---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-lv60
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: wav2vec2-librispeech-demo
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LIBRISPEECH_ASR - CLEAN
      type: librispeech_asr
      config: clean
      split: test
      args: 'Config: clean, Training split: test, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 1.0225474683544304
---

<!-- 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-librispeech-demo

This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0030
- Wer: 1.0225

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| No log        | 0.6329  | 100  | 3.9409          | 1.0    |
| No log        | 1.2658  | 200  | 3.0441          | 1.0    |
| No log        | 1.8987  | 300  | 2.9165          | 1.0    |
| No log        | 2.5316  | 400  | 1.4925          | 1.9968 |
| 3.7012        | 3.1646  | 500  | 0.3010          | 1.9446 |
| 3.7012        | 3.7975  | 600  | 0.1713          | 1.8259 |
| 3.7012        | 4.4304  | 700  | 0.0990          | 1.6163 |
| 3.7012        | 5.0633  | 800  | 0.0692          | 1.5439 |
| 3.7012        | 5.6962  | 900  | 0.0463          | 1.4233 |
| 0.1686        | 6.3291  | 1000 | 0.0389          | 1.3469 |
| 0.1686        | 6.9620  | 1100 | 0.0290          | 1.3101 |
| 0.1686        | 7.5949  | 1200 | 0.0204          | 1.1994 |
| 0.1686        | 8.2278  | 1300 | 0.0161          | 1.1839 |
| 0.1686        | 8.8608  | 1400 | 0.0143          | 1.1499 |
| 0.0553        | 9.4937  | 1500 | 0.0110          | 1.1460 |
| 0.0553        | 10.1266 | 1600 | 0.0082          | 1.0953 |
| 0.0553        | 10.7595 | 1700 | 0.0088          | 1.1119 |
| 0.0553        | 11.3924 | 1800 | 0.0059          | 1.0574 |
| 0.0553        | 12.0253 | 1900 | 0.0054          | 1.0510 |
| 0.0295        | 12.6582 | 2000 | 0.0042          | 1.0356 |
| 0.0295        | 13.2911 | 2100 | 0.0039          | 1.0360 |
| 0.0295        | 13.9241 | 2200 | 0.0033          | 1.0269 |
| 0.0295        | 14.5570 | 2300 | 0.0031          | 1.0237 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.5.1
- Datasets 2.21.0
- Tokenizers 0.19.1