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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: openai/whisper-small
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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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- diarizers-community/ami_speaker_diarization_dataset
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model-index:
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- name: speaker-segmentation-fine-tuned-ami-speaker-diarization
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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-ami-speaker-diarization
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/ami_speaker_diarization_dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4425
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- Der: 0.1760
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- False Alarm: 0.0627
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- Missed Detection: 0.0634
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- Confusion: 0.0499
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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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 | Der | False Alarm | Missed Detection | Confusion |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
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| 0.3957 | 1.0 | 1809 | 0.4538 | 0.1799 | 0.0605 | 0.0656 | 0.0537 |
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| 0.4027 | 2.0 | 3618 | 0.4446 | 0.1780 | 0.0645 | 0.0627 | 0.0508 |
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| 0.3639 | 3.0 | 5427 | 0.4504 | 0.1798 | 0.0669 | 0.0604 | 0.0524 |
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| 0.3764 | 4.0 | 7236 | 0.4431 | 0.1762 | 0.0632 | 0.0623 | 0.0508 |
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| 0.3916 | 5.0 | 9045 | 0.4425 | 0.1760 | 0.0627 | 0.0634 | 0.0499 |
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.19.1
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