transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_13_0 base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hi-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: hi
      split: test
      args: hi
    metrics:
    - name: Wer
      type: wer
      value: 1.0222783552113959
---

<!-- 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-large-xls-r-300m-hi-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7522
- Wer: 1.0223

## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.9636        | 0.95  | 400  | 2.2746          | 1.0338 |
| 0.9533        | 1.89  | 800  | 0.8338          | 1.0699 |
| 0.5378        | 2.84  | 1200 | 0.7781          | 1.0159 |
| 0.4041        | 3.79  | 1600 | 0.7267          | 1.0191 |
| 0.3308        | 4.73  | 2000 | 0.6780          | 1.0237 |
| 0.2728        | 5.68  | 2400 | 0.6862          | 1.0203 |
| 0.2272        | 6.63  | 2800 | 0.6658          | 1.0266 |
| 0.198         | 7.57  | 3200 | 0.6819          | 1.0306 |
| 0.1787        | 8.52  | 3600 | 0.6930          | 1.0257 |
| 0.158         | 9.47  | 4000 | 0.7278          | 1.0318 |
| 0.1391        | 10.41 | 4400 | 0.7102          | 1.0319 |
| 0.1249        | 11.36 | 4800 | 0.7726          | 1.0190 |
| 0.1131        | 12.31 | 5200 | 0.7325          | 1.0253 |
| 0.1049        | 13.25 | 5600 | 0.7512          | 1.0227 |
| 0.095         | 14.2  | 6000 | 0.7580          | 1.0222 |
| 0.0835        | 15.15 | 6400 | 0.7161          | 1.0204 |
| 0.0817        | 16.09 | 6800 | 0.7530          | 1.0239 |
| 0.0707        | 17.04 | 7200 | 0.7613          | 1.0250 |
| 0.0682        | 17.99 | 7600 | 0.7412          | 1.0196 |
| 0.0599        | 18.93 | 8000 | 0.7700          | 1.0214 |
| 0.0593        | 19.88 | 8400 | 0.7522          | 1.0223 |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.2.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
