add print more

app.py

@@ -5,7 +5,6 @@ import torch
 import shutil
 import requests
 import gradio as gr
-from tqdm import tqdm
 from piano_transcription_inference import PianoTranscription, load_audio, sample_rate
 from modelscope import snapshot_download
 from urllib.parse import urlparse
@@ -31,7 +30,7 @@ def download_audio(url: str, save_path: str):
         for chunk in response.iter_content(chunk_size=8192):
             file.write(chunk)
 
-    return os.path.abspath(save_path)
+    print(f"{url} has been downloded to {save_path}")
 
 
 def is_url(s: str):
@@ -46,15 +45,16 @@ def is_url(s: str):
 def audio2midi(audio_path: str, cache_dir: str):
     # Load audio
     audio, _ = load_audio(audio_path, sr=sample_rate, mono=True)
-    # Transcriptor
+    print("load_audio OK")
     transcriptor = PianoTranscription(
         device="cuda" if torch.cuda.is_available() else "cpu",
         checkpoint_path=WEIGHTS_PATH,
     )
-    # device: 'cuda' | 'cpu' Transcribe and write out to MIDI file
+    print("PianoTranscription OK")
     midi_path = f"{cache_dir}/output.mid"
     # midi_path = audio_path.replace(audio_path.split(".")[-1], "mid")
     transcriptor.transcribe(audio, midi_path)
+    print("transcribe OK")
     return midi_path, os.path.basename(audio_path).split(".")[-2].capitalize()
 
 
@@ -115,7 +115,7 @@ def url_infer(song: str, cache_dir="./__pycache__/mode2"):
             if not free:
                 raise AttributeError("Unable to parse VIP songs")
 
-            audio_path = download_audio(song_url, audio_path)
+            download_audio(song_url, audio_path)
 
         midi, title = audio2midi(audio_path, cache_dir)
         if song_name: