Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rossevine/Check_Model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Check_Model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Check_Model_1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Check_Model_1") model = AutoModelForCTC.from_pretrained("rossevine/Check_Model_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-large | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - common_voice | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Check_Model_1 | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: common_voice | |
| type: common_voice | |
| config: id | |
| split: test | |
| args: id | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 0.37479022934924483 | |
| <!-- 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. --> | |
| # Check_Model_1 | |
| This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the common_voice dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5522 | |
| - Wer: 0.3748 | |
| - Cer: 0.1158 | |
| ## 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 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | |
| | 2.1839 | 3.23 | 400 | 0.8796 | 0.7306 | 0.2332 | | |
| | 0.6388 | 6.45 | 800 | 0.8702 | 0.6410 | 0.2200 | | |
| | 0.4695 | 9.68 | 1200 | 0.7064 | 0.5360 | 0.1632 | | |
| | 0.3659 | 12.9 | 1600 | 0.5814 | 0.5211 | 0.1662 | | |
| | 0.285 | 16.13 | 2000 | 0.6394 | 0.5041 | 0.1663 | | |
| | 0.2254 | 19.35 | 2400 | 0.5889 | 0.4428 | 0.1405 | | |
| | 0.1801 | 22.58 | 2800 | 0.5712 | 0.4013 | 0.1182 | | |
| | 0.1392 | 25.81 | 3200 | 0.5914 | 0.3934 | 0.1177 | | |
| | 0.1051 | 29.03 | 3600 | 0.5522 | 0.3748 | 0.1158 | | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.13.3 | |