transformers safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:facebook/wav2vec2-large-xlsr-53 license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
- accuracy
model-index:
- name: asr-nepali
  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. -->

# asr-nepali

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
- Cer: 0.9965
- Accuracy: 0.0035

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 180
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer | Cer    | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---:|:------:|:--------:|
| 451.545       | 1.46  | 100  | 43.3285         | 1.0 | 0.9684 | 0.0316   |
| 194.4567      | 2.92  | 200  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 4.38  | 300  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 5.84  | 400  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 7.3   | 500  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 8.76  | 600  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 10.22 | 700  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 11.68 | 800  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 13.14 | 900  | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 14.6  | 1000 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 16.06 | 1100 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 17.52 | 1200 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 18.98 | 1300 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 20.44 | 1400 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 21.9  | 1500 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 23.36 | 1600 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 24.82 | 1700 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 26.28 | 1800 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 27.74 | 1900 | nan             | 1.0 | 0.9965 | 0.0035   |
| 0.0           | 29.2  | 2000 | nan             | 1.0 | 0.9965 | 0.0035   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
