File size: 3,132 Bytes
8195a99 c6c444e 8195a99 c6c444e 8195a99 c6c444e 8195a99 |
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 79 |
---
library_name: transformers
language:
- id
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- speaker-segmentation
model-index:
- name: speaker-segmentation-fine-tuned-datasetID-hugging_2_4
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-datasetID-hugging_2_4
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: 0.3735
- Model Preparation Time: 0.0099
- Der: 0.1228
- False Alarm: 0.0205
- Missed Detection: 0.0096
- Confusion: 0.0927
## 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: 43
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4492 | 1.0 | 570 | 0.4525 | 0.0099 | 0.1515 | 0.0219 | 0.0094 | 0.1202 |
| 0.3875 | 2.0 | 1140 | 0.4229 | 0.0099 | 0.1403 | 0.0212 | 0.0098 | 0.1093 |
| 0.4014 | 3.0 | 1710 | 0.3889 | 0.0099 | 0.1324 | 0.0207 | 0.0102 | 0.1016 |
| 0.3656 | 4.0 | 2280 | 0.3899 | 0.0099 | 0.1305 | 0.0212 | 0.0094 | 0.1000 |
| 0.3232 | 5.0 | 2850 | 0.3804 | 0.0099 | 0.1283 | 0.0207 | 0.0096 | 0.0981 |
| 0.319 | 6.0 | 3420 | 0.3796 | 0.0099 | 0.1256 | 0.0204 | 0.0097 | 0.0956 |
| 0.3018 | 7.0 | 3990 | 0.3750 | 0.0099 | 0.1247 | 0.0202 | 0.0101 | 0.0945 |
| 0.2928 | 8.0 | 4560 | 0.3700 | 0.0099 | 0.1220 | 0.0203 | 0.0097 | 0.0920 |
| 0.3321 | 9.0 | 5130 | 0.3753 | 0.0099 | 0.1238 | 0.0204 | 0.0098 | 0.0936 |
| 0.2738 | 10.0 | 5700 | 0.3735 | 0.0099 | 0.1228 | 0.0205 | 0.0096 | 0.0927 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|