---
license: apache-2.0
base_model: nutella-toast/wav2vec2-large-xls-r-ssw
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-ssw
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ml-superb-subset
      type: ml-superb-subset
      config: ssw
      split: dev
      args: ssw
    metrics:
    - name: Wer
      type: wer
      value: 0.940809968847352
---

<!-- 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. -->

# wav2vec2-large-xls-r-ssw

This model is a fine-tuned version of [nutella-toast/wav2vec2-large-xls-r-ssw](https://huggingface.co/nutella-toast/wav2vec2-large-xls-r-ssw) on the ml-superb-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0000
- Wer: 0.9408

## Model description

Finetuned version of vanilla Wav2Vec2 for CS224S.

## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.3831        | 1.0471 | 100  | 1.3053          | 1.0    |
| 1.2606        | 2.0942 | 200  | 1.1802          | 0.9720 |
| 1.0789        | 3.1414 | 300  | 1.0889          | 1.0405 |
| 0.9249        | 4.1885 | 400  | 1.0000          | 0.9408 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1