Instructions to use rohitp1/libri-alpha-0.5-Temp-1-att with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rohitp1/libri-alpha-0.5-Temp-1-att with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rohitp1/libri-alpha-0.5-Temp-1-att")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rohitp1/libri-alpha-0.5-Temp-1-att") model = AutoModelForCTC.from_pretrained("rohitp1/libri-alpha-0.5-Temp-1-att") - Notebooks
- Google Colab
- Kaggle
libri-alpha-0.5-Temp-1-att
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 31.9525
- eval_wer: 0.0993
- eval_runtime: 165.9829
- eval_samples_per_second: 16.285
- eval_steps_per_second: 4.073
- epoch: 13.45
- step: 12000
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 40
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
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