Instructions to use Semih/wav2vec2_Irish_Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Semih/wav2vec2_Irish_Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Semih/wav2vec2_Irish_Large")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Semih/wav2vec2_Irish_Large") model = AutoModelForCTC.from_pretrained("Semih/wav2vec2_Irish_Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: ga-IE
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Irish by Semih GULUM
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice gle
type: common_voice
args: ga-IE
metrics:
- name: Test WER
type: wer
wav2vec2-irish-lite Speech to Text
Usage
The model can be used directly (without a language model) as follows:
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("Semih/wav2vec2_Irish_Large")
model = Wav2Vec2ForCTC.from_pretrained("Semih/wav2vec2_Irish_Large")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
Test Result: 55.11