Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use aadel4/kid-whisper-medium-en-myst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/kid-whisper-medium-en-myst with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aadel4/kid-whisper-medium-en-myst")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("aadel4/kid-whisper-medium-en-myst") model = AutoModelForSpeechSeq2Seq.from_pretrained("aadel4/kid-whisper-medium-en-myst") - Notebooks
- Google Colab
- Kaggle
openai/whisper-medium-en
This model is a fine-tuned version of openai/whisper-medium-en on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2578390836715698
- Wer: 8.311953858914759
Training and evaluation data
- Training data: Myst Train (125 hours)
- Evaluation data: Myst Dev (20.9 hours)
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- converged_after: 1500
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Evaluation results
- WER on myst-testtest set self-reported8.910
- WER on cslu_scriptedtest set self-reported47.940
- WER on cslu_spontaneoustest set self-reported25.560
- WER on librispeechself-reported3.950