Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Seyfelislem/afripspeech_data_aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seyfelislem/afripspeech_data_aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Seyfelislem/afripspeech_data_aug")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Seyfelislem/afripspeech_data_aug") model = AutoModelForSpeechSeq2Seq.from_pretrained("Seyfelislem/afripspeech_data_aug") - Notebooks
- Google Colab
- Kaggle
afripspeech_data_aug
This model is a fine-tuned version of afrispeech_large_A100 on the afrispeech-200 dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
https://huggingface.co/Seyfelislem/afripspeech_data_aug/tensorboard
Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
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Evaluation results
- Wer on afrispeech-200self-reported6.008