---
language:
- fr
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-large-xlsr-53
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: acoustic_model0_cv_17_fr_XLSR-53
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 17
      type: mozilla-foundation/common_voice_17_0
      config: fr
      split: test
      args: fr
    metrics:
    - type: wer
      value: 0.46428149722517414
      name: Wer
---

<!-- 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. -->

# acoustic_model0_cv_17_fr_XLSR-53

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5171
- Wer: 0.4643

## 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
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.0475        | 2.084  | 400  | 2.9894          | 0.9949 |
| 1.2413        | 5.0767 | 800  | 0.6745          | 0.5958 |
| 0.5812        | 8.0693 | 1200 | 0.5171          | 0.4643 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1