Create app.py

app.py

@@ -0,0 +1,188 @@
+import os
+import numpy as np
+import librosa
+from pydub import AudioSegment
+import soundfile as sf
+import gdown
+from TTS.api import TTS
+from langdetect import detect
+from scipy.spatial.distance import cosine
+import torch
+import matplotlib.pyplot as plt
+import pandas as pd
+import streamlit as st
+from io import BytesIO
+
+# === Utility Functions ===
+def convert_mp3_to_wav(mp3_file, wav_file):
+    audio = AudioSegment.from_file(mp3_file, format="mp3")
+    audio.export(wav_file, format="wav")
+
+def extract_mfcc(wav_file):
+    y, sr = librosa.load(wav_file, sr=None)
+    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    return np.mean(mfcc, axis=1)
+
+def clone_and_compare(tts, ref_wav, text, language, output_wav="cloned.wav"):
+    tts.tts_to_file(text=text, speaker_wav=ref_wav, language=language, file_path=output_wav)
+    orig = extract_mfcc(ref_wav)
+    clone = extract_mfcc(output_wav)
+    similarity = 1 - cosine(orig, clone)
+    return similarity, output_wav
+
+def standardize_audio_format(input_file, output_file, sample_rate=22050):
+    y, sr = librosa.load(input_file, sr=sample_rate)
+    sf.write(output_file, y, sample_rate)
+
+# === Streamlit App ===
+def main():
+    st.title("🎙️ Voice Cloning App")
+    st.write("Clone voices and compare similarity with the original")
+
+    # Initialize TTS model
+    if 'tts' not in st.session_state:
+        with st.spinner("Loading TTS model..."):
+            st.session_state.tts = TTS(
+                model_name="tts_models/multilingual/multi-dataset/your_tts",
+                progress_bar=False,
+                gpu=torch.cuda.is_available()
+            )
+
+    # Input method selection
+    input_method = st.radio(
+        "How do you want to provide the voice/text data?",
+        options=[
+            "Upload audio and text manually",
+            "Enter local paths",
+            "Use Google Drive link",
+            "Upload existing CSV file"
+        ]
+    )
+
+    wav_file = None
+    input_text = None
+    csv_data = None
+
+    if input_method == "Upload audio and text manually":
+        audio_file = st.file_uploader("Upload your audio (MP3) file", type=["mp3"])
+        text_file = st.file_uploader("Upload your text file", type=["txt"])
+        
+        if audio_file and text_file:
+            wav_file = "input.wav"
+            with open("temp.mp3", "wb") as f:
+                f.write(audio_file.getbuffer())
+            convert_mp3_to_wav("temp.mp3", wav_file)
+            
+            input_text = text_file.read().decode("utf-8")
+
+    elif input_method == "Enter local paths":
+        mp3_path = st.text_input("Enter path to your MP3 file")
+        text_path = st.text_input("Enter path to your text file")
+        
+        if mp3_path and text_path:
+            wav_file = mp3_path.replace(".mp3", ".wav")
+            convert_mp3_to_wav(mp3_path, wav_file)
+            
+            with open(text_path, 'r') as file:
+                input_text = file.read()
+
+    elif input_method == "Use Google Drive link":
+        gdrive_url = st.text_input("Enter the Google Drive MP3 link")
+        input_text = st.text_area("Enter the text to be spoken using cloned voice")
+        
+        if gdrive_url and input_text:
+            mp3_file = "input.mp3"
+            wav_file = "input.wav"
+            try:
+                file_id = gdrive_url.split("/d/")[1].split("/")[0]
+                download_url = f"https://drive.google.com/uc?id={file_id}"
+                gdown.download(download_url, mp3_file, quiet=False)
+                convert_mp3_to_wav(mp3_file, wav_file)
+            except Exception as e:
+                st.error(f"Error downloading from Google Drive: {e}")
+
+    elif input_method == "Upload existing CSV file":
+        csv_file = st.file_uploader("Upload your voice_dataset.csv", type=["csv"])
+        if csv_file:
+            csv_data = pd.read_csv(csv_file)
+            st.write("Uploaded CSV data:")
+            st.dataframe(csv_data)
+
+    # Process cloning if we have the required inputs
+    if csv_data is not None:
+        st.success("✅ You uploaded an existing CSV, skipping voice cloning.")
+    elif wav_file and input_text:
+        try:
+            language = detect(input_text)
+            st.write(f"Detected language: {language}")
+
+            if st.button("Start Voice Cloning"):
+                best_similarity = 0
+                best_output = ""
+                results = []
+
+                st.write("🔁 Running 5 cloning attempts for best match...")
+                progress_bar = st.progress(0)
+                
+                for i in range(5):
+                    with st.spinner(f"Running attempt {i+1}/5..."):
+                        sim, out_file = clone_and_compare(
+                            st.session_state.tts, 
+                            wav_file, 
+                            input_text, 
+                            language, 
+                            f"clone_try_{i}.wav"
+                        )
+                    results.append({"Attempt": i + 1, "Similarity": sim})
+                    progress_bar.progress((i+1)/5)
+                    st.write(f"Attempt {i+1}: Similarity = {sim*100:.2f}%")
+
+                    if sim > best_similarity:
+                        best_similarity = sim
+                        best_output = out_file
+
+                # Standardize & Save Final Audio
+                standardize_audio_format(best_output, "final_cloned_voice.wav")
+                st.success(f"✅ Best voice with similarity {best_similarity*100:.2f}%")
+
+                # Save CSV
+                df = pd.DataFrame(results)
+                df.to_csv("voice_dataset.csv", index=False)
+
+                # Plot
+                fig, ax = plt.subplots()
+                ax.plot(df['Attempt'], df['Similarity'] * 100, marker='o')
+                ax.set_title("Voice Similarity Over Attempts")
+                ax.set_xlabel("Attempt")
+                ax.set_ylabel("Similarity (%)")
+                ax.set_ylim(0, 100)
+                ax.grid(True)
+                st.pyplot(fig)
+
+                # Download options
+                st.subheader("📥 Download Results")
+                
+                col1, col2 = st.columns(2)
+                
+                with col1:
+                    with open("voice_dataset.csv", "rb") as f:
+                        st.download_button(
+                            "Download CSV",
+                            f,
+                            file_name="voice_dataset.csv",
+                            mime="text/csv"
+                        )
+                
+                with col2:
+                    with open("final_cloned_voice.wav", "rb") as f:
+                        st.download_button(
+                            "Download Audio",
+                            f,
+                            file_name="final_cloned_voice.wav",
+                            mime="audio/wav"
+                        )
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    main()