spectrogramHash.py

import numpy as np
import matplotlib.pyplot as plt
import soundfile as sf
import hashlib
import os


def toDatabase(folder: str) -> list[[dict[str, list[int]]]]: 
 files = [file for file in os.listdir(folder) if file.endswith('.wav')] 
 database: list[[dict[str, list[int]]]] = []

 for file in files: 
 file_path = os.path.join(folder, file) 
 truncated_Hash, spectrogram = spectrogramHash(file_path) 
 entry = {file: {'hash': truncated_Hash, 'spectrogram': spectrogram.tolist()}}
 database.append(entry)
 
 return database

def spectrogramHash(audio_file: str, keyLength: int = 10) -> dict[str, list[int]]:
 data, samplerate = sf.read(audio_file)

 if len(data.shape) > 1:
 data = data.mean(axis=1)
 
 window_size = 1024
 hop_size = 512
 
 n_samples = len(data)
 n_overlap = window_size - hop_size
 n_windows = (n_samples - n_overlap) // hop_size
 
 spectrogram = []
 
 for i in range(n_windows):
 start = i * hop_size
 end = start + window_size
 segment = data[start:end]
 windowed_segment = segment * np.hamming(window_size)
 spectrum = np.abs(np.fft.rfft(windowed_segment, window_size))
 spectrogram.append(spectrum)
 
 spectrogram_array = np.array(spectrogram)
 
 concatenated_hash = b''
 for row in spectrogram_array:
 row_bytes = row.tobytes()
 sha256_hash = hashlib.sha256(row_bytes).hexdigest().encode('utf-8')
 concatenated_hash += sha256_hash
 
 unique_hash = concatenated_hash.hex()
 truncated_Hash = unique_hash[:keyLength]
 
 return truncated_Hash, spectrogram_array

hashAudio = spectrogramHash("./data/1.wav")
print(f"Hash audio is {hashAudio}")

data = './data'
database = toDatabase(data)
print(database[1])