Create app.py

app.py

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+import streamlit as st
+from pydub import AudioSegment
+import os
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import librosa
+import math
+import json
+
+GENRES = ["Metal", "Disco", "Pop", "Classical", "Reggae", "Country", "Rock", "Hiphop", "Jazz", "Blues"]
+
+class CNNModel2(nn.Module):
+    def __init__(self, input_shape, num_classes=10):
+        super(CNNModel2, self).__init__()
+        self.conv1 = nn.Conv2d(input_shape[0], 32, kernel_size=3)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.pool1 = nn.MaxPool2d(3, stride=2, padding=1)
+        self.dropout1 = nn.Dropout(0.2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.pool2 = nn.MaxPool2d(3, stride=2, padding=1)
+        self.dropout2 = nn.Dropout(0.1)
+        self.conv3 = nn.Conv2d(64, 64, kernel_size=2)
+        self.bn3 = nn.BatchNorm2d(64)
+        self.pool3 = nn.MaxPool2d(2, stride=2, padding=1)
+        self.dropout3 = nn.Dropout(0.1)
+        self.flatten_dim = self._calculate_flatten_dim(input_shape)
+        self.fc1 = nn.Linear(self.flatten_dim, 128)
+        self.dropout4 = nn.Dropout(0.5)
+        self.fc2 = nn.Linear(128, num_classes)
+
+    def _calculate_flatten_dim(self, input_shape):
+        x = torch.zeros(1, *input_shape)
+        x = self.pool1(F.relu(self.bn1(self.conv1(x))))
+        x = self.pool2(F.relu(self.bn2(self.conv2(x))))
+        x = self.pool3(F.relu(self.bn3(self.conv3(x))))
+        return x.numel()
+
+    def forward(self, x):
+        x = self.pool1(F.relu(self.bn1(self.conv1(x))))
+        x = self.dropout1(x)
+        x = self.pool2(F.relu(self.bn2(self.conv2(x))))
+        x = self.dropout2(x)
+        x = self.pool3(F.relu(self.bn3(self.conv3(x))))
+        x = self.dropout3(x)
+        x = x.view(-1, self.flatten_dim)
+        x = F.relu(self.fc1(x))
+        x = self.dropout4(x)
+        x = self.fc2(x)
+        return x
+
+def get_mfccs(file, fs=22500, duration=30, n_fft=2048, hop_length=512, n_mfcc=13, num_segments=10):
+    data = {
+        "genre_name": [],
+        "genre_num": [],
+        "mfcc": []
+    }
+    samples_per_track = fs * duration
+    samps_per_segment = int(samples_per_track/num_segments)
+    mfccs_per_segment = math.ceil(samps_per_segment/hop_length)
+    audio, fs = librosa.load(file, sr=fs)
+    for seg in range(num_segments):
+        start_sample = seg * samps_per_segment
+        end_sample = start_sample + samps_per_segment
+        mfcc = librosa.feature.mfcc(y=audio[start_sample:end_sample],
+                                     sr=fs,
+                                     n_fft=n_fft,
+                                     hop_length=hop_length,
+                                     n_mfcc=n_mfcc)
+        mfcc = mfcc.T
+        if len(mfcc) == mfccs_per_segment:
+            data["mfcc"].append(mfcc.tolist())
+    with open('data.json', "w") as filepath:
+        json.dump(data, filepath, indent=4)
+
+    return np.array(data["mfcc"]), np.array(data["genre_name"]), np.array(data["genre_num"])
+
+def cut_audio(input_file):
+    audio = AudioSegment.from_wav(input_file)
+    first_30_seconds = audio[:30 * 1000]
+    output_file = "output_30_seconds.wav"
+    first_30_seconds.export(output_file, format="wav")
+    return output_file
+
+model = CNNModel2((1, 130, 13))
+model.load_state_dict(torch.load("model_cnn2.pth"))
+model.eval()
+
+st.title("Audio Genre Classification")
+st.write("Upload an audio file to classify its genre.")
+uploaded_file = st.file_uploader("Choose an audio file...", type=["wav"])
+
+if uploaded_file is not None:
+    file_path = "temp.wav"
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    file_path = cut_audio(file_path)
+    mfccs, genre_names, genre_nums = get_mfccs(file_path)
+
+    X_to_pred = torch.tensor(mfccs, dtype=torch.float32).unsqueeze(1)
+    predictions = []
+
+    for mfcc in X_to_pred:
+        mfcc = mfcc.unsqueeze(0)
+        with torch.no_grad():
+            output = model(mfcc)
+            _, predicted_class = torch.max(output, 1)
+            predictions.append(predicted_class.item())
+
+    st.write("Prediction:")
+    st.write(GENRES[max(set(predictions), key=predictions.count)])