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--- |
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library_name: transformers |
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language: |
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- hi |
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license: mit |
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base_model: pyannote/speaker-diarization-3.1 |
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tags: |
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- speaker-diarization |
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- speaker-segmentation |
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- generated_from_trainer |
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datasets: |
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- Samyak29/synthetic-speaker-diarization-dataset-hindi-large |
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model-index: |
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- name: speaker-segmentation-fine-tuned-hindi |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# speaker-segmentation-fine-tuned-hindi |
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This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the Samyak29/synthetic-speaker-diarization-dataset-hindi-large dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4367 |
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- Model Preparation Time: 0.0045 |
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- Der: 0.1440 |
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- False Alarm: 0.0230 |
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- Missed Detection: 0.0280 |
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- Confusion: 0.0930 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
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| 0.4598 | 1.0 | 194 | 0.4815 | 0.0045 | 0.1608 | 0.0231 | 0.0340 | 0.1036 | |
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| 0.3926 | 2.0 | 388 | 0.4519 | 0.0045 | 0.1545 | 0.0225 | 0.0312 | 0.1008 | |
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| 0.3602 | 3.0 | 582 | 0.4442 | 0.0045 | 0.1476 | 0.0232 | 0.0288 | 0.0956 | |
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| 0.3611 | 4.0 | 776 | 0.4388 | 0.0045 | 0.1443 | 0.0228 | 0.0281 | 0.0934 | |
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| 0.3399 | 5.0 | 970 | 0.4367 | 0.0045 | 0.1440 | 0.0230 | 0.0280 | 0.0930 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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