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---
library_name: transformers
language:
- ne
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- kiranpantha/OpenSLR54-Balanced-Nepali
metrics:
- wer
model-index:
- name: Wave2Vec2-Bert2.0 - Kiran Pantha
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR54
type: kiranpantha/OpenSLR54-Balanced-Nepali
config: default
split: test
args: 'config: ne, split: train,test'
metrics:
- name: Wer
type: wer
value: 0.25254629629629627
---
<!-- 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. -->
# Wave2Vec2-Bert2.0 - Kiran Pantha
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the OpenSLR54 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2212
- Wer: 0.2525
- Cer: 0.0565
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 0.4436 | 0.0900 | 300 | 0.5638 | 0.5560 | 0.1447 |
| 0.5495 | 0.1800 | 600 | 0.6876 | 0.6171 | 0.1641 |
| 0.6148 | 0.2699 | 900 | 0.6872 | 0.6211 | 0.1724 |
| 0.564 | 0.3599 | 1200 | 0.5503 | 0.5162 | 0.1326 |
| 0.4964 | 0.4499 | 1500 | 0.5831 | 0.5319 | 0.1318 |
| 0.4437 | 0.5399 | 1800 | 0.4913 | 0.4935 | 0.1202 |
| 0.4441 | 0.6299 | 2100 | 0.4754 | 0.4764 | 0.1193 |
| 0.3861 | 0.7199 | 2400 | 0.4357 | 0.4361 | 0.1055 |
| 0.3811 | 0.8098 | 2700 | 0.4282 | 0.4137 | 0.0976 |
| 0.3754 | 0.8998 | 3000 | 0.3905 | 0.4069 | 0.0975 |
| 0.3511 | 0.9898 | 3300 | 0.3547 | 0.3692 | 0.0863 |
| 0.2496 | 1.0798 | 3600 | 0.3297 | 0.3433 | 0.0796 |
| 0.242 | 1.1698 | 3900 | 0.3125 | 0.3315 | 0.0770 |
| 0.2378 | 1.2597 | 4200 | 0.3158 | 0.3336 | 0.0757 |
| 0.2274 | 1.3497 | 4500 | 0.2871 | 0.3097 | 0.0722 |
| 0.2142 | 1.4397 | 4800 | 0.3010 | 0.3058 | 0.0712 |
| 0.1949 | 1.5297 | 5100 | 0.2767 | 0.2944 | 0.0678 |
| 0.198 | 1.6197 | 5400 | 0.2487 | 0.2824 | 0.0639 |
| 0.1806 | 1.7097 | 5700 | 0.2376 | 0.2674 | 0.0612 |
| 0.1675 | 1.7996 | 6000 | 0.2293 | 0.2630 | 0.0595 |
| 0.1671 | 1.8896 | 6300 | 0.2248 | 0.2581 | 0.0576 |
| 0.1526 | 1.9796 | 6600 | 0.2212 | 0.2525 | 0.0565 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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