Automatic Speech Recognition
Transformers
PyTorch
Swedish
wav2vec2
mozilla-foundation/common_voice_9_0
Generated from Trainer
Eval Results (legacy)
Instructions to use marinone94/xls-r-300m-sv-robust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marinone94/xls-r-300m-sv-robust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="marinone94/xls-r-300m-sv-robust")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("marinone94/xls-r-300m-sv-robust") model = AutoModelForCTC.from_pretrained("marinone94/xls-r-300m-sv-robust") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- ab
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set:
- Loss: 155.7939
- Wer: 1.0842
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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
Training results
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0