Automatic Speech Recognition
Transformers
PyTorch
Persian
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use manifoldix/xlsr-fa-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manifoldix/xlsr-fa-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="manifoldix/xlsr-fa-lm")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("manifoldix/xlsr-fa-lm") model = AutoModelForCTC.from_pretrained("manifoldix/xlsr-fa-lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: fa
datasets:
- common_voice
tags:
- hf-asr-leaderboard
- robust-speech-event
widget:
- example_title: Common Voice sample 2978
src: https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample2978.flac
- example_title: Common Voice sample 5168
src: https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample5168.flac
model-index:
- name: XLS-R-300m Wav2Vec2 Persian
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fa
type: common_voice
args: fa
metrics:
- name: Test WER without LM
type: wer
value: 26%
- name: Test WER with LM
type: wer
value: 23%
XLSR-300m Persian
Fine-tuned on commom voice FA