Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Italian
wav2vec2
audio
speech
phonemize
phoneme
Eval Results (legacy)
Instructions to use Cnam-LMSSC/wav2vec2-italian-phonemizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cnam-LMSSC/wav2vec2-italian-phonemizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cnam-LMSSC/wav2vec2-italian-phonemizer")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Cnam-LMSSC/wav2vec2-italian-phonemizer") model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/wav2vec2-italian-phonemizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# Fine-tuned Italian Voxpopuli v2 wav2vec2-base model for speech-to-phoneme task in Italian
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Fine-tuned [facebook/wav2vec2-base-
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## Audio samplerate for usage
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# Fine-tuned Italian Voxpopuli v2 wav2vec2-base model for speech-to-phoneme task in Italian
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Fine-tuned [facebook/wav2vec2-base-it-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-it-voxpopuli-v2) for **Italian speech-to-phoneme** (without language model) using the train and validation splits of [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech).
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## Audio samplerate for usage
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