Downloads · 30 days
14
5% of all-time downloads
speaker-segmentation-fine-tuned-callhome-jpn
shreyaskal3/speaker-segmentation-fine-tuned-callhome-jpn
speaker-segmentation-fine-tuned-callhome-jpn is a machine learning model from shreyaskal3. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
275
Public
Parameters
1.5M
118 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors5.9 MB · 99%
At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Nov 7, 2024
- Updated
- Jan 3, 2025
- SHA
- 67a22d39
Base models
Model card
From the Hugging Face model README
<!-- 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-hindi
This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the Samyak29/synthetic-speaker-diarization-dataset-hindi-large dataset. It achieves the following results on the evaluation set:
- Loss: 0.4367
- Model Preparation Time: 0.0045
- Der: 0.1440
- False Alarm: 0.0230
- Missed Detection: 0.0280
- Confusion: 0.0930
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 |
|---|---|---|---|---|---|---|---|---|
| 0.4598 | 1.0 | 194 | 0.4815 | 0.0045 | 0.1608 | 0.0231 | 0.0340 | 0.1036 |
| 0.3926 | 2.0 | 388 | 0.4519 | 0.0045 | 0.1545 | 0.0225 | 0.0312 | 0.1008 |
| 0.3602 | 3.0 | 582 | 0.4442 | 0.0045 | 0.1476 | 0.0232 | 0.0288 | 0.0956 |
| 0.3611 | 4.0 | 776 | 0.4388 | 0.0045 | 0.1443 | 0.0228 | 0.0281 | 0.0934 |
| 0.3399 | 5.0 | 970 | 0.4367 | 0.0045 | 0.1440 | 0.0230 | 0.0280 | 0.0930 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Library
- transformers
- License
- mit
- Languages
- hi
- Created
- Nov 7, 2024
- Updated
- Jan 3, 2025