Instructions to use AstralZander/igbo_ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AstralZander/igbo_ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AstralZander/igbo_ASR")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("AstralZander/igbo_ASR") model = AutoModelForCTC.from_pretrained("AstralZander/igbo_ASR") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
language:
- ig
facebook/wav2vec2-xls-r-300m fine-tuned on google/fleurs and mozilla-foundation/common_voice_13_0 for Igbo language.
WER: 0.51
Code for running:
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("AstralZander/igbo_ASR")
audio_paths = [audio_path] # List with paths to audio
transcriptions = model.transcribe(audio_paths)
transcriptions # List of transcriptions, timestamps and probabilities
transcriptions[ind_audio]['transcription'] # Transcription of audio with the ind_audio index from the audio_paths list