File size: 2,547 Bytes
7f0b954 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
library_name: transformers
language:
- bn
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- bangla
- bengali
- pyannote
- audio
- generated_from_trainer
datasets:
- Sam3000/speaker-diarization-dataset-bangla
model-index:
- name: bangla-segment
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. -->
# bangla-segment
This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the Sam3000/speaker-diarization-dataset-bangla dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4452
- Model Preparation Time: 0.0056
- Der: 0.1488
- False Alarm: 0.0317
- Missed Detection: 0.0372
- Confusion: 0.0799
## 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4657 | 1.0 | 170 | 0.4409 | 0.0056 | 0.1506 | 0.0392 | 0.0198 | 0.0916 |
| 0.4403 | 2.0 | 340 | 0.4201 | 0.0056 | 0.1507 | 0.0328 | 0.0317 | 0.0861 |
| 0.3691 | 3.0 | 510 | 0.4362 | 0.0056 | 0.1485 | 0.0317 | 0.0350 | 0.0818 |
| 0.3602 | 4.0 | 680 | 0.4437 | 0.0056 | 0.1493 | 0.0319 | 0.0377 | 0.0797 |
| 0.3875 | 5.0 | 850 | 0.4452 | 0.0056 | 0.1488 | 0.0317 | 0.0372 | 0.0799 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
|