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w2v-bert-2.0_yoruba_v1
FarmerlineML/w2v-bert-2.0_yoruba_v1
w2v-bert-2.0_yoruba_v1 is a automatic speech recognition model from FarmerlineML. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Jun 17, 2025
- Updated
- Jun 17, 2025
- SHA
- ccceb880
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
w2v-bert-2.0_yoruba_v1
This model is a fine-tuned version of FarmerlineML/w2v-bert-2.0_yoruba on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: inf
- eval_cer: 0.1043
- eval_wer: 0.3272
- eval_runtime: 138.6083
- eval_samples_per_second: 36.636
- eval_steps_per_second: 4.581
- epoch: 0.7354
- step: 4200
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 800
- num_epochs: 16
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Jun 17, 2025
- Updated
- Jun 17, 2025