app.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import scipy.io.wavfile as wav
from scipy.fftpack import idct
import gradio as gr
import os
import matplotlib.pyplot as plt
from huggingface_hub import hf_hub_download

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Modele CNN
class modele_CNN(nn.Module):
    def __init__(self, num_classes=8, dropout=0.3):
        super(modele_CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 1 * 62, 128) 
        self.fc2 = nn.Linear(128, num_classes)
        self.dropout = nn.Dropout(dropout)
    
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(x.size(0), -1)
        x = self.dropout(F.relu(self.fc1(x)))
        x = self.fc2(x)
        return x        

# Audio processor
class AudioProcessor:
    def Mel2Hz(self, mel): return 700 * (np.power(10, mel/2595)-1)
    def Hz2Mel(self, freq): return 2595 * np.log10(1+freq/700)
    def Hz2Ind(self, freq, fs, Tfft): return (freq*Tfft/fs).astype(int)
    
    def hamming(self, T): 
        if T <= 1:
            return np.ones(T)
        return 0.54-0.46*np.cos(2*np.pi*np.arange(T)/(T-1))

    def FiltresMel(self, fs, nf=36, Tfft=512, fmin=100, fmax=8000):
        Indices = self.Hz2Ind(self.Mel2Hz(np.linspace(self.Hz2Mel(fmin), self.Hz2Mel(min(fmax, fs/2)), nf+2)), fs, Tfft)
        filtres = np.zeros((int(Tfft/2), nf))
        for i in range(nf): filtres[Indices[i]:Indices[i+2], i] = self.hamming(Indices[i+2]-Indices[i])
        return filtres

    def spectrogram(self, x, T, p, Tfft):
        S = [] 
        for i in range(0, len(x)-T, p): S.append(x[i:i+T]*self.hamming(T))
        S = np.fft.fft(S, Tfft)
        return np.abs(S), np.angle(S)
    
    def mfcc(self, data, filtres, nc=13, T=256, p=64, Tfft=512):
        data = (data[1]-np.mean(data[1]))/np.std(data[1])
        amp, ph = self.spectrogram(data, T, p, Tfft)
        amp_f = np.log10(np.dot(amp[:, :int(Tfft/2)], filtres)+1)
        return idct(amp_f, n=nc, norm='ortho')

    def process_audio(self, audio_data, sr, audio_length=32000):
        if sr != 16000:
            audio_resampled = np.interp(
                np.linspace(0, len(audio_data), int(16000 * len(audio_data) / sr)),
                np.arange(len(audio_data)),
                audio_data
            )
            sgn = audio_resampled
            fs = 16000
        else:
            sgn = audio_data
            fs = sr
        
        sgn = np.array(sgn, dtype=np.float32)
        
        if len(sgn) > audio_length:
            sgn = sgn[:audio_length]
        else:
            sgn = np.pad(sgn, (0, audio_length - len(sgn)), mode='constant')
        
        filtres = self.FiltresMel(fs)
        sgn_features = self.mfcc([fs, sgn], filtres)
        
        mfcc_tensor = torch.tensor(sgn_features.T, dtype=torch.float32)
        mfcc_tensor = mfcc_tensor.unsqueeze(0).unsqueeze(0)
        
        return mfcc_tensor

# Fonction prédiction
def predict_speaker(audio, model, processor):
    if audio is None:
        return "Aucun audio détecté.", None
    
    try:
        import soundfile as sf
        audio_data, sr = sf.read(audio)  # <- ici tu lis direct l'audio
        input_tensor = processor.process_audio(audio_data, sr)
        
        device = next(model.parameters()).device
        input_tensor = input_tensor.to(device)
        
        with torch.no_grad():
            output = model(input_tensor)
            print(output)
            probabilities = F.softmax(output, dim=1)
            confidence, predicted_class = torch.max(probabilities, 1)
        
        speakers = ["George", "Jackson", "Lucas", "Nicolas", "Theo", "Yweweler", "Narimene"]
        predicted_speaker = speakers[predicted_class.item()]
        
        result = f"Locuteur reconnu : {predicted_speaker} (confiance : {confidence.item()*100:.2f}%)"
        
        probs_dict = {speakers[i]: float(probs) for i, probs in enumerate(probabilities[0].cpu().numpy())}
        
        return result, probs_dict
    
    except Exception as e:
        return f"Erreur : {str(e)}", None

# Charger modèle
def load_model(model_id="nareauow/my_speech_recognition", model_filename="model_3.pth"):
    try:
        model_path = hf_hub_download(repo_id=model_id, filename=model_filename)
        model = modele_CNN(num_classes=7, dropout=0.)
        model.load_state_dict(torch.load(model_path, map_location=device))
        model.to(device)
        model.eval()
        print("Modèle chargé avec succès !")
        return model
    except Exception as e:
        print(f"Erreur de chargement: {e}")
        return None

# Gradio Interface
def create_interface():
    processor = AudioProcessor()
    
    with gr.Blocks(title="Reconnaissance de Locuteur") as interface:
        gr.Markdown("# 🗣️ Reconnaissance de Locuteur")
        gr.Markdown("Enregistrez votre voix pendant 2 secondes pour identifier qui parle.")
        
        with gr.Row():
            with gr.Column():
                model_selector = gr.Dropdown(
                    choices=["model_1.pth", "model_2.pth", "model_3.pth"],
                    value="model_3.pth",
                    label="Choisissez le modèle"
                )
                audio_input = gr.Audio(sources=["microphone"], type="filepath", label="🎙️ Parlez ici")
                record_btn = gr.Button("Reconnaître")
            with gr.Column():
                result_text = gr.Textbox(label="Résultat")
                plot_output = gr.Plot(label="Confiance par locuteur")
        
        def recognize(audio, selected_model):
            model = load_model(model_filename=selected_model)  # Charger le modèle choisi
            res, probs = predict_speaker(audio, model, processor)
            fig = None
            if probs:
                fig, ax = plt.subplots()
                ax.bar(probs.keys(), probs.values(), color='skyblue')
                ax.set_ylim([0, 1])
                ax.set_ylabel("Confiance")
                ax.set_xlabel("Locuteurs")
                plt.xticks(rotation=45)
            return res, fig
        
        record_btn.click(fn=recognize, inputs=[audio_input, model_selector], outputs=[result_text, plot_output])
        
        gr.Markdown("""### Comment utiliser ?
        - Choisissez le modèle.
        - Cliquez sur 🎙️ pour enregistrer votre voix.
        - Cliquez sur **Reconnaître** pour obtenir la prédiction.
        """)
    
    return interface

# Lancer
if __name__ == "__main__":
    app = create_interface()
    app.launch(share=True)