metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b29-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: 0.231951560316721
b29-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: 0.2967
- Wer: 0.2320
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 |
|---|---|---|---|---|
| 6.3337 | 3.05 | 400 | 2.9529 | 1.0 |
| 2.9274 | 6.11 | 800 | 2.8462 | 0.9995 |
| 1.0082 | 9.16 | 1200 | 0.3782 | 0.3628 |
| 0.2754 | 12.21 | 1600 | 0.3225 | 0.2857 |
| 0.168 | 15.27 | 2000 | 0.3102 | 0.2748 |
| 0.1198 | 18.32 | 2400 | 0.3077 | 0.2513 |
| 0.1053 | 21.37 | 2800 | 0.3086 | 0.2531 |
| 0.0829 | 24.43 | 3200 | 0.2985 | 0.2396 |
| 0.0726 | 27.48 | 3600 | 0.2967 | 0.2320 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3