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metadata
license: mit
base_model: pyannote/segmentation-3.0
tags:
  - speaker-diarization
  - speaker-segmentation
  - generated_from_trainer
datasets:
  - diarizers-community/ami
model-index:
  - name: speaker-segmentation-fine-tuned-ami
    results: []

speaker-segmentation-fine-tuned-ami

This model is a fine-tuned version of pyannote/segmentation-3.0 on the diarizers-community/ami ihm dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3660
  • Der: 0.1396
  • False Alarm: 0.0503
  • Missed Detection: 0.0578
  • Confusion: 0.0314

Model description

More information needed

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
0.4133 1.0 1427 0.3629 0.1388 0.0424 0.0646 0.0318
0.3907 2.0 2854 0.3638 0.1400 0.0492 0.0583 0.0324
0.3651 3.0 4281 0.3631 0.1403 0.0506 0.0581 0.0316
0.3692 4.0 5708 0.3643 0.1394 0.0489 0.0591 0.0314
0.3484 5.0 7135 0.3660 0.1396 0.0503 0.0578 0.0314

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.19.1