---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xls-r-300m-hbs-phoneme-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: 0.5337394564198688
---

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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/7duhfamy)
# xls-r-300m-hbs-phoneme-unfrozen-batch16

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9205
- Wer: 0.5337
- Cer: 0.1244

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 4.0877        | 3.2258  | 100  | 3.7799          | 1.0    | 1.0    |
| 3.2643        | 6.4516  | 200  | 3.2338          | 1.0    | 1.0    |
| 3.2182        | 9.6774  | 300  | 3.1963          | 1.0    | 1.0    |
| 0.8009        | 12.9032 | 400  | 0.9289          | 0.8240 | 0.2193 |
| 0.2664        | 16.1290 | 500  | 0.8523          | 0.7381 | 0.1855 |
| 0.1359        | 19.3548 | 600  | 0.8465          | 0.6757 | 0.1676 |
| 0.1022        | 22.5806 | 700  | 0.8537          | 0.6603 | 0.1656 |
| 0.0641        | 25.8065 | 800  | 0.8821          | 0.6664 | 0.1620 |
| 0.0565        | 29.0323 | 900  | 0.9185          | 0.6610 | 0.1608 |
| 0.068         | 32.2581 | 1000 | 0.8839          | 0.6286 | 0.1513 |
| 0.0556        | 35.4839 | 1100 | 0.8898          | 0.6125 | 0.1479 |
| 0.0457        | 38.7097 | 1200 | 0.8840          | 0.6204 | 0.1448 |
| 0.0439        | 41.9355 | 1300 | 0.9207          | 0.6249 | 0.1490 |
| 0.0296        | 45.1613 | 1400 | 0.9572          | 0.6246 | 0.1510 |
| 0.0461        | 48.3871 | 1500 | 0.8875          | 0.5918 | 0.1395 |
| 0.0419        | 51.6129 | 1600 | 0.8967          | 0.5846 | 0.1384 |
| 0.0333        | 54.8387 | 1700 | 0.9827          | 0.5951 | 0.1420 |
| 0.0318        | 58.0645 | 1800 | 0.9055          | 0.5733 | 0.1364 |
| 0.0238        | 61.2903 | 1900 | 0.9497          | 0.5696 | 0.1363 |
| 0.0257        | 64.5161 | 2000 | 0.9268          | 0.5590 | 0.1330 |
| 0.0266        | 67.7419 | 2100 | 0.9374          | 0.5703 | 0.1351 |
| 0.0292        | 70.9677 | 2200 | 0.9304          | 0.5754 | 0.1352 |
| 0.0288        | 74.1935 | 2300 | 0.9419          | 0.5649 | 0.1334 |
| 0.0125        | 77.4194 | 2400 | 0.9625          | 0.5581 | 0.1335 |
| 0.0241        | 80.6452 | 2500 | 0.9449          | 0.5569 | 0.1313 |
| 0.0217        | 83.8710 | 2600 | 0.9315          | 0.5504 | 0.1292 |
| 0.0136        | 87.0968 | 2700 | 0.9079          | 0.5373 | 0.1257 |
| 0.0203        | 90.3226 | 2800 | 0.8935          | 0.5373 | 0.1241 |
| 0.0166        | 93.5484 | 2900 | 0.9169          | 0.5354 | 0.1239 |
| 0.0114        | 96.7742 | 3000 | 0.9245          | 0.5323 | 0.1240 |
| 0.011         | 100.0   | 3100 | 0.9205          | 0.5337 | 0.1244 |


### Framework versions

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