Instructions to use toantam1290/whisper-large-v3-vietnamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toantam1290/whisper-large-v3-vietnamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="toantam1290/whisper-large-v3-vietnamese")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("toantam1290/whisper-large-v3-vietnamese") model = AutoModelForSpeechSeq2Seq.from_pretrained("toantam1290/whisper-large-v3-vietnamese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
whisper-large-v3-vietnamese
This model is a fine-tuned version of openai/whisper-large-v3 on the None 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: 4
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- optimizer: Use adafactor and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 624
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for toantam1290/whisper-large-v3-vietnamese
Base model
openai/whisper-large-v3