Audio Classification
speechbrain
PyTorch
English
Emotion
Diarization
wavlm
speechbrainteam commited on
Commit
2e73ddf
1 Parent(s): 3e60708

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +16 -4
README.md CHANGED
@@ -50,15 +50,16 @@ The code will automatically normalize your audio (i.e., resampling + mono channe
50
  First of all, please install the **development** version of SpeechBrain with the following command:
51
 
52
  ```
53
- pip install speechbrain
 
 
 
54
  ```
55
 
56
  Please notice that we encourage you to read our tutorials and learn more about
57
  [SpeechBrain](https://speechbrain.github.io).
58
 
59
- ### Perform Speech Emotion Diarization
60
-
61
- An external `py_module_file=custom.py` is used as an external Predictor class into this HF repos. We use `foreign_class` function from `speechbrain.pretrained.interfaces` that allows you to load your custom model.
62
 
63
  ```python
64
  from speechbrain.pretrained.interfaces import Speech_Emotion_Diarization
@@ -75,6 +76,17 @@ print(diary)
75
  # {'start': 1.94, 'end': 4.48, 'emotion': 'h'} # h -> happy
76
  # ]
77
  # }
 
 
 
 
 
 
 
 
 
 
 
78
  ```
79
  The output will contain a dictionary of emotion components and their boundaries.
80
 
 
50
  First of all, please install the **development** version of SpeechBrain with the following command:
51
 
52
  ```
53
+ git clone https://github.com/speechbrain/speechbrain.git
54
+ cd speechbrain
55
+ pip install -r requirements.txt
56
+ pip install --editable .
57
  ```
58
 
59
  Please notice that we encourage you to read our tutorials and learn more about
60
  [SpeechBrain](https://speechbrain.github.io).
61
 
62
+ ### Perform Speech Emotion Diarization
 
 
63
 
64
  ```python
65
  from speechbrain.pretrained.interfaces import Speech_Emotion_Diarization
 
76
  # {'start': 1.94, 'end': 4.48, 'emotion': 'h'} # h -> happy
77
  # ]
78
  # }
79
+
80
+ diary = classifier.diarize_file("speechbrain/emotion-diarization-wavlm-large/example_sad.wav")
81
+ print(diary)
82
+
83
+ # {
84
+ # 'speechbrain/emotion-diarization-wavlm-large/example_sad.wav':
85
+ # [
86
+ # {'start': 0.0, 'end': 3.54, 'emotion': 's'}, # s -> sad
87
+ # {'start': 3.54, 'end': 5.26, 'emotion': 'n'} # n -> neutral
88
+ # ]
89
+ # }
90
  ```
91
  The output will contain a dictionary of emotion components and their boundaries.
92