Instructions to use AstralZander/xlsr-finetune-igbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AstralZander/xlsr-finetune-igbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AstralZander/xlsr-finetune-igbo")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("AstralZander/xlsr-finetune-igbo") model = AutoModelForCTC.from_pretrained("AstralZander/xlsr-finetune-igbo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlsr-finetune-igbo
WIP, for the personal dataset. This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.7100
- eval_wer: 0.5062
- eval_runtime: 190.2812
- eval_samples_per_second: 5.113
- eval_steps_per_second: 0.641
- epoch: 14.71
- step: 1500
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: 9.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.29.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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