---
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: 0.5893
- Der: 0.1935
- False Alarm: 0.0760
- Missed Detection: 0.0713
- Confusion: 0.0461

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5956        | 1.0   | 336  | 0.6028          | 0.2004 | 0.0637      | 0.0824           | 0.0543    |
| 0.5696        | 2.0   | 672  | 0.6137          | 0.2001 | 0.0760      | 0.0741           | 0.0500    |
| 0.5334        | 3.0   | 1008 | 0.5886          | 0.1941 | 0.0787      | 0.0701           | 0.0453    |
| 0.5168        | 4.0   | 1344 | 0.5914          | 0.1933 | 0.0745      | 0.0723           | 0.0465    |
| 0.5174        | 5.0   | 1680 | 0.5893          | 0.1935 | 0.0760      | 0.0713           | 0.0461    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1