercontent.com/u/162698670" width="20" style="vertical-align:text-bottom;" /> `labs` model is currently in private beta.
 
 1. Create pyannoteAI API key at [`dashboard.pyannote.ai`](https://dashboard.pyannote.ai) 
@@ -226,3 +212,7 @@ diarization = pipeline("audio.wav")
 }
 ```
 
+## Acknowledgment
+
+Training and tuning made possible thanks to [GENCI](https://www.genci.fr/) on the [**Jean Zay**](http://www.idris.fr/eng/jean-zay/) supercomputer.
+