wav2vec2-base-Speech_Recognition_Dataset
This model is a fine-tuned version of facebook/wav2vec2-base. It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Audio-Projects/Automatic%20Speech%20Recognition/Speech%20Recognition%20Dataset/ASR_Speech_Recognition_Dataset.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/phmanhth/speech-recognition-dataset
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
- training_steps: 2000
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.0 | 1.04 | 1000 | nan | 1.0 |
0.0 | 2.07 | 2000 | nan | 1.0 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
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