Instructions to use krishnareddy/asr_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krishnareddy/asr_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="krishnareddy/asr_example")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("krishnareddy/asr_example") model = AutoModelForCTC.from_pretrained("krishnareddy/asr_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: asr_example | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # asr_example | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.8808 | |
| - 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: 1e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 8 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 2000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:---:| | |
| | 3.2136 | 50.0 | 500 | 3.1954 | 1.0 | | |
| | 2.8884 | 100.0 | 1000 | 2.9321 | 1.0 | | |
| | 2.7653 | 150.0 | 1500 | 2.8864 | 1.0 | | |
| | 2.7109 | 200.0 | 2000 | 2.8808 | 1.0 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |