---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- automatic-speech-recognition
- gttsehu/basque_parliament_1
- generated_from_trainer
metrics:
- wer
model-index:
- name: facebook/wav2vec2-xls-r-300m
  results: []
datasets:
- gttsehu/basque_parliament_1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# facebook/wav2vec2-xls-r-300m

This work was partially funded by the Spanish Ministry of Science and Innovation (OPENSPEECH
project, PID2019-106424RB-I00).

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the GTTSEHU/BASQUE_PARLIAMENT_1 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0846
- Wer: 0.0367
- Cer: 0.0132

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 0.7054        | 0.19  | 4000   | 0.1011          | 0.0871 | 0.0227 |
| 0.0856        | 0.39  | 8000   | 0.0995          | 0.0747 | 0.0207 |
| 0.075         | 0.58  | 12000  | 0.0868          | 0.0647 | 0.0185 |
| 0.0694        | 0.77  | 16000  | 0.0853          | 0.0619 | 0.0183 |
| 0.0658        | 0.97  | 20000  | 0.0778          | 0.0573 | 0.0171 |
| 0.0589        | 1.16  | 24000  | 0.0821          | 0.0546 | 0.0166 |
| 0.0572        | 1.35  | 28000  | 0.0827          | 0.0558 | 0.0170 |
| 0.0551        | 1.55  | 32000  | 0.0830          | 0.0533 | 0.0169 |
| 0.054         | 1.74  | 36000  | 0.0788          | 0.0512 | 0.0162 |
| 0.0524        | 1.93  | 40000  | 0.0783          | 0.0489 | 0.0156 |
| 0.048         | 2.13  | 44000  | 0.0861          | 0.0492 | 0.0160 |
| 0.046         | 2.32  | 48000  | 0.0763          | 0.0494 | 0.0154 |
| 0.0456        | 2.51  | 52000  | 0.0835          | 0.0471 | 0.0153 |
| 0.0439        | 2.71  | 56000  | 0.0790          | 0.0469 | 0.0152 |
| 0.0436        | 2.9   | 60000  | 0.0832          | 0.0472 | 0.0155 |
| 0.0406        | 3.09  | 64000  | 0.0810          | 0.0442 | 0.0148 |
| 0.0386        | 3.29  | 68000  | 0.0810          | 0.0436 | 0.0146 |
| 0.038         | 3.48  | 72000  | 0.0778          | 0.0430 | 0.0143 |
| 0.0373        | 3.67  | 76000  | 0.0785          | 0.0430 | 0.0144 |
| 0.0363        | 3.87  | 80000  | 0.0788          | 0.0421 | 0.0144 |
| 0.0348        | 4.06  | 84000  | 0.0823          | 0.0423 | 0.0144 |
| 0.0323        | 4.25  | 88000  | 0.0819          | 0.0407 | 0.0143 |
| 0.0319        | 4.45  | 92000  | 0.0809          | 0.0410 | 0.0142 |
| 0.0314        | 4.64  | 96000  | 0.0821          | 0.0400 | 0.0138 |
| 0.0306        | 4.83  | 100000 | 0.0813          | 0.0389 | 0.0137 |
| 0.0295        | 5.03  | 104000 | 0.0820          | 0.0377 | 0.0131 |
| 0.0275        | 5.22  | 108000 | 0.0866          | 0.0378 | 0.0137 |
| 0.0267        | 5.41  | 112000 | 0.0831          | 0.0376 | 0.0134 |
| 0.0264        | 5.61  | 116000 | 0.0845          | 0.0369 | 0.0132 |
| 0.0258        | 5.8   | 120000 | 0.0859          | 0.0370 | 0.0133 |
| 0.0254        | 6.0   | 124000 | 0.0846          | 0.0367 | 0.0132 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.1.dev0
- Tokenizers 0.15.0