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:
- sv-SE
license: cc0-1.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sv
- robust-speech-event
- model_for_talk
datasets:
- mozilla-foundation/common_voice_8_0
- marinone94/nst_sv
model-index:
- name: XLS-R-300M - Swedish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0
type: mozilla-foundation/common_voice_8_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 16.98
- name: Test CER
type: cer
value: 5.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: speech-recognition-community-v2/dev_data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 27.01
- name: Test CER
type: cer
value: 13.14
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on 2 epochs of the MARINONE94/NST_SV - SV dataset (80% random split with seed 42 as the dataset for now has only the "train" split), and then on 50 epochs of the the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset ("train+validation" split). See run.sh to have a complete overview of all the training steps. NOTE: the first training for now didn't work as expected, so it might be useless or even degrade performance. Further investigation and development is needed.