|
|
--- |
|
|
library_name: transformers |
|
|
language: |
|
|
- id |
|
|
license: mit |
|
|
base_model: pyannote/speaker-diarization-3.1 |
|
|
tags: |
|
|
- speaker-diarization |
|
|
- speaker-segmentation |
|
|
- modality:audio |
|
|
- modality:text |
|
|
- format:parquet |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- speaker-segmentation |
|
|
model-index: |
|
|
- name: speaker-segmentation-fine-tuned-id-2603 |
|
|
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-id-2603 |
|
|
|
|
|
This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the speaker-segmentation dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.7019 |
|
|
- Model Preparation Time: 0.0038 |
|
|
- Der: 0.2255 |
|
|
- False Alarm: 0.0699 |
|
|
- Missed Detection: 0.0388 |
|
|
- Confusion: 0.1168 |
|
|
|
|
|
## 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 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
|
|
| 0.7173 | 1.0 | 99 | 0.7460 | 0.0038 | 0.2482 | 0.0758 | 0.0436 | 0.1288 | |
|
|
| 0.6364 | 2.0 | 198 | 0.6885 | 0.0038 | 0.2301 | 0.0710 | 0.0429 | 0.1162 | |
|
|
| 0.5832 | 3.0 | 297 | 0.6896 | 0.0038 | 0.2238 | 0.0717 | 0.0372 | 0.1149 | |
|
|
| 0.5552 | 4.0 | 396 | 0.6964 | 0.0038 | 0.2249 | 0.0700 | 0.0387 | 0.1162 | |
|
|
| 0.5303 | 5.0 | 495 | 0.7019 | 0.0038 | 0.2255 | 0.0699 | 0.0388 | 0.1168 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.50.0 |
|
|
- Pytorch 2.6.0+cu124 |
|
|
- Datasets 3.4.1 |
|
|
- Tokenizers 0.21.1 |
|
|
|
