Instructions to use ntoldalagi/C0_LID_DEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntoldalagi/C0_LID_DEV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ntoldalagi/C0_LID_DEV")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ntoldalagi/C0_LID_DEV") model = AutoModelForCTC.from_pretrained("ntoldalagi/C0_LID_DEV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ntoldalagi/C0_LID_DEV: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
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https://huggingface.co/ntoldalagi/C0_LID_DEV/resolve/main/README.md
- Command line
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hf download hf://ntoldalagi/C0_LID_DEV/README.md
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curl -L -o README.md https://huggingface.co/ntoldalagi/C0_LID_DEV/resolve/main/README.md
1.79 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| name: C0_LID_DEV | |
| <!-- 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. --> | |
| # C0_LID_DEV | |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: inf | |
| - Wer: 0.8267 | |
| ## 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: 4 | |
| - eval_batch_size: 8 | |
| - 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 | |
| - num_epochs: 1 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| | |
| | No log | 0.0 | 25 | inf | 0.8426 | | |
| | 1.5354 | 0.17 | 2000 | inf | 0.8198 | | |
| | 1.5688 | 0.33 | 4000 | inf | 0.8271 | | |
| | 1.5294 | 0.5 | 6000 | inf | 0.8339 | | |
| | 1.1947 | 0.67 | 8000 | inf | 0.8260 | | |
| | 1.1534 | 0.83 | 10000 | inf | 0.8267 | | |
| | 1.1484 | 1.0 | 12000 | inf | 0.8267 | | |
| ### Framework versions | |
| - Transformers 4.11.3 | |
| - Pytorch 1.10.2+cu113 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.10.3 | |