Instructions to use phucdyale/vi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phucdyale/vi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="phucdyale/vi")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("phucdyale/vi") model = AutoModelForSpeechSeq2Seq.from_pretrained("phucdyale/vi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from phucdyale/vi: direct link, hf CLI and curl.
- Browser
- Download file 1.58 kB
-
https://huggingface.co/phucdyale/vi/resolve/main/README.md
- Command line
-
hf download hf://phucdyale/vi/README.md
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curl -L -o README.md https://huggingface.co/phucdyale/vi/resolve/main/README.md
1.58 kB
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: vi | |
| 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. --> | |
| # vi | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6612 | |
| - Wer: 1.2655 | |
| ## 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: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 4000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 0.0204 | 5.59 | 1000 | 0.5415 | 1.3689 | | |
| | 0.0005 | 11.17 | 2000 | 0.6213 | 1.2094 | | |
| | 0.0003 | 16.76 | 3000 | 0.6507 | 1.2515 | | |
| | 0.0002 | 22.35 | 4000 | 0.6612 | 1.2655 | | |
| ### Framework versions | |
| - Transformers 4.36.0.dev0 | |
| - Pytorch 1.12.1 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |