---
language:
- jpn
license: apache-2.0
base_model: openai/whisper-small
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-jpn
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# speaker-segmentation-fine-tuned-callhome-jpn

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4792
- Der: 0.1886
- False Alarm: 0.0606
- Missed Detection: 0.0736
- Confusion: 0.0544

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3877        | 1.0   | 362  | 0.4746          | 0.1886 | 0.0603      | 0.0737           | 0.0547    |
| 0.4206        | 2.0   | 724  | 0.4904          | 0.1956 | 0.0656      | 0.0733           | 0.0566    |
| 0.3933        | 3.0   | 1086 | 0.4704          | 0.1855 | 0.0589      | 0.0737           | 0.0529    |
| 0.3708        | 4.0   | 1448 | 0.4759          | 0.1879 | 0.0600      | 0.0739           | 0.0540    |
| 0.3516        | 5.0   | 1810 | 0.4792          | 0.1886 | 0.0606      | 0.0736           | 0.0544    |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1