lectura-decodeur — Decodeur phonetique CTC du francais
Transcription audio vers phonemes IPA via un modele CNN-BiGRU-CTC medium (10.6M params, PER ~4.34%).
Le modele medium supporte les sigles, formules (nombres, dates, calculs) et la parole courante grace a un vocabulaire etendu de 59 tokens et un fine-tuning specialise.
Installation
# Avec backend ONNX (recommande)
pip install lectura-decodeur[onnx]
# Sans ONNX (mode API uniquement)
pip install lectura-decodeur
Utilisation
import numpy as np
from lectura_decodeur import creer_engine
engine = creer_engine()
# Audio PCM float32 mono 16kHz
audio = np.zeros(16000, dtype=np.float32) # 1 seconde de silence
result = engine.transcrire(audio)
print(result) # chaine IPA : "b ɔ̃ ʒ u ʁ | l ə | m ɔ̃ d"
Modele
| Caracteristique | Valeur |
|---|---|
| Architecture | CNN [48, 96] + BiGRU 384x4 + CTC head |
| Parametres | 10.6M |
| Performance | PER ~4.34% (formules v2) |
| Vocabulaire | 59 tokens (46 phones IPA + liaisons + ponctuation + speciaux) |
| Entrainement | Pre-train corpus complet + fine-tune formules/sigles multispeaker |
Backends
| Backend | Dependance | Latence | Modele |
|---|---|---|---|
| ONNX Runtime | onnxruntime |
~10 ms/s audio | phone_ctc_int8.onnx (38 Mo) |
| API | aucune | ~100 ms/s audio | serveur Lectura |
Parametres audio
- Sample rate : 16 kHz
- Format : PCM float32 mono
- Mel : 80 bins, n_fft=512, hop=160, win=400
Licence
Double licence : AGPL-3.0 (libre) + Licence Commerciale (payante).
Les modeles ONNX sont distribues separement — voir MODEL_LICENCE.md.
Auteur
Max Carriere — lectura.world
1 maintainer
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