--- language: - it license: apache-2.0 datasets: - mozilla-foundation/common_voice_8_0 metrics: - wer - cer tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - it - mozilla-foundation/common_voice_8_0 - speech - wav2vec2 model-index: - name: XLS-R Wav2Vec2 Italian by radiogroup crits results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8.0 italian type: mozilla-foundation/common_voice_8_0 args: it metrics: - name: Test WER type: wer value: 9.04 - name: Test CER type: cer value: 2.2 - name: Test WER (+LM) type: wer value: 6.24 - name: Test CER (+LM) type: cer value: 1.67 --- # XLS-R-1B-ITALIAN-DOC4LM-5GRAM ## Fine-tuned XLS-R 1B model for speech recognition in Italian Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). When using this model, make sure that your speech input is sampled at 16kHz. ## Language model information Our language model was generated using a dataset of Italian wikipedia articles and manual transcriptions of radio newspapers and television programs. ## Download CommonVoice8.0 dataset for italian language ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_8_0", "it", use_auth_token=True) ``` ## Evaluation Commands To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`: ```bash python eval.py --model_id radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram --dataset mozilla-foundation/common_voice_8_0 --config it --split test --log_outputs --greedy mv log_mozilla-foundation_common_voice_8_0_it_test_predictions.txt log_mozilla-foundation_common_voice_8_0_it_test_predictions_greedy.txt mv log_mozilla-foundation_common_voice_8_0_it_test_targets.txt log_mozilla-foundation_common_voice_8_0_it_test_targets_greedy.txt mv mozilla-foundation_common_voice_8_0_it_test_eval_results.txt mozilla-foundation_common_voice_8_0_it_test_eval_results_greedy.txt python eval.py --model_id radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram --dataset mozilla-foundation/common_voice_8_0 --config it --split test --log_outputs mv log_mozilla-foundation_common_voice_8_0_it_test_predictions.txt log_mozilla-foundation_common_voice_8_0_it_test_predictions_lm.txt mv log_mozilla-foundation_common_voice_8_0_it_test_targets.txt log_mozilla-foundation_common_voice_8_0_it_test_targets_lm.txt mv mozilla-foundation_common_voice_8_0_it_test_eval_results.txt mozilla-foundation_common_voice_8_0_it_test_eval_results_lm.txt ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{crits2022wav2vec2-xls-r-1b-italian-doc4lm-5gram, title={XLS-R Wav2Vec2 Italian by radiogroup crits}, author={Teraoni Prioletti Raffaele, Casagranda Paolo and Russo Francesco}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram}}, year={2022} } ```