metadata
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- fleurs
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xlsr-fula-google-fleurs-5-hours
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: fleurs
type: fleurs
config: ff_sn
split: None
args: ff_sn
metrics:
- type: wer
value: 0.646049896049896
name: Wer
wav2vec2-xlsr-fula-google-fleurs-5-hours
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.1949
- Wer: 0.6460
- Cer: 0.2359
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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 7.1138 | 10.96 | 200 | 2.9561 | 1.0 | 1.0 |
| 2.8708 | 21.92 | 400 | 2.0221 | 1.0 | 0.6369 |
| 1.0031 | 32.88 | 600 | 0.9750 | 0.6509 | 0.2222 |
| 0.4471 | 43.84 | 800 | 1.1949 | 0.6460 | 0.2359 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2