Automatic Speech Recognition
Transformers
PyTorch
Swedish
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use torileatherman/whisper_small_sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use torileatherman/whisper_small_sv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="torileatherman/whisper_small_sv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("torileatherman/whisper_small_sv") model = AutoModelForSpeechSeq2Seq.from_pretrained("torileatherman/whisper_small_sv") - Notebooks
- Google Colab
- Kaggle
Whisper Small Swedish
This model is an adapted version of openai/whisper-small on the Common Voice 11.0 dataset in Swedish. It achieves the following results on the evaluation set:
- Wer: 19.8166
Model description & uses
This model is the openai whisper small transformer adapted for Swedish audio to text transcription. The model is available through its HuggingFace web app
Training and evaluation data
Data used for training is the initial 10% of train and validation of Swedish Common Voice 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Swedish Common Voice. The training data has been augmented with random noise, random pitching and change of the speed of the voice.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- weight decay: 0
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1379 | 0.95 | 1000 | 0.295811 | 21.467 |
| 0.0245 | 2.86 | 3000 | 0.300059 | 20.160 |
| 0.0060 | 3.82 | 4000 | 0.320301 | 19.762 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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Evaluation results
- Wer on Common Voice 11.0self-reported19.763