Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
Generated from Trainer
Instructions to use Ghayah/Whisper-Augmented-11-latin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ghayah/Whisper-Augmented-11-latin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Ghayah/Whisper-Augmented-11-latin")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Ghayah/Whisper-Augmented-11-latin") model = AutoModelForSpeechSeq2Seq.from_pretrained("Ghayah/Whisper-Augmented-11-latin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper-Augmented-11-latin
This model is a fine-tuned version of PFE-IRM/Whisper-IRM on an unknown dataset. It achieves the following results on the evaluation set:
- epoch: 1.71
- eval_loss: 0.1432
- eval_runtime: 5360.4927
- eval_samples_per_second: 2.327
- eval_steps_per_second: 0.291
- eval_wer: 13.1648
- step: 4000
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: 1.25e-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: 5000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Ghayah/Whisper-Augmented-11-latin
Base model
PFE-IRM/Whisper-IRM