# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("stillerman/stammer-libristutter-small")
model = AutoModelForSpeechSeq2Seq.from_pretrained("stillerman/stammer-libristutter-small")Quick Links
stammer-libristutter-small
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.2804
- eval_wer: 18.7648
- eval_runtime: 69.9551
- eval_samples_per_second: 2.859
- eval_steps_per_second: 0.357
- epoch: 2.56
- step: 128
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32
- training_steps: 256
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.43.2
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 3
Model tree for stillerman/stammer-libristutter-small
Base model
openai/whisper-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stillerman/stammer-libristutter-small")