# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("khalidey/ID2223_Lab2_Whisper_SV")
model = AutoModelForSpeechSeq2Seq.from_pretrained("khalidey/ID2223_Lab2_Whisper_SV")Quick Links
Whisper Small - Swedish
This is a fine-tuned version of the openai/whisper-small model on the Common Voice 11.0 dataset.
The following results were achieved after training for 4000 optimization steps:
- Training Loss: 0.003900
- Validation Loss: 0.326255
- WER: 19.894598
Training hyperparameters
The following hyperparameters were used during training:
- train_batch_size: 16
- gradient_accumulation_steps: 1
- learning_rate: 1e-5
- eval_batch_size: 8
- max_steps: 4000
- eval_steps: 1000
Framework Versions
- Transformers 4.25.0
- Pytorch 1.12.1
- Datasets 2.7.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="khalidey/ID2223_Lab2_Whisper_SV")