Instructions to use rajat99/Fine_Tuning_XLSR_300M_testing_4_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajat99/Fine_Tuning_XLSR_300M_testing_4_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rajat99/Fine_Tuning_XLSR_300M_testing_4_model")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_testing_4_model") model = AutoModelForCTC.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_testing_4_model") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Fine_Tuning_XLSR_300M_testing_4_model
results: []
Fine_Tuning_XLSR_300M_testing_4_model
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None 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: 0.1
- 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: 5
Training results
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3