Instructions to use rajat99/Fine_Tuning_XLSR_300M_on_OpenSLR_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_on_OpenSLR_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_on_OpenSLR_model")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_on_OpenSLR_model") model = AutoModelForCTC.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_on_OpenSLR_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fine_Tuning_XLSR_300M_on_OpenSLR_model
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.2669
- Wer: 1.0
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: 0.0003
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.5102 | 23.53 | 400 | 3.2669 | 1.0 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
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