Create backup.py

backup.py

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+import streamlit as st
+import os
+import time
+import matchering as mg
+from pedalboard import Pedalboard, HighpassFilter, Compressor, Limiter, Reverb, Gain
+from pedalboard.io import AudioFile
+import numpy as np
+from scipy.signal import butter, lfilter
+
+class AudioProcessor:
+    def __init__(self, target_file, reference_file):
+        self.target_file = target_file
+        self.reference_file = reference_file
+        self.mastered_24bit = 'my_song_master_24bit_.wav'
+    
+    def mix_and_generate(self):
+        # Let's keep only warning outputs here, muting everything else
+        mg.log(warning_handler=print)
+
+        # Perform the mixing and generating to 24bit
+        mg.process(
+            target=self.target_file,
+            reference=self.reference_file,
+            results=[
+                mg.pcm16("my_song_master_16bit_.wav"),
+                mg.pcm24(self.mastered_24bit),
+            ],
+            preview_target=mg.pcm16("preview_my_song.flac"),
+            preview_result=mg.pcm16("preview_my_song_master.flac"),
+        )
+        print("Mixing and generating to 24bit completed.")
+    
+    def stereo_widen(self, audio, width=1.2):
+        left_channel = audio[0::2] * width
+        right_channel = audio[1::2] * width
+        widened_audio = np.empty_like(audio)
+        widened_audio[0::2] = left_channel
+        widened_audio[1::2] = right_channel
+        return widened_audio
+
+    def reduce_piano_volume(self, audio, sample_rate, freq_low=200, freq_high=2000, reduction_db=-18):
+        nyquist = 0.5 * sample_rate
+        low = freq_low / nyquist
+        high = freq_high / nyquist
+        b, a = butter(1, [low, high], btype='band')
+        filtered_audio = lfilter(b, a, audio)
+        gain_reduction = 10 ** (reduction_db / 20)
+        reduced_audio = audio - (filtered_audio * gain_reduction)
+        return reduced_audio
+    
+    def determine_parameters(self, audio):
+        avg_volume = np.mean(np.abs(audio))
+        highpass_cutoff = 100 + (avg_volume * 50)
+        compressor_threshold = -20 + (avg_volume * 10)
+        reverb_room_size = min(max(avg_volume, 0.1), 0.5)
+        return highpass_cutoff, compressor_threshold, reverb_room_size
+    
+    def master_audio(self):
+        # Load the mixed and generated 24bit audio file
+        print(f"Loading 24bit audio file: {self.mastered_24bit}")
+        with AudioFile(self.mastered_24bit) as f:
+            audio = f.read(f.frames)
+            sample_rate = f.samplerate
+
+        # Automatically determine parameters
+        highpass_cutoff, compressor_threshold, reverb_room_size = self.determine_parameters(audio)
+        print(f"Determined Parameters - Highpass Cutoff: {highpass_cutoff}, Compressor Threshold: {compressor_threshold}, Reverb Room Size: {reverb_room_size}")
+
+        # Create a pedalboard with the desired effects
+        board = Pedalboard([
+            HighpassFilter(cutoff_frequency_hz=highpass_cutoff),
+            Compressor(threshold_db=compressor_threshold, ratio=4),
+            Limiter(threshold_db=-0.1),
+            Reverb(room_size=reverb_room_size, wet_level=0.2),
+            Gain(gain_db=3),
+        ])
+
+        # Process the audio with the pedalboard
+        processed_audio = board(audio, sample_rate)
+
+        # Apply custom stereo widening
+        processed_audio = self.stereo_widen(processed_audio)
+
+        # Reduce piano volume
+        processed_audio = self.reduce_piano_volume(processed_audio, sample_rate)
+
+        # Save the processed audio to a new file
+        output_file = 'final_mastered_audio.wav'
+        print(f"Saving final mastered audio to: {output_file}")
+        with AudioFile(output_file, 'w', sample_rate, processed_audio.shape[0]) as f:
+            f.write(processed_audio)
+
+        print("Audio mastering completed.")
+        return output_file
+
+# Streamlit app
+st.title('Moonarch Music Analysis: Mixing & Mastering')
+st.header("Experience the Future of Sound")
+st.write("""
+Everything you need to create and release your music, including samples, plugins, unlimited distribution, and the world's best AI mastering engine.
+""")
+
+# Display the Start button
+if st.button("Start using Moonarch"):
+    st.write("Welcome to Moonarch! Let's start creating amazing music.")
+
+# Add upload option
+uploaded_file = st.file_uploader("Upload a song", type=["mp3", "wav"])
+
+if uploaded_file is not None:
+    st.write("File uploaded successfully.")
+    
+    # Placeholder for progress bar
+    progress_bar = st.progress(0)
+    
+    # Save uploaded file to a temporary location
+    audio_file_path = os.path.join(os.getcwd(), f'{uploaded_file.name}')
+    with open(audio_file_path, 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    
+    # Simulate file processing
+    for percent_complete in range(100):
+        time.sleep(0.01)
+        progress_bar.progress(percent_complete + 1)
+    
+    st.write("File processing complete.")
+    
+    # Reference file for mixing
+    reference_file = 'audio_example.mp3'
+
+    # Initialize the processor
+    processor = AudioProcessor(target_file=audio_file_path, reference_file=reference_file)
+
+    if st.button('Master Audio'):
+        with st.spinner('Mixing, generating, and mastering audio...'):
+            processor.mix_and_generate()
+            final_output = processor.master_audio()
+            
+            st.success('Audio mastering complete!')
+            st.audio(final_output)
+            
+            with open(final_output, 'rb') as f:
+                st.download_button(
+                    label="Download Mastered Audio",
+                    data=f,
+                    file_name='final_mastered_audio.wav',
+                    mime='audio/wav'
+                )