Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr") model = AutoModelForCTC.from_pretrained("jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr") - Notebooks
- Google Colab
- Kaggle
wav2vec2-large-xls-r-300m-breton-colab_lr
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: 3.3053
- Wer: 1.0
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: 3e-05
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 14.0739 | 0.67 | 200 | 7.9939 | 1.0 |
| 4.5637 | 1.34 | 400 | 3.9380 | 1.0 |
| 3.2975 | 2.02 | 600 | 3.1902 | 1.0 |
| 3.0457 | 2.69 | 800 | 3.1215 | 1.0 |
| 2.8262 | 3.36 | 1000 | 3.1005 | 1.0 |
| 2.2082 | 4.03 | 1200 | 3.2135 | 1.0 |
| 1.7804 | 4.71 | 1400 | 3.3053 | 1.0 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for jcrkn/wav2vec2-large-xls-r-300m-breton-colab_lr
Base model
facebook/wav2vec2-xls-r-300mEvaluation results
- Wer on common_voice_13_0test set self-reported1.000