Update README.md

README.md

@@ -23,10 +23,10 @@ cd WhisperSeg & pip install -r requirements.txt
 
 Then in the folder "WhisperSeg", run the following python script:
 ```python
-from model import WhisperSegmenter
+from model import WhisperSegmenterFast
 import librosa
 import json
-segmenter = WhisperSegmenter( "nccratliri/whisperseg-large-ms", device="cuda" )
+segmenter = WhisperSegmenterFast( "nccratliri/whisperseg-large-ms-ct2", device="cuda" )
 
 sr = 32000  
 min_frequency = 0
@@ -40,12 +40,10 @@ audio, _ = librosa.load( "data/example_subset/Zebra_finch/test_adults/zebra_finc
 
 prediction = segmenter.segment(  audio, sr = sr, min_frequency = min_frequency, spec_time_step = spec_time_step,
                        min_segment_length = min_segment_length, eps = eps,num_trials = num_trials )
-
 print(prediction)
 ```
   {'onset': [0.01, 0.38, 0.603, 0.758, 0.912, 1.813, 1.967, 2.073, 2.838, 2.982, 3.112, 3.668, 3.828, 3.953, 5.158, 5.323, 5.467], 'offset': [0.073, 0.447, 0.673, 0.83, 1.483, 1.882, 2.037, 2.643, 2.893, 3.063, 3.283, 3.742, 3.898, 4.523, 5.223, 5.393, 6.043], 'cluster': ['zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0', 'zebra_finch_0']}
 
-
 For more details, please refer to the GitHub repository: https://github.com/nianlonggu/WhisperSeg
 
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