---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-phoneme-timit
  results: []
datasets:
- timit_asr
language:
- en
---

<!-- 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. -->

# working

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3630
- Wer: 0.6243
- Cer: 0.1316

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 3.5325        | 11.9   | 1000  | 3.4897          | 1.0    | 0.9266 |
| 2.1973        | 23.81  | 2000  | 1.1350          | 0.8396 | 0.2403 |
| 1.4762        | 35.71  | 3000  | 0.5270          | 0.6845 | 0.1563 |
| 1.2409        | 47.62  | 4000  | 0.4195          | 0.6331 | 0.1403 |
| 1.1241        | 59.52  | 5000  | 0.3845          | 0.6362 | 0.1379 |
| 1.024         | 71.43  | 6000  | 0.3716          | 0.6321 | 0.1355 |
| 0.9922        | 83.33  | 7000  | 0.3728          | 0.6290 | 0.1331 |
| 0.9432        | 95.24  | 8000  | 0.3648          | 0.6170 | 0.1321 |
| 0.9279        | 107.14 | 9000  | 0.3643          | 0.6248 | 0.1325 |
| 0.9268        | 119.05 | 10000 | 0.3630          | 0.6243 | 0.1316 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2