Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Rashmi21/whisper-small-vt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rashmi21/whisper-small-vt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rashmi21/whisper-small-vt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Rashmi21/whisper-small-vt") model = AutoModelForSpeechSeq2Seq.from_pretrained("Rashmi21/whisper-small-vt") - Notebooks
- Google Colab
- Kaggle
Whisper Small vd - Rashmi Shinde
This model is a fine-tuned version of openai/whisper-small on the videos data dataset. It achieves the following results on the evaluation set:
- Loss: 0.5692
- Wer Ortho: 14.7630
- Wer: 11.3930
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0002 | 50.0 | 500 | 0.5692 | 14.7630 | 11.3930 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for Rashmi21/whisper-small-vt
Base model
openai/whisper-smallEvaluation results
- Wer on videos dataself-reported11.393