---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: w2v2-lmk_updated
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.5749128919860628
---

<!-- 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. -->

# w2v2-lmk_updated

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0281
- Wer: 0.5749
- Cer: 0.2049

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 9.4495        | 6.25  | 100  | 4.3525          | 1.0    | 1.0    |
| 3.1633        | 12.5  | 200  | 2.9339          | 1.0    | 1.0    |
| 2.964         | 18.75 | 300  | 2.8446          | 1.0    | 1.0    |
| 2.8489        | 25.0  | 400  | 2.6748          | 1.0    | 1.0    |
| 2.409         | 31.25 | 500  | 1.9297          | 1.0    | 0.6824 |
| 1.6654        | 37.5  | 600  | 1.3226          | 0.9024 | 0.3686 |
| 1.2589        | 43.75 | 700  | 1.0957          | 0.7666 | 0.2749 |
| 1.0072        | 50.0  | 800  | 1.0241          | 0.6585 | 0.2445 |
| 0.818         | 56.25 | 900  | 1.0104          | 0.6132 | 0.2239 |
| 0.7072        | 62.5  | 1000 | 0.9941          | 0.5923 | 0.2155 |
| 0.5998        | 68.75 | 1100 | 1.0503          | 0.5749 | 0.2079 |
| 0.5677        | 75.0  | 1200 | 1.0376          | 0.5784 | 0.2049 |
| 0.5125        | 81.25 | 1300 | 1.0549          | 0.5819 | 0.2133 |
| 0.4983        | 87.5  | 1400 | 1.0222          | 0.5505 | 0.1988 |
| 0.4859        | 93.75 | 1500 | 1.0176          | 0.5575 | 0.1995 |
| 0.4436        | 100.0 | 1600 | 1.0281          | 0.5749 | 0.2049 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 3.0.0
- Tokenizers 0.22.1