|
|
--- |
|
|
library_name: transformers |
|
|
license: mit |
|
|
base_model: pyannote/segmentation-3.0 |
|
|
tags: |
|
|
- speaker-diarization |
|
|
- speaker-segmentation |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- diarizers-community/simsamu |
|
|
model-index: |
|
|
- name: speaker-segmentation-simsamu-fra |
|
|
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-simsamu-fra |
|
|
|
|
|
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/simsamu dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.2243 |
|
|
- Model Preparation Time: 0.0033 |
|
|
- Der: 0.0906 |
|
|
- False Alarm: 0.0239 |
|
|
- Missed Detection: 0.0427 |
|
|
- Confusion: 0.0240 |
|
|
|
|
|
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: cosine |
|
|
- num_epochs: 5.0 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
|
|
| No log | 1.0 | 56 | 0.2303 | 0.0033 | 0.0985 | 0.0298 | 0.0430 | 0.0257 | |
|
|
| 0.2116 | 2.0 | 112 | 0.2301 | 0.0033 | 0.0968 | 0.0218 | 0.0524 | 0.0227 | |
|
|
| 0.2116 | 3.0 | 168 | 0.2247 | 0.0033 | 0.0923 | 0.0230 | 0.0462 | 0.0231 | |
|
|
| 0.1681 | 4.0 | 224 | 0.2244 | 0.0033 | 0.0909 | 0.0246 | 0.0424 | 0.0240 | |
|
|
| 0.1681 | 5.0 | 280 | 0.2243 | 0.0033 | 0.0906 | 0.0239 | 0.0427 | 0.0240 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.52.3 |
|
|
- Pytorch 2.6.0+cu126 |
|
|
- Datasets 3.6.0 |
|
|
- Tokenizers 0.21.1 |
|
|
|
