no comment

app.py

@@ -1,10 +1,18 @@
 import gradio as gr
 from inference_main import infer
+import wave
 def interference(wav_file, trans=0):
     # determine if wav_file is .wav
     # then, inference
     # return the result
     # if the wav_file is not .wav, inform the user and let user re upload the file
+    
+    # ME: 
+    # if wav_file length > 30s or < 1s, inform the user and let user re upload the file
+    f = wave.open(wav_file, 'rb')
+    time_count = f.getnframes() / f.getframerate()
+    if time_count > 30 or time_count < 1:
+        return None, "Please upload a .wav file with length between 1s and 30s"
     if  not wav_file.endswith('.wav'):
         return None, "Please upload a .wav file"
     return infer(wav_file, trans=[trans]), "Succeed"

results/自己唱的22768bc9e21891ae91a6543283be1b3e614da1d5-0-100_tokaiteio_13.0key.flac

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c3f0dcba263aa3509c526a9fbf75a1457e26eb11e41bacc7f377a46b97dc78d5
-size 1232607
+oid sha256:25c38a8fa2d21362fa331b09fb827230819e96483af3a7e305f68450569c9428
+size 1234204

test.py

@@ -1,17 +0,0 @@
-import gradio as gr
-
-def welcome(name):
-    return f"Welcome to Gradio, {name}!"
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-    """
-    # Hello World!
-    Start typing below to see the output.
-    """)
-    inp = gr.Textbox(placeholder="What is your name?")
-    out = gr.Textbox()
-    inp.change(welcome, inp, out)
-
-if __name__ == "__main__":
-    demo.launch()

utils.py

@@ -262,7 +262,7 @@ def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path)
               'optimizer': optimizer.state_dict(),
               'learning_rate': learning_rate}, checkpoint_path)
 
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
+def clean_checkpoints(path_to_models='vdecoder/hifigan-a/', n_ckpts_to_keep=2, sort_by_time=True):
   """Freeing up space by deleting saved ckpts
 
   Arguments: