add app.py

app.py

@@ -1,11 +1,8 @@
-from logging import getLogger
-
 import gradio as gr
 import torch
 import torchaudio
 from denoisers import WaveUNetModel
 
-LOGGER = getLogger(__name__)
 MODEL = WaveUNetModel.from_pretrained("wrice/waveunet-vctk-24khz")
 
 
@@ -14,8 +11,8 @@ def denoise(inputs):
     audio = torch.from_numpy(audio)[None]
     audio = audio / 32768.0
 
-    LOGGER.info(f"Audio shape: {audio.shape}")
-    LOGGER.info(f"Sample rate: {sr}")
+    print(f"Audio shape: {audio.shape}")
+    print(f"Sample rate: {sr}")
 
     if sr != MODEL.config.sample_rate:
         audio = torchaudio.functional.resample(audio, sr, MODEL.config.sample_rate)
@@ -35,7 +32,7 @@ def denoise(inputs):
     denoised = torch.concat(clean)[:, : audio.shape[-1]].squeeze().clamp(-1.0, 1.0)
     denoised = (denoised * 32767.0).numpy().astype("int16")
 
-    LOGGER.info(f"Denoised shape: {denoised.shape}")
+    print(f"Denoised shape: {denoised.shape}")
 
     return MODEL.config.sample_rate, denoised