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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
import noisereduce as nr # Ajout de la bibliothèque pour réduire le bruit
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")
# Réduction du bruit (si nécessaire)
audio_denoised = nr.reduce_noise(y=audio, sr=sr)
print("Bruit réduit.")
# Extraction des MFCC
mfccs = librosa.feature.mfcc(y=audio_denoised, 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_denoised) // 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_denoised[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 !")
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