Create app.py

app.py

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+import gradio as gr
+import torch
+from TTS.api import TTS
+import os
+from datetime import datetime
+
+# Function to process text and voice input, then generate speech
+def tts_process(transcript_file, voice_file):
+    # Initialize TTS with your model path
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
+
+    # Read transcript text from uploaded file
+    text = transcript_file.read().decode("utf-8")
+
+    # Generate output file name with timestamp
+    timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
+    output_file_name = f"Download_{timestamp}.wav"
+
+    # Assuming the voice cloning model accepts paths, save files temporarily
+    transcript_path = f"temp_transcript_{timestamp}.txt"
+    voice_path = f"temp_voice_{timestamp}.wav"
+    with open(transcript_path, 'w') as f:
+        f.write(text)
+    with open(voice_path, 'wb') as f:
+        f.write(voice_file.read())
+
+    # Generate speech and save to a file
+    tts.tts_to_file(text=text, speaker_wav=voice_path, language="en", file_path=output_file_name)
+
+    # Cleanup temporary files
+    os.remove(transcript_path)
+    os.remove(voice_path)
+
+    return output_file_name
+
+# Gradio interface setup
+iface = gr.Interface(fn=tts_process,
+                     inputs=[gr.inputs.File(label="Upload Transcript Text File"),
+                             gr.inputs.File(label="Upload Voice File for Cloning")],
+                     outputs=gr.outputs.File(label="Download Speech Output"),
+                     title="TTS Voice Cloning",
+                     description="Upload a transcript text file and a voice file to clone the voice and generate speech.")
+
+# Execute only if run as a script
+if __name__ == "__main__":
+    iface.launch(share=True)