Merge branch 'main' of https://gitarero.ecam.fr/guillaume.bonabau/Robotic_Piano_Player

pretrained_models/2stems/.probe

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+OK

pretrained_models/2stems/checkpoint

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+model_checkpoint_path: "model"
+all_model_checkpoint_paths: "model"

pretrained_models/4stems/.probe

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+OK

pretrained_models/4stems/checkpoint

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+model_checkpoint_path: "model"
+all_model_checkpoint_paths: "model"

pretrained_models/5stems/.probe

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+OK

pretrained_models/5stems/checkpoint

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+model_checkpoint_path: "model"
+all_model_checkpoint_paths: "model"

src/Song_Spleeter.py

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+import subprocess
+import os
+import sys
+
+def separate_audio(input_file,stems,output_folder="output"):
+    try:
+        # Create output directory if it doesn't exist
+        os.makedirs(output_folder, exist_ok=True)
+        
+        input_file_name=input_file
+        input_file_name= input_file_name.replace(".mp3","")
+        # Use python -m to ensure the correct module is called
+        command = [
+            sys.executable, "-m", "spleeter", "separate", 
+            "-o", output_folder+"\\"+input_file_name+stems,
+            "-p", "spleeter:"+stems,
+            "C:\\Users\\louis\\Desktop\\VScode\\Python\\Songs\\"+input_file, 
+        ]
+        
+        print(f"Running command: {' '.join(command)}")
+        subprocess.run(command, check=True)
+        print(f"Separation complete! Files saved in {output_folder}/")
+    except subprocess.CalledProcessError as e:
+        print(f"Error running Spleeter: {e}")
+    except Exception as e:
+        print(f"Unexpected error: {e}")
+
+# Example usage
+separate_audio("TheSylversRememberTheRain.mp3" , "4stems")

