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("itsally/Dataset")
model = AutoModelForSpeechSeq2Seq.from_pretrained("itsally/Dataset")Quick Links
Fine Tune Whisper on People Speech
This model is a fine-tuned version of openai/whisper-small on the Peoples Speech dataset. It achieves the following results on the evaluation set:
- Loss: 0.5068
- Wer: 14.7846
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0071 | 2.0 | 100 | 0.5080 | 14.2950 |
| 0.006 | 4.0 | 200 | 0.4859 | 14.1645 |
| 0.0012 | 6.0 | 300 | 0.4997 | 14.3603 |
| 0.0002 | 8.0 | 400 | 0.5017 | 14.4582 |
| 0.0005 | 10.0 | 500 | 0.5068 | 14.7846 |
Framework versions
- Transformers 4.52.0
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.21.4
- Downloads last month
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Model tree for itsally/Dataset
Base model
openai/whisper-smallDataset used to train itsally/Dataset
Evaluation results
- Wer on Peoples Speechself-reported14.785
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="itsally/Dataset")