Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Kanuri
whisper
Generated from Trainer
Instructions to use doongsae/whisper_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use doongsae/whisper_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="doongsae/whisper_finetuning")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("doongsae/whisper_finetuning") model = AutoModelForSpeechSeq2Seq.from_pretrained("doongsae/whisper_finetuning") - Notebooks
- Google Colab
- Kaggle
Fine-tuning whisper
This model is a fine-tuned version of openai/whisper-base on the TTS dataset for Capstone Design II project in Sogang University.
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: 10
- training_steps: 100
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for doongsae/whisper_finetuning
Base model
openai/whisper-base