app.py

import gradio as gr
import librosa
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from transformers import pipeline

print("Chargement du modèle Wav2Vec2...")  
stt_pipeline = pipeline("automatic-speech-recognition", model="boumehdi/wav2vec2-large-xlsr-moroccan-darija")
print("Modèle chargé avec succès !")

def process_audio(audio_path):
    print(f"Fichier reçu : {audio_path}")

    try:
        # Charger uniquement les 30 premières secondes
        audio, sr = librosa.load(audio_path, sr=None, duration=30)
        print(f"Audio chargé : {len(audio)} échantillons à {sr} Hz")

        # Extraction des MFCC
        mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
        print(f"MFCC extrait, shape: {mfccs.shape}")

        # Normalisation
        scaler = StandardScaler()
        mfccs_scaled = scaler.fit_transform(mfccs.T)
        print("MFCC normalisé.")

        # Clustering avec KMeans
        kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)
        speaker_labels = kmeans.fit_predict(mfccs_scaled)
        print(f"Clustering terminé, {len(set(speaker_labels))} locuteurs détectés.")

        # Segmentation et transcription
        transcriptions = []
        segment_duration = len(audio) // len(speaker_labels)

        print("Début de la transcription...")
        for i in range(0, len(audio), sr * 5):
            segment = audio[i : i + sr * 5]
            if len(segment) < sr:
                continue

            transcription = stt_pipeline(segment)  # Transcription
            transcriptions.append(f"Speaker {speaker_labels[i // segment_duration]}: {transcription['text']}")
            print(f"Segment {i // sr}-{(i + sr * 5) // sr}s transcrit.")

        print("Transcription terminée !")
        return "\n".join(transcriptions)

    except Exception as e:
        print(f"Erreur : {e}")
        return "Une erreur s'est produite."

# Interface Gradio
print("Démarrage de Gradio...")
iface = gr.Interface(
    fn=process_audio,
    inputs=gr.Audio(type="filepath"),
    outputs="text",
    title="Speaker Diarization & Transcription",
    description="Upload an audio file to detect speakers and transcribe speech for each segment."
)

iface.launch()
print("Interface lancée avec succès !")