File size: 2,916 Bytes
4d4fb28 4c6d014 4d4fb28 4c6d014 |
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 80 81 82 83 84 85 86 87 88 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: HO_ASR-Model_KIIT2025
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. -->
# HO_ASR-Model_KIIT2025
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6844
- Wer: 0.5516
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9609 | 2.0 | 250 | 2.8620 | 0.9958 |
| 2.3792 | 4.0 | 500 | 1.5657 | 0.8976 |
| 0.8142 | 6.0 | 750 | 0.8104 | 0.7383 |
| 0.5211 | 8.0 | 1000 | 0.6461 | 0.6508 |
| 0.4145 | 10.0 | 1250 | 0.5793 | 0.6257 |
| 0.3562 | 12.0 | 1500 | 0.5991 | 0.6315 |
| 0.3135 | 14.0 | 1750 | 0.5680 | 0.6295 |
| 0.2694 | 16.0 | 2000 | 0.5731 | 0.6139 |
| 0.2333 | 18.0 | 2250 | 0.6170 | 0.6482 |
| 0.2061 | 20.0 | 2500 | 0.5771 | 0.5852 |
| 0.1823 | 22.0 | 2750 | 0.5820 | 0.5776 |
| 0.1628 | 24.0 | 3000 | 0.5853 | 0.5793 |
| 0.1434 | 26.0 | 3250 | 0.6188 | 0.5776 |
| 0.129 | 28.0 | 3500 | 0.6095 | 0.5644 |
| 0.1185 | 30.0 | 3750 | 0.6210 | 0.5753 |
| 0.1071 | 32.0 | 4000 | 0.6250 | 0.5680 |
| 0.097 | 34.0 | 4250 | 0.6207 | 0.5636 |
| 0.0901 | 36.0 | 4500 | 0.6477 | 0.5760 |
| 0.0833 | 38.0 | 4750 | 0.6510 | 0.5666 |
| 0.0758 | 40.0 | 5000 | 0.6519 | 0.5553 |
| 0.0693 | 42.0 | 5250 | 0.6641 | 0.5549 |
| 0.0637 | 44.0 | 5500 | 0.6648 | 0.5490 |
| 0.0591 | 46.0 | 5750 | 0.6809 | 0.5535 |
| 0.0585 | 48.0 | 6000 | 0.6786 | 0.5512 |
| 0.0555 | 50.0 | 6250 | 0.6844 | 0.5516 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
|