monngodb1.py

import os
import librosa
import numpy as np
from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')  # Update connection string as needed
db = client['project']
collection = db['tashi']

# Path to the folder containing all the folders with audio files
root_folder = '/home/laibu/Downloads/fma_large'

# Initialize lists to store features for all audio files
all_mfccs = []
all_centroids = []
all_zero_crossing_rates = []

# Define a fixed length for MFCC arrays
max_frames = 500

# Number of files to process per folder
files_per_folder = 350
for folder_name in sorted(os.listdir(root_folder)):  # Iterate over all folders
    folder_path = os.path.join(root_folder, folder_name)

    # Skip if it's not a directory
    if not os.path.isdir(folder_path):
        continue

    # Counter for the number of files processed in the current folder
    files_processed = 0

    # Print the folder path
    print(f"Processing folder: {folder_path}")

    # Loop through each audio file in the current subfolder
    for audio_file in os.listdir(folder_path):
        if audio_file.endswith('.mp3'):
            audio_path = os.path.join(folder_path, audio_file)
            
            # Print the audio file path
            print(f"Processing audio file: {audio_path}")

            try:
                # Load audio file
                y, sr = librosa.load(audio_path)

                # Extract MFCCs
                mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)

                # Pad or truncate MFCCs to ensure fixed length
                if mfccs.shape[1] < max_frames:
                    # Pad with zeros
                    mfccs = np.pad(mfccs, ((0, 0), (0, max_frames - mfccs.shape[1])), mode='constant')
                elif mfccs.shape[1] > max_frames:
                    # Truncate
                    mfccs = mfccs[:, :max_frames]

                # Calculate spectral centroid
                centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
                centroid = centroid[:, :max_frames]  # Truncate to match MFCCs length
                centroid_mean = np.mean(centroid)  # Calculate the mean value of the centroid
                centroid = np.full_like(mfccs, centroid_mean)  # Create an array with the same shape as MFCCs, filled with the centroid mean

                # Calculate zero-crossing rate
                zero_crossing_rate = librosa.feature.zero_crossing_rate(y)
                zero_crossing_rate = zero_crossing_rate[:, :max_frames]  # Truncate to match MFCCs length
                zero_crossing_rate_mean = np.mean(zero_crossing_rate)  # Calculate the mean value of the zero-crossing rate
                zero_crossing_rate = np.full_like(mfccs, zero_crossing_rate_mean)  # Create an array with the same shape as MFCCs, filled with the zero-crossing rate mean

                # Append MFCCs, spectral centroid, and zero-crossing rate to the lists
                all_mfccs.append(mfccs)
                all_centroids.append(centroid)
                all_zero_crossing_rates.append(zero_crossing_rate)

                # Prepare document to insert into MongoDB
                document = {
                    "folder": folder_name,
                    "audio_file": audio_file,
                    "mfccs": mfccs.tolist(),
                    "spectral_centroid": centroid.tolist(),
                    "zero_crossing_rate": zero_crossing_rate.tolist()
                }

                # Insert document into MongoDB collection
                collection.insert_one(document)

                # Increment the counter for files processed in the current folder
                files_processed += 1

                # Break if processed the required number of files per folder
                if files_processed >= files_per_folder:
                    break
            
            except Exception as e:
                print(f"Error processing audio file: {audio_path}")
                print(e)

# Convert the lists of features to numpy arrays
all_mfccs = np.array(all_mfccs)
all_centroids = np.array(all_centroids)
all_zero_crossing_rates = np.array(all_zero_crossing_rates)

# Get the number of frames (columns) in the MFCCs array
num_frames = all_mfccs.shape[2]