feat: rename /paper to /reproducible_research

README.md

@@ -19,13 +19,9 @@ inference: false
 
 # pyannote.audio // speaker segmentation
 
-This model is described in the technical report *[End-to-end speaker segmentation for overlap-aware resegmentation](paper/report.pdf)*, by Hervé Bredin and Antoine Laurent.
-
 ![Example](example.png)
 
-## Citation
-
-If you use this model for academic research, please consider citing the `pyannote.audio` library:
+Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](reproducible_research/report.pdf)*, by Hervé Bredin and Antoine Laurent.
 
 ```bibtex
 @inproceedings{Bredin2020,
@@ -40,7 +36,8 @@ If you use this model for academic research, please consider citing the `pyannot
 
 ## Support
 
-If you (would like to) use this model in commercial products and need help to make the most of it, please contact [me](mailto:[email protected]).
+For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).  
+For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
 
 ## Requirements
 
@@ -90,16 +87,6 @@ pipeline.instantiate(HYPER_PARAMETERS)
 vad = pipeline("audio.wav")
 ```
 
-In order to reproduce results of the [technical report](paper/report.pdf), one should use the following hyper-parameter values:
-
-Dataset         | `onset` | `offset` | `min_duration_on` | `min_duration_off`
-----------------|---------|----------|-------------------|-------------------
-AMI Mix-Headset | 0.851   | 0.430    | 0.115             | 0.146
-DIHARD3         | 0.855   | 0.292    | 0.036             | 0.001
-VoxConverse     | 0.883   | 0.688    | 0.106             | 0.526
-
-We also provide the [expected output](tree/main/paper/expected_outputs/vad) on those three datasets in RTTM format.
-
 ### Overlapped speech detection
 
 ```python
@@ -109,16 +96,6 @@ pipeline.instantiate(HYPER_PARAMETERS)
 osd = pipeline("audio.wav")
 ```
 
-In order to reproduce results of the [technical report](paper/report.pdf), one should use the following hyper-parameter values:
-
-Dataset         | `onset` | `offset` | `min_duration_on` | `min_duration_off`
-----------------|---------|----------|-------------------|-------------------
-AMI Mix-Headset | 0.552   | 0.311    | 0.131             | 0.180
-DIHARD3         | 0.564   | 0.264    | 0.158             | 0.080
-VoxConverse     | 0.617   | 0.387    | 0.367             | 0.334
-
-We also provide the [expected output](tree/main/paper/expected_outputs/osd) on those three datasets in RTTM format.
-
 ### Resegmentation
 
 ```python
@@ -126,27 +103,32 @@ from pyannote.audio.pipelines import Resegmentation
 pipeline = Resegmentation(segmentation="pyannote/segmentation", 
                           diarization="baseline")
 pipeline.instantiate(HYPER_PARAMETERS)
+resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
+# where `baseline` should be provided as a pyannote.core.Annotation instance
 ```
 
-In order to reproduce (VBx) results of the technical report, one should use the following hyper-parameter values:
+## Reproducible research 
+
+In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation
+"](reproducible_research/report.pdf), use the following hyper-parameters:
 
-Dataset         | `onset` | `offset` | `min_duration_on` | `min_duration_off`
+Voice activity detection  | `onset` | `offset` | `min_duration_on` | `min_duration_off`
+----------------|---------|----------|-------------------|-------------------
+AMI Mix-Headset | 0.851   | 0.430    | 0.115             | 0.146
+DIHARD3         | 0.855   | 0.292    | 0.036             | 0.001
+VoxConverse     | 0.883   | 0.688    | 0.106             | 0.526
+
+Overlapped speech detection | `onset` | `offset` | `min_duration_on` | `min_duration_off`
+----------------|---------|----------|-------------------|-------------------
+AMI Mix-Headset | 0.552   | 0.311    | 0.131             | 0.180
+DIHARD3         | 0.564   | 0.264    | 0.158             | 0.080
+VoxConverse     | 0.617   | 0.387    | 0.367             | 0.334
+
+VBx resegmentation | `onset` | `offset` | `min_duration_on` | `min_duration_off`
 ----------------|---------|----------|-------------------|-------------------
 AMI Mix-Headset | 0.542   | 0.527    | 0.044             | 0.705
 DIHARD3         | 0.592   | 0.489    | 0.163             | 0.182
 VoxConverse     | 0.537   | 0.724    | 0.410             | 0.563
 
-
-
-[VBx RTTM files](tree/main/paper/expected_outputs/vbx) are also provided in this repository for convenience:
-
-```python
-from pyannote.database.utils import load_rttm
-vbx = load_rttm("paper/expected_outputs/vbx/DIHARD.rttm")
-resegmented_vbx = pipeline({"audio": "DH_EVAL_000.wav", 
-                            "baseline": vbx["DH_EVAL_000"]})
-```
-
-
-We also provide the [expected output](tree/main/paper/expected_outputs/rsg) on those three datasets in RTTM format.
+Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories.