---
library_name: transformers
language:
- en
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- arielcerdap/TimeStamped
metrics:
- wer
model-index:
- name: Wav2Vec2 TimeStamped Stutter - Ariel Cerda
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: TimeStamped
      type: arielcerdap/TimeStamped
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.9991797676008203
---

<!-- 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 TimeStamped Stutter - Ariel Cerda

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the TimeStamped dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 0.9992

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 11.5515       | 1.1696  | 100  | 11.9112         | 1.0001 |
| 13.4395       | 2.3392  | 200  | 11.9112         | 1.0001 |
| 11.9169       | 3.5088  | 300  | 11.9112         | 1.0001 |
| 11.1801       | 4.6784  | 400  | 11.9112         | 1.0001 |
| 15.3393       | 5.8480  | 500  | 11.9112         | 1.0001 |
| 11.8177       | 7.0175  | 600  | 11.9112         | 1.0001 |
| 10.7941       | 8.1871  | 700  | 11.9112         | 1.0001 |
| 13.8075       | 9.3567  | 800  | 11.9112         | 1.0001 |
| 11.5275       | 10.5263 | 900  | 11.9112         | 1.0001 |
| 10.1228       | 11.6959 | 1000 | 11.9112         | 1.0001 |
| 13.7071       | 12.8655 | 1100 | 11.9112         | 1.0001 |
| 11.6933       | 14.0351 | 1200 | 11.9112         | 1.0001 |
| 10.885        | 15.2047 | 1300 | 11.9112         | 1.0001 |
| 14.2349       | 16.3743 | 1400 | 11.9112         | 1.0001 |
| 12.8886       | 17.5439 | 1500 | 11.9112         | 1.0001 |
| 0.0           | 18.7135 | 1600 | nan             | 0.9992 |
| 0.0           | 19.8830 | 1700 | nan             | 0.9992 |
| 0.0           | 21.0526 | 1800 | nan             | 0.9992 |
| 0.0           | 22.2222 | 1900 | nan             | 0.9992 |
| 0.0           | 23.3918 | 2000 | nan             | 0.9992 |
| 0.0           | 24.5614 | 2100 | nan             | 0.9992 |
| 0.0           | 25.7310 | 2200 | nan             | 0.9992 |
| 0.0           | 26.9006 | 2300 | nan             | 0.9992 |
| 0.0           | 28.0702 | 2400 | nan             | 0.9992 |
| 0.0           | 29.2398 | 2500 | nan             | 0.9992 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1