Fix

app.py

@@ -15,11 +15,15 @@ np.random.seed(0)
 from util import print_size, sampling
 from network import CleanUNet
 import torchaudio
+import torchaudio.transforms as T
+
+SAMPLE_RATE = 22050
 
 def load_simple(filename):
-    print(filename)
-    audio, _ = torchaudio.load(filename)
-    return audio
+    wav, sr = torchaudio.load(filename)
+    resampler = T.Resample(sr, SAMPLE_RATE, dtype=wav.dtype)
+    resampled_wav = resampler(audio)
+    return resampled_wav
 
 CONFIG = "configs/DNS-large-full.json"
 CHECKPOINT = "./exp/DNS-large-high/checkpoint/pretrained.pkl"
@@ -65,24 +69,16 @@ def denoise(filename, ckpt_path = CHECKPOINT, out = "out.wav"):
     net.eval()
 
     # inference
-    batch_size = 1000000
     noisy_audio = load_simple(filename)
-    LENGTH = len(noisy_audio[0].squeeze())
-    noisy_audio = torch.chunk(noisy_audio, LENGTH // batch_size + 1, dim=1)
-    all_audio = []
 
     for batch in tqdm(noisy_audio):
         with torch.no_grad():
             generated_audio = sampling(net, batch)
-            generated_audio = generated_audio.cpu().numpy().squeeze()
-            all_audio.append(generated_audio)
-
-    all_audio = np.concatenate(all_audio, axis=0)
-    sf.write(out, np.ravel(all_audio.squeeze()), 32000)
+            generated_audio = generated_audio.cpu()
+            sf.write(out, np.ravel(generated_audio.squeeze()), SAMPLE_RATE)
 
     return out
 
-
 audio = gr.inputs.Audio(label = "Audio to denoise", type = 'filepath')
 inputs = [audio]
 outputs = gr.outputs.Audio(label = "Denoised audio", type = 'filepath')