Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Nepali
whisper
Generated from Trainer
Instructions to use NKRSubedi/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NKRSubedi/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NKRSubedi/checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NKRSubedi/checkpoints") model = AutoModelForSpeechSeq2Seq.from_pretrained("NKRSubedi/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from NKRSubedi/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
-
https://huggingface.co/NKRSubedi/checkpoints/resolve/main/README.md
- Command line
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hf download hf://NKRSubedi/checkpoints/README.md
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curl -L -o README.md https://huggingface.co/NKRSubedi/checkpoints/resolve/main/README.md
1.34 kB
| library_name: transformers | |
| language: | |
| - ne | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - NKRSubedi/Nepali-Large-Dataset-Clean | |
| model-index: | |
| - name: NKRSubedi/Nepali-Large-Model-Whisper-Small | |
| 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. --> | |
| # NKRSubedi/Nepali-Large-Model-Whisper-Small | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Nepali-Large-Dataset-Clean dataset. | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 5 | |
| - training_steps: 250 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.1 | |