Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2-bert
Generated from Trainer
Instructions to use LevonHakobyan/adapter_head_l2_l23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LevonHakobyan/adapter_head_l2_l23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="LevonHakobyan/adapter_head_l2_l23")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("LevonHakobyan/adapter_head_l2_l23") model = AutoModelForCTC.from_pretrained("LevonHakobyan/adapter_head_l2_l23") - Notebooks
- Google Colab
- Kaggle
adapter_head_l2_l23
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.5190
- eval_wer: 0.9259
- eval_cer: 0.2445
- eval_runtime: 222.1952
- eval_samples_per_second: 19.267
- eval_steps_per_second: 2.412
- epoch: 69.2308
- step: 22500
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for LevonHakobyan/adapter_head_l2_l23
Base model
facebook/w2v-bert-2.0