pytorch tensorboard wav2vec2 audio-classification transformers automatic-speech-recognition google/xtreme_s generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- automatic-speech-recognition
- google/xtreme_s
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_xlsr_minds14
  results: []
---

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# xtreme_s_xlsr_minds14

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2890
- F1: 0.9474
- Accuracy: 0.9470

## 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- 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: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 2.551         | 2.7   | 200  | 2.5855          | 0.0407 | 0.1201   |
| 1.6934        | 5.41  | 400  | 1.5072          | 0.5862 | 0.6085   |
| 0.5914        | 8.11  | 600  | 0.7274          | 0.8270 | 0.8232   |
| 0.3896        | 10.81 | 800  | 0.4402          | 0.8905 | 0.8890   |
| 0.5052        | 13.51 | 1000 | 0.4483          | 0.8837 | 0.8829   |
| 0.4806        | 16.22 | 1200 | 0.4981          | 0.8784 | 0.8787   |
| 0.2103        | 18.92 | 1400 | 0.4957          | 0.8810 | 0.8817   |
| 0.4198        | 21.62 | 1600 | 0.5161          | 0.8927 | 0.8921   |
| 0.11          | 24.32 | 1800 | 0.4456          | 0.8923 | 0.8902   |
| 0.1233        | 27.03 | 2000 | 0.3858          | 0.9016 | 0.9012   |
| 0.1827        | 29.73 | 2200 | 0.3765          | 0.9162 | 0.9159   |
| 0.1235        | 32.43 | 2400 | 0.3716          | 0.9134 | 0.9128   |
| 0.1873        | 35.14 | 2600 | 0.3080          | 0.9314 | 0.9311   |
| 0.017         | 37.84 | 2800 | 0.2629          | 0.9415 | 0.9409   |
| 0.0436        | 40.54 | 3000 | 0.3159          | 0.9397 | 0.9390   |
| 0.0455        | 43.24 | 3200 | 0.2963          | 0.9393 | 0.9390   |
| 0.046         | 45.95 | 3400 | 0.2914          | 0.9457 | 0.9451   |
| 0.0042        | 48.65 | 3600 | 0.2890          | 0.9474 | 0.9470   |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
