---
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: xlsr-he-adap-es
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: he
      split: validation
      args: he
    metrics:
    - name: Wer
      type: wer
      value: 0.7473309608540926
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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-hebrew/runs/vr6ermqe)
# xlsr-he-adap-es

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: 2.3575
- Wer: 0.7473
- Cer: 0.3212

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 4.9752        | 3.3333  | 100  | 5.0053          | 1.0    | 1.0    |
| 3.5082        | 6.6667  | 200  | 3.7745          | 1.0    | 1.0    |
| 3.3178        | 10.0    | 300  | 3.4926          | 1.0    | 1.0    |
| 3.2202        | 13.3333 | 400  | 3.4051          | 1.0    | 1.0    |
| 3.2765        | 16.6667 | 500  | 3.4608          | 1.0    | 0.9692 |
| 3.1366        | 20.0    | 600  | 3.4355          | 0.9990 | 0.9359 |
| 3.1141        | 23.3333 | 700  | 3.3969          | 1.0    | 0.9301 |
| 2.6831        | 26.6667 | 800  | 2.8457          | 1.0239 | 0.7439 |
| 1.2342        | 30.0    | 900  | 1.7356          | 0.8983 | 0.4213 |
| 0.6381        | 33.3333 | 1000 | 1.3967          | 0.8541 | 0.3920 |
| 0.7105        | 36.6667 | 1100 | 1.4446          | 0.8261 | 0.3728 |
| 0.5292        | 40.0    | 1200 | 1.7486          | 0.8180 | 0.3681 |
| 0.2724        | 43.3333 | 1300 | 1.6140          | 0.8231 | 0.3571 |
| 0.3193        | 46.6667 | 1400 | 1.7165          | 0.8327 | 0.3538 |
| 0.304         | 50.0    | 1500 | 1.8261          | 0.7911 | 0.3372 |
| 0.1434        | 53.3333 | 1600 | 1.8850          | 0.7875 | 0.3361 |
| 0.151         | 56.6667 | 1700 | 1.9615          | 0.7824 | 0.3377 |
| 0.1663        | 60.0    | 1800 | 2.0116          | 0.7850 | 0.3352 |
| 0.1473        | 63.3333 | 1900 | 2.0547          | 0.7748 | 0.3309 |
| 0.1021        | 66.6667 | 2000 | 2.2370          | 0.7682 | 0.3305 |
| 0.1984        | 70.0    | 2100 | 2.2134          | 0.7560 | 0.3257 |
| 0.1731        | 73.3333 | 2200 | 2.2655          | 0.7483 | 0.3265 |
| 0.2154        | 76.6667 | 2300 | 2.3460          | 0.7514 | 0.3266 |
| 0.1106        | 80.0    | 2400 | 2.2466          | 0.7585 | 0.3260 |
| 0.1152        | 83.3333 | 2500 | 2.2369          | 0.7631 | 0.3309 |
| 0.0955        | 86.6667 | 2600 | 2.2950          | 0.7499 | 0.3223 |
| 0.1673        | 90.0    | 2700 | 2.3182          | 0.7453 | 0.3202 |
| 0.1635        | 93.3333 | 2800 | 2.3247          | 0.7428 | 0.3201 |
| 0.101         | 96.6667 | 2900 | 2.3576          | 0.7463 | 0.3215 |
| 0.1347        | 100.0   | 3000 | 2.3575          | 0.7473 | 0.3212 |


### Framework versions

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