Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import os
+from pathlib import Path
+from synthesizer.inference import Synthesizer
+from encoder import inference as encoder
+from vocoder import inference as vocoder
+from pydub import AudioSegment
+
+# Load the models
+project_name = "Real-Time-Voice-Cloning"
+encoder.load_model(Path(project_name) / "encoder/saved_models/pretrained.pt")
+synthesizer = Synthesizer(Path(project_name) / "synthesizer/saved_models/pretrained/pretrained.pt")
+vocoder.load_model(Path(project_name) / "vocoder/saved_models/pretrained/pretrained.pt")
+
+def clone_voice(text, reference_audio):
+    # Save the uploaded reference audio
+    audio_path = "reference_audio.wav"
+    reference_audio.export(audio_path, format="wav")
+    
+    # Process the audio to extract embedding
+    audio = encoder.preprocess_wav(audio_path)
+    embedding = encoder.embed_utterance(audio)
+    
+    # Synthesize the new speech
+    specs = synthesizer.synthesize_spectrograms([text], [embedding])
+    generated_wav = vocoder.infer_waveform(specs[0])
+    
+    # Save and return the generated audio
+    output_path = "output.wav"
+    generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant")
+    AudioSegment(generated_wav, frame_rate=synthesizer.sample_rate, sample_width=2, channels=1).export(output_path, format="wav")
+    return output_path
+
+iface = gr.Interface(
+    fn=clone_voice,
+    inputs=[gr.Textbox(label="Text"), gr.Audio(label="Reference Audio", type="file")],
+    outputs=gr.Audio(label="Generated Audio"),
+    title="Real-Time Voice Cloning",
+    description="Generate new speech using a reference audio sample and provided text."
+)
+
+if __name__ == "__main__":
+    iface.launch()