metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b28-wav2vec2-large-xls-r-romansh-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: rm-vallader
split: test
args: rm-vallader
metrics:
- name: Wer
type: wer
value: 0.27619934792734047
b28-wav2vec2-large-xls-r-romansh-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3534
- Wer: 0.2762
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.6021 | 3.05 | 400 | 2.9592 | 1.0 |
| 1.8751 | 6.11 | 800 | 0.5165 | 0.5247 |
| 0.295 | 9.16 | 1200 | 0.3708 | 0.3901 |
| 0.1398 | 12.21 | 1600 | 0.3738 | 0.3419 |
| 0.0968 | 15.27 | 2000 | 0.3595 | 0.3202 |
| 0.0693 | 18.32 | 2400 | 0.3392 | 0.3048 |
| 0.0593 | 21.37 | 2800 | 0.3660 | 0.2951 |
| 0.0464 | 24.43 | 3200 | 0.3596 | 0.2809 |
| 0.0404 | 27.48 | 3600 | 0.3534 | 0.2762 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3