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---
library_name: transformers
language:
- ne
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- kiranpantha/OpenSLR54-Balanced-Nepali
metrics:
- wer
model-index:
- name: XLSR-300M-Nepali
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: 0.5244204160175937
---
<!-- 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. -->
# XLSR-300M-Nepali
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OpenSLR54 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2681
- Wer: 0.5244
## 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.2642 | 0.0722 | 300 | 2.9627 | 1.0 |
| 2.1949 | 0.1444 | 600 | 1.5526 | 1.0160 |
| 1.4595 | 0.2166 | 900 | 1.1674 | 0.9810 |
| 1.2128 | 0.2888 | 1200 | 0.9901 | 0.9668 |
| 0.976 | 0.3610 | 1500 | 0.6942 | 0.7696 |
| 0.8267 | 0.4332 | 1800 | 0.6314 | 0.7552 |
| 0.7542 | 0.5054 | 2100 | 0.5522 | 0.7156 |
| 0.7228 | 0.5776 | 2400 | 0.5210 | 0.6960 |
| 0.6707 | 0.6498 | 2700 | 0.4744 | 0.6581 |
| 0.6368 | 0.7220 | 3000 | 0.4529 | 0.6535 |
| 0.5944 | 0.7942 | 3300 | 0.4229 | 0.6264 |
| 0.5651 | 0.8664 | 3600 | 0.4061 | 0.6161 |
| 0.5469 | 0.9386 | 3900 | 0.3788 | 0.6103 |
| 0.5308 | 1.0108 | 4200 | 0.3668 | 0.5957 |
| 0.4684 | 1.0830 | 4500 | 0.3509 | 0.5920 |
| 0.4382 | 1.1552 | 4800 | 0.3398 | 0.5920 |
| 0.4424 | 1.2274 | 5100 | 0.3260 | 0.5767 |
| 0.4159 | 1.2996 | 5400 | 0.3189 | 0.5690 |
| 0.419 | 1.3718 | 5700 | 0.3067 | 0.5581 |
| 0.4114 | 1.4440 | 6000 | 0.3019 | 0.5568 |
| 0.3903 | 1.5162 | 6300 | 0.2982 | 0.5549 |
| 0.3915 | 1.5884 | 6600 | 0.2887 | 0.5493 |
| 0.3789 | 1.6606 | 6900 | 0.2813 | 0.5398 |
| 0.3725 | 1.7329 | 7200 | 0.2763 | 0.5339 |
| 0.3706 | 1.8051 | 7500 | 0.2704 | 0.5285 |
| 0.3624 | 1.8773 | 7800 | 0.2706 | 0.5264 |
| 0.357 | 1.9495 | 8100 | 0.2681 | 0.5244 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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