Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Vietnamese
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use arun100/whisper-small-vi-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arun100/whisper-small-vi-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arun100/whisper-small-vi-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arun100/whisper-small-vi-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("arun100/whisper-small-vi-2") - Notebooks
- Google Colab
- Kaggle
Whisper Small Vienamese
This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_16_0 vi dataset. It achieves the following results on the evaluation set:
- Loss: 0.6705
- Wer: 24.5680
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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 2500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0174 | 33.0 | 500 | 0.6207 | 24.6696 |
| 0.0045 | 66.0 | 1000 | 0.6705 | 24.5680 |
| 0.0027 | 99.01 | 1500 | 0.6945 | 25.2795 |
| 0.002 | 133.0 | 2000 | 0.7079 | 26.4790 |
| 0.0018 | 166.0 | 2500 | 0.7127 | 26.3976 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
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Model tree for arun100/whisper-small-vi-2
Base model
openai/whisper-smallEvaluation results
- Wer on mozilla-foundation/common_voice_16_0 vitest set self-reported24.568