Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bqtsio/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bqtsio/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bqtsio/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bqtsio/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("bqtsio/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Small en-US - BT
This model is a fine-tuned version of openai/whisper-small on the Minds 14 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6225
- Wer Ortho: 25.3117
- Wer: 25.7007
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0003 | 17.8571 | 500 | 0.6225 | 25.3117 | 25.7007 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for bqtsio/whisper-small-dv
Base model
openai/whisper-smallEvaluation results
- Wer on Minds 14self-reported25.701