app.py

app.py

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+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
+from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor
+from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import soundfile as sf
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Load speech-to-text model
+try:
+    speech_recognizer = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr").to(device)
+    speech_processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
+    print("Speech recognition model loaded successfully!")
+except Exception as e:
+    print(f"Error loading speech recognition model: {e}")
+    speech_recognizer = None
+    speech_processor = None
+
+# Load text-to-speech models
+try:
+    # Load processor and model
+    tts_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+    tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+    tts_vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
+    
+    # Load speaker embeddings
+    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+    speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(device)
+    print("Text-to-speech models loaded successfully!")
+except Exception as e:
+    print(f"Error loading text-to-speech models: {e}")
+    tts_processor = None
+    tts_model = None
+    tts_vocoder = None
+    speaker_embeddings = None
+
+# Modele CNN
+class modele_CNN(nn.Module):
+    def __init__(self, num_classes=7, 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
+
+# Speech recognition function
+def recognize_speech(audio_path):
+    if speech_recognizer is None or speech_processor is None:
+        return "Speech recognition model not available"
+    
+    try:
+        # Read audio file
+        audio_data, sr = sf.read(audio_path)
+        
+        # Resample to 16kHz if needed
+        if sr != 16000:
+            audio_data = np.interp(
+                np.linspace(0, len(audio_data), int(16000 * len(audio_data) / sr)),
+                np.arange(len(audio_data)),
+                audio_data
+            )
+            sr = 16000
+        
+        # Process audio
+        inputs = speech_processor(audio_data, sampling_rate=sr, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        
+        # Generate transcription
+        generated_ids = speech_recognizer.generate(**inputs)
+        transcription = speech_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        
+        return transcription
+    except Exception as e:
+        return f"Speech recognition error: {str(e)}"
+
+# Speech synthesis function
+def synthesize_speech(text):
+    if tts_processor is None or tts_model is None or tts_vocoder is None or speaker_embeddings is None:
+        return None
+    
+    try:
+        # Preprocess text
+        inputs = tts_processor(text=text, return_tensors="pt").to(device)
+        
+        # Generate speech with speaker embeddings
+        spectrogram = tts_model.generate_speech(inputs["input_ids"], speaker_embeddings)
+        
+        # Convert to waveform
+        with torch.no_grad():
+            speech = tts_vocoder(spectrogram)
+        
+        # Convert to numpy array and normalize
+        speech = speech.cpu().numpy()
+        speech = speech / np.max(np.abs(speech))
+        
+        return (16000, speech.squeeze())
+    except Exception as e:
+        print(f"Speech synthesis error: {str(e)}")
+        return None
+
+# Fonction prédiction
+def predict_speaker(audio, model, processor):
+    if audio is None:
+        return "Aucun audio détecté.", None, None
+    
+    try:
+        audio_data, sr = sf.read(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())}
+        
+        # Recognize speech
+        recognized_text = recognize_speech(audio)
+        
+        return result, probs_dict, recognized_text,predicted_speaker
+    
+    except Exception as e:
+        return f"Erreur : {str(e)}", None, 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")
+                recognized_text = gr.Textbox(label="Texte reconnu")
+                audio_output = gr.Audio(label="Synthèse vocale", type="numpy")
+        
+        def recognize(audio, selected_model):
+            model = load_model(model_filename=selected_model)
+            res, probs, text,locuteur = predict_speaker(audio, model, processor)
+            
+            # Generate plot
+            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)
+            
+            # Generate speech synthesis if text was recognized
+            synth_audio = None
+            if text and "error" not in text.lower():
+                synth_text = f"{locuteur} said  : {text}"
+                synth_audio = synthesize_speech(synth_text)
+            
+            return res, fig, text, synth_audio
+        
+        record_btn.click(fn=recognize, 
+                        inputs=[audio_input, model_selector], 
+                        outputs=[result_text, plot_output, recognized_text, audio_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)