speaker segmentation fine tuned callhome jpn This model is a fine tuned version of pyannote/segmentation 3.0 on the diarizers community/callhome jpn dataset. It achieves the following results on the evaluation set: Loss: 0.7653 Der: 0.2311 False Alarm: 0.0477 Missed Detection: 0.1352 Confusion: 0.0482 Model description This segmentation model has been trained on Japanese data (Callhome) using diarizers. It can be loaded with two lines of code: To use it within a pyannote speaker diarization pipeline, load the pyannote/speaker diarization 3.1 pipeline, and convert the model to a pyannote compatible format: You can now use the pipeline on audio examples: Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning rate: 0.001 train batch size: 32 eval batch size: 32 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e 08 lr scheduler type: cosine num epochs: 5.0 Training results Training Loss Epoch Step Validation Loss Der False Alarm Missed Detection Confusion : : : : : : : : : : : : : : : : 0.5917 1.0 328 0.7859 0.2409 0…
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