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BengaliDiarization

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pyannet
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# Gated model: Login with a HF token with gated access permission
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# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gomiiie/BengaliDiarization", device_map="auto")
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BengaliDiarization

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2690
  • Der: 0.0945
  • False Alarm: 0.0140
  • Missed Detection: 0.0450
  • Confusion: 0.0355

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: 8e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Der False Alarm Missed Detection Confusion
0.5565 1.7241 100 0.3220 0.1152 0.0158 0.0628 0.0366
0.498 3.4483 200 0.3009 0.1055 0.0164 0.0474 0.0418
0.4955 5.1724 300 0.2863 0.0979 0.0161 0.0431 0.0387
0.4766 6.8966 400 0.2784 0.0981 0.0162 0.0429 0.0391
0.4726 8.6207 500 0.2755 0.0970 0.0151 0.0435 0.0385
0.4574 10.3448 600 0.2740 0.0966 0.0145 0.0450 0.0371
0.4441 12.0690 700 0.2719 0.0956 0.0143 0.0445 0.0369
0.4157 13.7931 800 0.2736 0.0958 0.0141 0.0449 0.0368
0.4916 15.5172 900 0.2722 0.0954 0.0141 0.0453 0.0361
0.4358 17.2414 1000 0.2682 0.0941 0.0141 0.0448 0.0352
0.4287 18.9655 1100 0.2690 0.0945 0.0140 0.0450 0.0355

Framework versions

  • Transformers 4.57.1
  • Pytorch 2.9.1+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.1
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Model size
1.47M params
Tensor type
F32
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