Instructions to use cminja/wav2vec2-sft-sr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cminja/wav2vec2-sft-sr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cminja/wav2vec2-sft-sr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cminja/wav2vec2-sft-sr") model = AutoModelForCTC.from_pretrained("cminja/wav2vec2-sft-sr") - Notebooks
- Google Colab
- Kaggle
wav2vec2-xlsr-530-serbian-colab This model is a finetune of facebook/wav2vec2-xls-r-300m on an juznevesti serbian dataset.
The following hyperparameters were used during training:
learning_rate: 0.0003 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 lr_scheduler_warmup_steps: 500 num_epochs: 30 Framework versions Transformers 4.20.0 Pytorch 1.12.0 Datasets 2.4.0 Tokenizers 0.12.1
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