Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("CordwainerSmith/whisper-tiny-PolyAI-minds14")
model = AutoModelForSpeechSeq2Seq.from_pretrained("CordwainerSmith/whisper-tiny-PolyAI-minds14")Quick Links
whisper-tiny-PolyAI-minds14
This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0321
- Wer Ortho: 0.0237
- Wer: 0.0236
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 |
|---|---|---|---|---|---|
| 0.0003 | 14.29 | 500 | 0.0321 | 0.0237 | 0.0236 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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Model tree for CordwainerSmith/whisper-tiny-PolyAI-minds14
Dataset used to train CordwainerSmith/whisper-tiny-PolyAI-minds14
Evaluation results
- Wer on PolyAI/minds14self-reported0.024
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CordwainerSmith/whisper-tiny-PolyAI-minds14")