Update app.py

app.py

@@ -1,57 +1,51 @@
 import streamlit as st
-import librosa
+import numpy as np
+import os
 import soundfile as sf
 from TTS.api import TTS
+import torch
+from io import BytesIO
 
-# Load the pre-trained TTS model
-MODEL_NAME = "tts_models/en/vctk/vits"  # Change this to other models if desired
+# Set up the model for text-to-speech (TTS)
+MODEL_NAME = "tts_models/en/ljspeech/tacotron2-DDC"  # Example TTS model; adjust as needed
 tts = TTS(model_name=MODEL_NAME, progress_bar=True, gpu=False)
 
-# Title and description
-st.title("Voice Cloning Tool")
-st.markdown("""
-Upload a sample of your voice and type text to generate a cloned output. 
-This tool uses a pre-trained voice synthesis model.
-""")
-
-# Step 1: Upload an audio file
-uploaded_file = st.file_uploader("Upload your voice sample (WAV format preferred):", type=["wav", "mp3"])
-if uploaded_file:
-    st.audio(uploaded_file, format="audio/wav", start_time=0)
-
-# Step 2: Enter text for synthesis
-text_input = st.text_area("Enter text to synthesize with the cloned voice:")
-if not text_input:
-    st.warning("Please enter text to generate cloned voice output.")
-
-# Process the audio input (convert to mono WAV)
-def preprocess_audio(file):
-    """Converts audio to mono WAV with a sampling rate of 16kHz."""
-    y, sr = librosa.load(file, sr=16000, mono=True)
-    return y, sr
-
-# Save processed audio for the model
-if uploaded_file:
-    with open("input_audio.wav", "wb") as f:
-        f.write(uploaded_file.read())
-    input_audio_path = "input_audio.wav"
-
-# Step 3: Clone voice and synthesize speech
-if st.button("Clone Voice"):
-    if uploaded_file and text_input:
-        # Process the input audio
-        audio, sr = preprocess_audio(input_audio_path)
-        sf.write("processed_audio.wav", audio, sr)
-
-        # Clone the voice and synthesize speech
+# Function to load audio file
+def load_audio(file):
+    audio_data, sample_rate = sf.read(file)
+    return audio_data, sample_rate
+
+# Function to save the generated audio to a file
+def save_audio(output_audio, sample_rate):
+    output_path = "output_cloned_voice.wav"
+    sf.write(output_path, output_audio, sample_rate)
+    return output_path
+
+# Streamlit app
+def main():
+    st.title("Voice Cloning Tool")
+    st.markdown("Upload a voice input, and get the cloned voice output.")
+
+    # File upload
+    audio_file = st.file_uploader("Upload your audio file", type=["wav", "mp3"])
+    
+    if audio_file is not None:
+        st.audio(audio_file, format="audio/wav")
+        
+        # Load audio file
+        audio_data, sample_rate = load_audio(audio_file)
+
+        # Perform voice cloning (This assumes your TTS model supports some form of input)
         try:
-            output_audio = tts.tts(text=text_input, speaker_wav="processed_audio.wav")
-            output_path = "cloned_output.wav"
-            sf.write(output_path, output_audio, samplerate=16000)
-            st.success("Voice cloning complete! Listen to the output below:")
+            st.text("Processing your input...")
+            output_audio = tts.tts(audio_data)  # Pass the audio to your TTS model for cloning
+            output_path = save_audio(output_audio, sample_rate)
+            
+            # Provide download link
             st.audio(output_path, format="audio/wav")
-            st.download_button("Download Cloned Voice", data=open(output_path, "rb"), file_name="cloned_output.wav")
+            st.markdown(f"[Download Cloned Voice](/{output_path})")
         except Exception as e:
-            st.error(f"Error during voice cloning: {e}")
-    else:
-        st.error("Please upload a voice sample and enter text for synthesis.")
+            st.error(f"Error processing audio: {e}")
+
+if __name__ == "__main__":
+    main()