src/WaveToNotesTests.py

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+import crepe
+from scipy.io import wavfile
+import json
+import csv
+import numpy as np
+
+
+# Define file paths
+fileName = "NujabesFeather4stems/other.wav"
+audio_file = "output/"+fileName
+# Replace the slash with nothing to flatten the path
+flat_name = fileName.replace("/", "")
+# Remove the .wav extension
+without_extension = flat_name[:-4]  # removes last 4 characters (.wav)
+# Create the output filename
+output_csv = "crepe_output" + without_extension + ".csv"
+
+def getNotesFromFreq(freqNotesArray, freqNotesConfidence):
+    notes_json='''{
+  "notes": [
+    {"note": "A", "octave": 0, "frequency": 27.5},
+    {"note": "Bb", "octave": 0, "frequency": 29.135},
+    {"note": "B", "octave": 0, "frequency": 30.868},
+    
+    {"note": "C", "octave": 1, "frequency": 32.703},
+    {"note": "Db", "octave": 1, "frequency": 34.648},
+    {"note": "D", "octave": 1, "frequency": 36.708},
+    {"note": "Eb", "octave": 1, "frequency": 38.891},
+    {"note": "E", "octave": 1, "frequency": 41.203},
+    {"note": "F", "octave": 1, "frequency": 43.654},
+    {"note": "Gb", "octave": 1, "frequency": 46.249},
+    {"note": "G", "octave": 1, "frequency": 48.999},
+    {"note": "Ab", "octave": 1, "frequency": 51.913},
+    {"note": "A", "octave": 1, "frequency": 55.0},
+    {"note": "Bb", "octave": 1, "frequency": 58.27},
+    {"note": "B", "octave": 1, "frequency": 61.735},
+    
+    {"note": "C", "octave": 2, "frequency": 65.406},
+    {"note": "Db", "octave": 2, "frequency": 69.296},
+    {"note": "D", "octave": 2, "frequency": 73.416},
+    {"note": "Eb", "octave": 2, "frequency": 77.782},
+    {"note": "E", "octave": 2, "frequency": 82.407},
+    {"note": "F", "octave": 2, "frequency": 87.307},
+    {"note": "Gb", "octave": 2, "frequency": 92.499},
+    {"note": "G", "octave": 2, "frequency": 97.999},
+    {"note": "Ab", "octave": 2, "frequency": 103.826},
+    {"note": "A", "octave": 2, "frequency": 110.0},
+    {"note": "Bb", "octave": 2, "frequency": 116.541},
+    {"note": "B", "octave": 2, "frequency": 123.471},
+    
+    {"note": "C", "octave": 3, "frequency": 130.813},
+    {"note": "Db", "octave": 3, "frequency": 138.591},
+    {"note": "D", "octave": 3, "frequency": 146.832},
+    {"note": "Eb", "octave": 3, "frequency": 155.563},
+    {"note": "E", "octave": 3, "frequency": 164.814},
+    {"note": "F", "octave": 3, "frequency": 174.614},
+    {"note": "Gb", "octave": 3, "frequency": 184.997},
+    {"note": "G", "octave": 3, "frequency": 195.998},
+    {"note": "Ab", "octave": 3, "frequency": 207.652},
+    {"note": "A", "octave": 3, "frequency": 220.0},
+    {"note": "Bb", "octave": 3, "frequency": 233.082},
+    {"note": "B", "octave": 3, "frequency": 246.942},
+    
+    {"note": "C", "octave": 4, "frequency": 261.626},
+    {"note": "Db", "octave": 4, "frequency": 277.183},
+    {"note": "D", "octave": 4, "frequency": 293.665},
+    {"note": "Eb", "octave": 4, "frequency": 311.127},
+    {"note": "E", "octave": 4, "frequency": 329.628},
+    {"note": "F", "octave": 4, "frequency": 349.228},
+    {"note": "Gb", "octave": 4, "frequency": 369.994},
+    {"note": "G", "octave": 4, "frequency": 391.995},
+    {"note": "Ab", "octave": 4, "frequency": 415.305},
+    {"note": "A", "octave": 4, "frequency": 440.0},
+    {"note": "Bb", "octave": 4, "frequency": 466.164},
+    {"note": "B", "octave": 4, "frequency": 493.883},
+    
+    {"note": "C", "octave": 5, "frequency": 523.251},
+    {"note": "Db", "octave": 5, "frequency": 554.365},
+    {"note": "D", "octave": 5, "frequency": 587.33},
+    {"note": "Eb", "octave": 5, "frequency": 622.254},
+    {"note": "E", "octave": 5, "frequency": 659.255},
+    {"note": "F", "octave": 5, "frequency": 698.456},
+    {"note": "Gb", "octave": 5, "frequency": 739.989},
+    {"note": "G", "octave": 5, "frequency": 783.991},
+    {"note": "Ab", "octave": 5, "frequency": 830.609},
+    {"note": "A", "octave": 5, "frequency": 880.0},
+    {"note": "Bb", "octave": 5, "frequency": 932.328},
+    {"note": "B", "octave": 5, "frequency": 987.767},
+    
+    {"note": "C", "octave": 6, "frequency": 1046.502},
+    {"note": "Db", "octave": 6, "frequency": 1108.731},
+    {"note": "D", "octave": 6, "frequency": 1174.659},
+    {"note": "Eb", "octave": 6, "frequency": 1244.508},
+    {"note": "E", "octave": 6, "frequency": 1318.51},
+    {"note": "F", "octave": 6, "frequency": 1396.913},
