Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use arkitex/wav2vec2-finetune-synthetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arkitex/wav2vec2-finetune-synthetic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arkitex/wav2vec2-finetune-synthetic")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("arkitex/wav2vec2-finetune-synthetic") model = AutoModelForCTC.from_pretrained("arkitex/wav2vec2-finetune-synthetic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-finetune-synthetic
This model is a fine-tuned version of facebook/wav2vec2-base on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 3.1877
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.8665 | 0.6105 | 500 | 3.3075 | 1.0 |
| 3.3045 | 1.2210 | 1000 | 3.1936 | 1.0 |
| 3.31 | 1.8315 | 1500 | 3.1613 | 1.0 |
| 3.2901 | 2.4420 | 2000 | 3.1877 | 1.0 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for arkitex/wav2vec2-finetune-synthetic
Base model
facebook/wav2vec2-baseEvaluation results
- Wer on common_voice_17_0self-reported1.000