Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Swedish
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use ZinebSN/whisper-small-swedish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZinebSN/whisper-small-swedish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ZinebSN/whisper-small-swedish")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ZinebSN/whisper-small-swedish") model = AutoModelForSpeechSeq2Seq.from_pretrained("ZinebSN/whisper-small-swedish") - Notebooks
- Google Colab
- Kaggle
Whisper Small hi - Swedish
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.2945
- eval_wer: 19.7602
- eval_runtime: 1248.2292
- eval_samples_per_second: 4.061
- eval_steps_per_second: 0.508
- epoch: 3.88
- step: 3000
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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