---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- accuracy
model-index:
- name: xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup
  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. -->

# xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9765
- Accuracy: 0.6199

## 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: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.6644        | 0.26  | 1000  | 0.3071   | 3.2482          |
| 0.394         | 0.52  | 2000  | 0.5948   | 1.8833          |
| 0.1034        | 0.78  | 3000  | 0.6297   | 1.5852          |
| 0.1088        | 1.04  | 4000  | 0.5992   | 1.7903          |
| 0.0032        | 1.3   | 5000  | 0.6356   | 1.6219          |
| 0.1813        | 1.56  | 6000  | 0.5788   | 1.8168          |
| 0.0654        | 1.82  | 7000  | 0.6234   | 1.6089          |
| 0.0144        | 2.08  | 8000  | 0.6424   | 1.6071          |
| 0.0019        | 2.34  | 9000  | 0.5822   | 1.7820          |
| 0.0159        | 2.6   | 10000 | 0.6043   | 1.8407          |
| 0.0029        | 2.86  | 11000 | 0.5845   | 1.8600          |
| 0.0458        | 3.12  | 12000 | 0.6299   | 1.6591          |
| 0.013         | 3.38  | 13000 | 0.5903   | 2.0788          |
| 0.003         | 3.64  | 14000 | 0.6188   | 1.7645          |
| 0.0015        | 3.9   | 15000 | 0.6328   | 1.7739          |
| 0.0003        | 4.16  | 16000 | 0.6072   | 1.8742          |
| 0.0005        | 4.42  | 17000 | 0.6231   | 1.7102          |
| 0.006         | 4.68  | 18000 | 0.6122   | 1.6909          |
| 0.2367        | 4.93  | 19000 | 0.6029   | 1.9891          |
| 0.005         | 5.19  | 20000 | 0.6220   | 1.7245          |
| 0.0813        | 5.45  | 21000 | 0.5739   | 2.0495          |
| 0.1233        | 5.71  | 22000 | 0.6104   | 1.9601          |
| 0.0003        | 5.97  | 23000 | 0.5924   | 1.8881          |
| 0.0003        | 6.23  | 24000 | 0.6055   | 1.9568          |
| 0.0001        | 6.49  | 25000 | 0.6086   | 1.8489          |
| 0.2198        | 6.75  | 26000 | 0.6292   | 1.8048          |
| 0.0261        | 7.01  | 27000 | 2.0284   | 0.5989          |
| 0.0001        | 7.27  | 28000 | 1.7323   | 0.6431          |
| 0.0001        | 7.53  | 29000 | 1.9329   | 0.6310          |
| 0.0011        | 7.79  | 30000 | 1.9256   | 0.6107          |
| 0.0933        | 8.05  | 31000 | 2.3915   | 0.5896          |
| 0.0001        | 8.31  | 32000 | 1.9948   | 0.6021          |
| 0.0003        | 8.57  | 33000 | 1.9518   | 0.6126          |
| 0.0005        | 8.83  | 34000 | 1.8935   | 0.6243          |
| 0.0           | 9.09  | 35000 | 2.0177   | 0.6144          |
| 0.0002        | 9.35  | 36000 | 2.0234   | 0.6174          |
| 0.0           | 9.61  | 37000 | 1.9568   | 0.6216          |
| 0.0           | 9.87  | 38000 | 1.9765   | 0.6199          |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6