Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Spanish
whisper
Generated from Trainer
Instructions to use ROGRANMAR/whisper-espanol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ROGRANMAR/whisper-espanol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ROGRANMAR/whisper-espanol")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ROGRANMAR/whisper-espanol") model = AutoModelForSpeechSeq2Seq.from_pretrained("ROGRANMAR/whisper-espanol") - Notebooks
- Google Colab
- Kaggle
Whisper Small spanish - ROGRANMAR
This model is a fine-tuned version of openai/whisper-small on the minds dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 100.0
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: 0.1
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 40
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 5.0 | 10 | 141.9978 | 1248.1793 |
| No log | 10.0 | 20 | nan | 100.0 |
| 77.0413 | 15.0 | 30 | nan | 100.0 |
| 77.0413 | 20.0 | 40 | nan | 100.0 |
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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