File size: 2,210 Bytes
9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 9f61a1d e11e426 |
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 |
---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-Odia-large
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. -->
# wav2vec2-base-Odia-large
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co./facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3174
- Wer: 0.2448
- Cer: 0.0666
## 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: 0.0003
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 6.2874 | 2.3622 | 300 | 3.3817 | 1.0 | 1.0 |
| 2.5772 | 4.7244 | 600 | 1.1573 | 0.7875 | 0.2934 |
| 0.8599 | 7.0866 | 900 | 0.6319 | 0.5302 | 0.1579 |
| 0.532 | 9.4488 | 1200 | 0.5208 | 0.4332 | 0.1278 |
| 0.374 | 11.8110 | 1500 | 0.4485 | 0.3917 | 0.1110 |
| 0.272 | 14.1732 | 1800 | 0.3939 | 0.3383 | 0.0928 |
| 0.2015 | 16.5354 | 2100 | 0.3646 | 0.3040 | 0.0824 |
| 0.152 | 18.8976 | 2400 | 0.3415 | 0.2700 | 0.0741 |
| 0.1146 | 21.2598 | 2700 | 0.3278 | 0.2584 | 0.0691 |
| 0.0939 | 23.6220 | 3000 | 0.3174 | 0.2448 | 0.0666 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 1.18.3
- Tokenizers 0.19.1
|