+    {"note": "Gb", "octave": 6, "frequency": 1479.978},
+    {"note": "G", "octave": 6, "frequency": 1567.982},
+    {"note": "Ab", "octave": 6, "frequency": 1661.219},
+    {"note": "A", "octave": 6, "frequency": 1760.0},
+    {"note": "Bb", "octave": 6, "frequency": 1864.655},
+    {"note": "B", "octave": 6, "frequency": 1975.533},
+    
+    {"note": "C", "octave": 7, "frequency": 2093.005},
+    {"note": "Db", "octave": 7, "frequency": 2217.461},
+    {"note": "D", "octave": 7, "frequency": 2349.318},
+    {"note": "Eb", "octave": 7, "frequency": 2489.016},
+    {"note": "E", "octave": 7, "frequency": 2637.021},
+    {"note": "F", "octave": 7, "frequency": 2793.826},
+    {"note": "Gb", "octave": 7, "frequency": 2959.955},
+    {"note": "G", "octave": 7, "frequency": 3135.964},
+    {"note": "Ab", "octave": 7, "frequency": 3322.438},
+    {"note": "A", "octave": 7, "frequency": 3520.0},
+    {"note": "Bb", "octave": 7, "frequency": 3729.31},
+    {"note": "B", "octave": 7, "frequency": 3951.066},
+    
+    {"note": "C", "octave": 8, "frequency": 4186.009}
+  ]
+    }'''
+    notes_data = json.loads(notes_json)
+    finalNoteArray = []
+    
+    for i, freq in enumerate(freqNotesArray):
+        bestFreqDif = float('inf')
+        if freqNotesConfidence[i] < 0.5:
+            finalNote = "NoNoteFound"
+        else :
+            for note_data in notes_data['notes']:
+                freqDif = abs(note_data['frequency'] - freq)
+                if freqDif < bestFreqDif:
+                    bestFreqDif = freqDif
+                    finalNote = note_data['note']
+        finalNoteArray.append(finalNote)
+        
+    
+    return finalNoteArray
+
+
+
+
+try:
+    # Try to read existing CSV
+    with open(output_csv, newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        data = list(reader)
+        
+        # Extract frequency and confidence from CSV
+        time = [float(row['time']) for row in data]
+        frequency = [float(row['frequency']) for row in data]
+        confidence = [float(row['confidence']) for row in data]
+        
+        print(f"Loaded {len(frequency)} data points from existing CSV")
+        
+except (FileNotFoundError, KeyError) as e:
+    # If file doesn't exist or has incorrect format, process the audio
+    print(f"Processing audio file: {audio_file}")
+    
+    # Read audio file
+    sr, audio = wavfile.read(audio_file)
+    
+    # Ensure audio is mono (if stereo, take first channel)
+    if len(audio.shape) > 1 and audio.shape[1] > 1:
+        audio = audio[:, 0]
+    
+    # Convert audio to float32 if needed
+    if audio.dtype != np.float32:
+        # Normalize based on data type
+        if audio.dtype == np.int16:
+            audio = audio.astype(np.float32) / 32768.0
+        elif audio.dtype == np.int32:
+            audio = audio.astype(np.float32) / 2147483648.0
+    
+    # Run CREPE pitch detection
+    time, frequency, confidence, activation = crepe.predict(audio, sr, viterbi=True)
+    
+    # Write results to CSV
+    with open(output_csv, 'w', newline='') as csvfile:
+        fieldnames = ['time', 'frequency', 'confidence']
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        
+        writer.writeheader()
+        for i,ArrayCombined in enumerate(zip(time, frequency, confidence)):
+             if confidence[i] >= 0.5:
+                writer.writerow({
+                    'time': ArrayCombined[0],
+                    'frequency': ArrayCombined[1],
+                    'confidence': ArrayCombined[2]
+            })
+    
+    print(f"Saved {len(frequency)} data points to {output_csv}")
+
+# Convert frequencies to notes
+notes = getNotesFromFreq(frequency, confidence)
+
+# Save notes to another CSV file
+notes_csv = "notes_output.csv"
+with open(notes_csv, 'w', newline='') as csvfile:
+    fieldnames = ['time', 'frequency', 'confidence', 'note']
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+    
+    writer.writeheader()
+    for i,ArrayCombined in enumerate(zip(time, frequency, confidence, notes)):
+        if confidence[i] >= 0.5:
+            writer.writerow({
+                'time': ArrayCombined[0],
+                'frequency': ArrayCombined[1],
+                'confidence': ArrayCombined[2],
+                'note': ArrayCombined[3]
+        })
+print(frequency[:30])
+print(f"Saved {len(notes)} notes to {notes_csv}")
+print(f"Sample of notes: {notes[:10]}")