Instructions to use TheRamsay/wav2vec2-ctc-scratch-50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheRamsay/wav2vec2-ctc-scratch-50M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TheRamsay/wav2vec2-ctc-scratch-50M")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("TheRamsay/wav2vec2-ctc-scratch-50M") model = AutoModelForCTC.from_pretrained("TheRamsay/wav2vec2-ctc-scratch-50M") - Notebooks
- Google Colab
- Kaggle
wav2vec2-ctc-scratch-50M
This model is a fine-tuned version of fav-kky/wav2vec2-base-cs-80k-ClTRUS on an unknown dataset.
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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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_ratio: 0.45
- num_epochs: 10
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for TheRamsay/wav2vec2-ctc-scratch-50M
Base model
fav-kky/wav2vec2-base-cs-80k-ClTRUS