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import torch
import torch.nn.functional as F
import torchaudio
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from src.multimodal import MultimodalSentimentClassifier
# 1. Transcription CTC
def transcribe(audio_path: str) -> str:
processor = Wav2Vec2Processor.from_pretrained(
"jonatasgrosman/wav2vec2-large-xlsr-53-french",
#cache_dir="./models"
)
model_ctc = Wav2Vec2ForCTC.from_pretrained(
"jonatasgrosman/wav2vec2-large-xlsr-53-french",
#cache_dir="./models"
)
waveform, sr = torchaudio.load(audio_path)
if sr != 16000:
waveform = torchaudio.transforms.Resample(sr, 16000)(waveform)
if waveform.size(0) > 1:
waveform = waveform.mean(dim=0, keepdim=True)
inputs = processor(
waveform.squeeze().numpy(),
sampling_rate=16000,
return_tensors="pt",
padding=True
)
with torch.no_grad():
logits = model_ctc(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
return transcription.lower()
# 2. Inférence multimodale
def infer(audio_path: str) -> dict:
# a) transcrire l’audio
text = transcribe(audio_path)
# b) charger et exécuter le modèle multimodal
model = MultimodalSentimentClassifier()
logits = model(audio_path, text) # [1, n_classes]
probs = F.softmax(logits, dim=1).squeeze().tolist()
labels = ["négatif", "neutre", "positif"]
return { labels[i]: round(probs[i], 3) for i in range(len(labels)) }
# Test rapide en ligne de commande
if __name__ == "__main__":
import sys
if len(sys.argv) != 2:
print("Usage: python src/inference.py <chemin_vers_audio.wav>")
sys.exit(1)
res = infer(sys.argv[1])
print(f"Résultat multimodal : {res}")
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