Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Malasar
whisper
Generated from Trainer
Instructions to use vrclc/Malasar_small_DTF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrclc/Malasar_small_DTF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vrclc/Malasar_small_DTF")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vrclc/Malasar_small_DTF") model = AutoModelForSpeechSeq2Seq.from_pretrained("vrclc/Malasar_small_DTF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -69,3 +69,18 @@ The following hyperparameters were used during training:
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- Pytorch 2.1.2+cu121
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- Datasets 2.16.0
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- Tokenizers 0.19.1
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- Pytorch 2.1.2+cu121
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- Datasets 2.16.0
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- Tokenizers 0.19.1
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### Citation
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```bibtex
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@misc{multistage2024,
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title={Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages},
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author={Leena G Pillai, Kavya Manohar, Basil K Raju, Elizabeth Sherly},
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year={2024},
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eprint={2411.04573},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2411.04573},
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}
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```
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