Instructions to use ambind/whisper-tiny-lora-pl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ambind/whisper-tiny-lora-pl with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
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Download README.md from ambind/whisper-tiny-lora-pl: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
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https://huggingface.co/ambind/whisper-tiny-lora-pl/resolve/main/README.md
- Command line
-
hf download hf://ambind/whisper-tiny-lora-pl/README.md
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curl -L -o README.md https://huggingface.co/ambind/whisper-tiny-lora-pl/resolve/main/README.md
1.32 kB
metadata
library_name: peft
language:
- pl
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper tiny pl
results: []
Whisper tiny pl
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 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: 0.0002
- train_batch_size: 16
- 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: 50
- training_steps: 200
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.13.3.dev0
- Transformers 4.46.1
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1