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speaker-segmentation-fine-tuned-nl

speaker-diarization

speaker-segmentation

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speaker-segmentation-fine-tuned-nl

This model is a fine-tuned version of pyannote/segmentation-3.0 on the Roy2358/synthetic-speaker-diarization-dataset-nl dataset.

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: 0.002
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 10.0

Training results

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

1.47M params

Tensor type

F32

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Base model

pyannote/segmentation-3.0

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Dataset used to train dembrane/speaker-segmentation-fine-tuned-nl