Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Vietnamese
whisper
Generated from Trainer
Instructions to use QuanHcmus/whisper-base-datavie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuanHcmus/whisper-base-datavie with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="QuanHcmus/whisper-base-datavie")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("QuanHcmus/whisper-base-datavie") model = AutoModelForSpeechSeq2Seq.from_pretrained("QuanHcmus/whisper-base-datavie") - Notebooks
- Google Colab
- Kaggle
Whisper Base Vie
This model is a fine-tuned version of openai/whisper-base on the datavie 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: 48
- 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
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 2
Model tree for QuanHcmus/whisper-base-datavie
Base model
openai/whisper-base