Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use OK923/wav2vec2-base-hindi_aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OK923/wav2vec2-base-hindi_aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="OK923/wav2vec2-base-hindi_aug")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("OK923/wav2vec2-base-hindi_aug") model = AutoModelForCTC.from_pretrained("OK923/wav2vec2-base-hindi_aug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-base-hindi_aug
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8480
- Wer: 0.6917
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.55.1
- Pytorch 2.6.0+cu124
- Datasets 2.18.0
- Tokenizers 0.21.4
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Model tree for OK923/wav2vec2-base-hindi_aug
Evaluation results
- Wer on common_voice_17_0self-reported0.692