---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xls-r-300m-hbs-ar-unfrozen-batch16
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: hsb
      split: test
      args: hsb
    metrics:
    - name: Wer
      type: wer
      value: 0.46954076850984067
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning/runs/tkr20gft)
# xls-r-300m-hbs-ar-unfrozen-batch16

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_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7763
- Wer: 0.4695
- Cer: 0.1093

## 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: 500
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 3.3679        | 3.2258  | 100  | 3.2752          | 1.0    | 1.0    |
| 3.0469        | 6.4516  | 200  | 2.9638          | 1.0    | 0.9902 |
| 0.5512        | 9.6774  | 300  | 0.7542          | 0.7664 | 0.1947 |
| 0.3029        | 12.9032 | 400  | 0.6819          | 0.6432 | 0.1584 |
| 0.1903        | 16.1290 | 500  | 0.7312          | 0.6361 | 0.1572 |
| 0.1464        | 19.3548 | 600  | 0.7223          | 0.5916 | 0.1456 |
| 0.1205        | 22.5806 | 700  | 0.7566          | 0.5738 | 0.1416 |
| 0.091         | 25.8065 | 800  | 0.7472          | 0.5527 | 0.1308 |
| 0.0686        | 29.0323 | 900  | 0.7029          | 0.5452 | 0.1337 |
| 0.0598        | 32.2581 | 1000 | 0.7889          | 0.5464 | 0.1309 |
| 0.0607        | 35.4839 | 1100 | 0.8012          | 0.5672 | 0.1412 |
| 0.0557        | 38.7097 | 1200 | 0.7628          | 0.5302 | 0.1333 |
| 0.0421        | 41.9355 | 1300 | 0.7861          | 0.5258 | 0.1265 |
| 0.0532        | 45.1613 | 1400 | 0.7843          | 0.5314 | 0.1272 |
| 0.0298        | 48.3871 | 1500 | 0.7888          | 0.5279 | 0.1253 |
| 0.0543        | 51.6129 | 1600 | 0.7847          | 0.5295 | 0.1290 |
| 0.0404        | 54.8387 | 1700 | 0.7314          | 0.5246 | 0.1249 |
| 0.0522        | 58.0645 | 1800 | 0.7505          | 0.5134 | 0.1222 |
| 0.0275        | 61.2903 | 1900 | 0.7588          | 0.5082 | 0.1202 |
| 0.0786        | 64.5161 | 2000 | 0.7733          | 0.4930 | 0.1171 |
| 0.0439        | 67.7419 | 2100 | 0.7953          | 0.4977 | 0.1133 |
| 0.0418        | 70.9677 | 2200 | 0.7664          | 0.4897 | 0.1126 |
| 0.0399        | 74.1935 | 2300 | 0.7599          | 0.4845 | 0.1100 |
| 0.0211        | 77.4194 | 2400 | 0.7747          | 0.4763 | 0.1115 |
| 0.0225        | 80.6452 | 2500 | 0.7607          | 0.4702 | 0.1094 |
| 0.0446        | 83.8710 | 2600 | 0.7583          | 0.4768 | 0.1103 |
| 0.0236        | 87.0968 | 2700 | 0.7824          | 0.4754 | 0.1102 |
| 0.0267        | 90.3226 | 2800 | 0.7861          | 0.4726 | 0.1110 |
| 0.0255        | 93.5484 | 2900 | 0.7928          | 0.4712 | 0.1106 |
| 0.0254        | 96.7742 | 3000 | 0.7834          | 0.4684 | 0.1102 |
| 0.0137        | 100.0   | 3100 | 0.7763          | 0.4695 | 0.1093 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1