|
--- |
|
license: mit |
|
base_model: pyannote/segmentation-3.0 |
|
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 [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5146 |
|
- Der: 0.1869 |
|
- False Alarm: 0.0933 |
|
- Missed Detection: 0.0709 |
|
- Confusion: 0.0227 |
|
|
|
## 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: 5.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:| |
|
| 0.6043 | 1.0 | 340 | 0.5075 | 0.1789 | 0.0682 | 0.0789 | 0.0318 | |
|
| 0.5766 | 2.0 | 680 | 0.5207 | 0.1951 | 0.1012 | 0.0708 | 0.0230 | |
|
| 0.5345 | 3.0 | 1020 | 0.5011 | 0.1798 | 0.0852 | 0.0716 | 0.0231 | |
|
| 0.518 | 4.0 | 1360 | 0.5344 | 0.1934 | 0.1009 | 0.0700 | 0.0225 | |
|
| 0.5147 | 5.0 | 1700 | 0.5146 | 0.1869 | 0.0933 | 0.0709 | 0.0227 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.0 |
|
- Pytorch 2.2.2+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.19.1 |
|
|
