app.py

import gradio as gr
import librosa
import numpy as np
import soundfile as sf
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.")

        # Regrouper les segments audio par speaker
        speaker_audio = {speaker: [] for speaker in set(speaker_labels)}
        segment_duration = len(audio) // len(speaker_labels)

        for i in range(len(speaker_labels)):
            start = i * segment_duration
            end = start + segment_duration
            speaker_id = speaker_labels[i]
            speaker_audio[speaker_id].extend(audio[start:end])

        # Transcrire les segments fusionnés
        result = []
        for speaker, audio_segment in speaker_audio.items():
            if len(audio_segment) == 0:
                continue
            
            temp_filename = f"temp_speaker_{speaker}.wav"
            sf.write(temp_filename, np.array(audio_segment), sr)  # Sauvegarder le segment
            
            transcription = stt_pipeline(temp_filename)  # Transcrire
            result.append(f"Speaker {speaker}: {transcription['text']}")

            print(f"Transcription Speaker {speaker} terminée.")

        return "\n".join(result)

    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 !")