Update app.py

app.py

@@ -58,9 +58,9 @@ def visualize(hr, lr, recon):
     stft_recon = 2 * np.abs(stft_recon) / np.sum(window)
 
     fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharey=True, sharex=True, figsize=(16, 10))
-    ax1.title.set_text('Target signal')
-    ax2.title.set_text('Lossy signal')
-    ax3.title.set_text('Enhanced signal')
+    ax1.title.set_text('Оригинальный сигнал')
+    ax2.title.set_text('Сигнал с потерями')
+    ax3.title.set_text('Улучшенный сигнал')
 
     canvas = FigureCanvas(fig)
     p = librosa.display.specshow(librosa.amplitude_to_db(stft_hr), ax=ax1, y_axis='linear', x_axis='time', sr=sr)
@@ -72,11 +72,11 @@ packet_size = CONFIG.DATA.EVAL.packet_size
 window = CONFIG.DATA.window_size
 stride = CONFIG.DATA.stride
 
-title = 'Packet Loss Concealment'
+title = 'Сокрытие потерь пакетов'
 st.set_page_config(page_title=title, page_icon=":sound:")
 st.title(title)
 
-st.subheader('Upload audio')
+st.subheader('Загрузить аудио')
 uploaded_file = st.file_uploader("Upload your audio file (.wav) at 48 kHz sampling rate")
 
 is_file_uploaded = uploaded_file is not None
@@ -89,7 +89,7 @@ target = target[:packet_size * (len(target) // packet_size)]
 st.text('Audio sample')
 st.audio(uploaded_file)
 
-st.subheader('Choose expected packet loss rate')
+st.subheader('Выберите желаемый процент потерь')
 slider = [st.slider("Expected loss rate for Markov Chain loss generator", 0, 100, step=1)]
 loss_percent = float(slider[0])/100
 mask_gen = MaskGenerator(is_train=False, probs=[(1 - loss_percent, loss_percent)])
@@ -103,11 +103,11 @@ re_im = torch.stft(lossy_input_tensor, window, stride, window=hann, return_compl
     1).numpy().astype(np.float32)
 session, onnx_model, input_names, output_names = load_model()
 
-if st.button('Conceal lossy audio!'):
-    with st.spinner('Please wait for completion'):
+if st.button('Улучшить'):
+    with st.spinner('Ожидайте...'):
         output = inference(re_im, session, onnx_model, input_names, output_names)
 
-        st.subheader('Visualization')
+        st.subheader('Визуализация')
         fig = visualize(target, lossy_input, output)
         st.pyplot(fig)
     st.success('Done!')