Instructions to use akera/sb-wspr-lg-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akera/sb-wspr-lg-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="akera/sb-wspr-lg-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("akera/sb-wspr-lg-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("akera/sb-wspr-lg-small") - Notebooks
- Google Colab
- Kaggle
sb-wspr-lg-small
This model is a fine-tuned version of openai/whisper-small on an unknown dataset.
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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.0
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
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Model tree for akera/sb-wspr-lg-small
Base model
openai/whisper-small