Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("David-Mazi/whisper-tiny.en-vox")
model = AutoModelForSpeechSeq2Seq.from_pretrained("David-Mazi/whisper-tiny.en-vox")Quick Links
Whisper Tiny En Vox - David Mazi
This model is a fine-tuned version of openai/whisper-tiny.en on the whisper-trial dataset. It achieves the following results on the evaluation set:
- Loss: 0.0019
- Wer: 70.0633
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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2694 | 4.0 | 100 | 0.1224 | 77.4886 |
| 0.0232 | 8.0 | 200 | 0.0165 | 69.1336 |
| 0.0035 | 12.0 | 300 | 0.0032 | 80.3548 |
| 0.0022 | 16.0 | 400 | 0.0021 | 68.5044 |
| 0.0019 | 20.0 | 500 | 0.0019 | 70.0633 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for David-Mazi/whisper-tiny.en-vox
Base model
openai/whisper-tiny.enEvaluation results
- Wer on whisper-trialself-reported70.063
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="David-Mazi/whisper-tiny.en-vox")