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") - Notebooks
- Google Colab
- Kaggle
File size: 421 Bytes
7c7665e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"[PAD]": 36,
"[UNK]": 35,
"a": 1,
"b": 2,
"d": 3,
"e": 4,
"f": 5,
"h": 6,
"i": 7,
"j": 8,
"k": 9,
"l": 10,
"m": 11,
"n": 12,
"o": 13,
"p": 14,
"r": 15,
"s": 16,
"t": 17,
"u": 18,
"v": 19,
"w": 20,
"z": 21,
"|": 0,
"ŋ": 22,
"ɔ": 23,
"ɛ": 24,
"ɡ": 25,
"ɪ": 26,
"ɲ": 27,
"ɾ": 28,
"ʃ": 29,
"ʊ": 30,
"ʎ": 31,
"ʒ": 32,
"ː": 33,
"̪": 34
}
|