doc: update README

README.md

@@ -31,44 +31,24 @@ Relies on pyannote.audio 2.0 currently in development: see [installation instruc
 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.
 
-## Basic usage
+## Usage
 
-```python
-from pyannote.audio import Inference
-inference = Inference("pyannote/segmentation")
-segmentation = inference("audio.wav")
-# `segmentation` is a pyannote.core.SlidingWindowFeature
-# instance containing raw segmentation scores like the 
-# one pictured above (output)
+### Voice activity detection
 
-from pyannote.audio.pipelines import Segmentation
-pipeline = Segmentation(segmentation="pyannote/segmentation")
+```python
+from pyannote.audio.pipelines import VoiceActivityDetection
+pipeline = VoiceActivityDetection(segmentation="pyannote/segmentation")
 HYPER_PARAMETERS = {
   # onset/offset activation thresholds
   "onset": 0.5, "offset": 0.5,
-  # remove speaker turn shorter than that many seconds.
+  # remove speech regions shorter than that many seconds.
   "min_duration_on": 0.0,
-  # fill within speaker pauses shorter than that many seconds.
+  # fill non-speech regions shorter than that many seconds.
   "min_duration_off": 0.0
 }
-
-pipeline.instantiate(HYPER_PARAMETERS)
-segmentation = pipeline("audio.wav")
-# `segmentation` now is a pyannote.core.Annotation
-# instance containing a hard binary segmentation 
-# like the one picutred above (reference)
-```
-
-
-## Advanced usage
-
-### Voice activity detection
-
-```python
-from pyannote.audio.pipelines import VoiceActivityDetection
-pipeline = VoiceActivityDetection(segmentation="pyannote/segmentation")
 pipeline.instantiate(HYPER_PARAMETERS)
 vad = pipeline("audio.wav")
+# `vad` is a pyannote.core.Annotation instance containing speech regions
 ```
 
 ### Overlapped speech detection
@@ -78,6 +58,7 @@ from pyannote.audio.pipelines import OverlappedSpeechDetection
 pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation")
 pipeline.instantiate(HYPER_PARAMETERS)
 osd = pipeline("audio.wav")
+# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
 ```
 
 ### Resegmentation
@@ -91,6 +72,17 @@ resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
 # where `baseline` should be provided as a pyannote.core.Annotation instance
 ```
 
+### Raw scores
+
+```python
+from pyannote.audio import Inference
+inference = Inference("pyannote/segmentation")
+segmentation = inference("audio.wav")
+# `segmentation` is a pyannote.core.SlidingWindowFeature
+# instance containing raw segmentation scores like the 
+# one pictured above (output)
+```
+
 ## Reproducible research 
 
 In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation
@@ -118,6 +110,16 @@ Expected outputs (and VBx baseline) are also provided in the `/reproducible_rese
 
 ## Citation
 
+```bibtex
+@inproceedings{Bredin2021,
+  Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
+  Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
+  Booktitle = {Proc. Interspeech 2021},
+  Address = {Brno, Czech Republic},
+  Month = {August},
+  Year = {2021},
+```
+
 ```bibtex
 @inproceedings{Bredin2020,
   Title = {{pyannote.audio: neural building blocks for speaker diarization}},