Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rossevine/Check_Model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Check_Model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Check_Model_2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Check_Model_2") model = AutoModelForCTC.from_pretrained("rossevine/Check_Model_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-xls-r-300m | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - common_voice | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Check_Model_2 | |
| 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: 0.2728883087823979 | |
| <!-- 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_2 | |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3499 | |
| - Wer: 0.2729 | |
| - Cer: 0.0673 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | |
| | 3.8708 | 3.23 | 400 | 0.7345 | 0.7259 | 0.2034 | | |
| | 0.4247 | 6.45 | 800 | 0.4128 | 0.4268 | 0.1102 | | |
| | 0.2047 | 9.68 | 1200 | 0.3726 | 0.3795 | 0.0930 | | |
| | 0.1422 | 12.9 | 1600 | 0.3690 | 0.3514 | 0.0884 | | |
| | 0.1139 | 16.13 | 2000 | 0.3811 | 0.3160 | 0.0794 | | |
| | 0.089 | 19.35 | 2400 | 0.3650 | 0.2895 | 0.0731 | | |
| | 0.0709 | 22.58 | 2800 | 0.3629 | 0.2944 | 0.0727 | | |
| | 0.0594 | 25.81 | 3200 | 0.3538 | 0.2779 | 0.0692 | | |
| | 0.0478 | 29.03 | 3600 | 0.3499 | 0.2729 | 0.0673 | | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.13.3 | |