Instructions to use federicocosta1989/speech_representations_asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use federicocosta1989/speech_representations_asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="federicocosta1989/speech_representations_asr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("federicocosta1989/speech_representations_asr") model = AutoModelForCTC.from_pretrained("federicocosta1989/speech_representations_asr") - Notebooks
- Google Colab
- Kaggle
speech_representations_asr
This model is a fine-tuned version of federicocosta1989/hubert_ca on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.7772
- 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: 16
- 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: 5
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 16.8356 | 5.0 | 10 | 12.1508 | 1.0 |
| 6.3514 | 10.0 | 20 | 5.3859 | 1.0 |
| 4.0009 | 15.0 | 30 | 4.0669 | 1.0 |
| 4.36 | 20.0 | 40 | 3.7772 | 1.0 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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