Automatic Speech Recognition
Transformers
PyTorch
Latin
wav2vec2
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use lsb/wav2vec2-base-it-latin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lsb/wav2vec2-base-it-latin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lsb/wav2vec2-base-it-latin")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lsb/wav2vec2-base-it-latin") model = AutoModelForCTC.from_pretrained("lsb/wav2vec2-base-it-latin") - Notebooks
- Google Colab
- Kaggle
wav2vec2-base-it-latin
This model is a fine-tuned version of wav2vec2-base-it-voxpopuli
The dataset used is the poetaexmachina-mp3-recitations, all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).
It achieves the following results on the evaluation set:
- Loss: 0.1943
- WER: 0.398
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Evaluation results
- Test WER on Poeta Ex Machina mp3 recitationsself-reported0.398