fixed mp3

app.py

@@ -5,10 +5,8 @@ import torch
 import shutil
 import requests
 import gradio as gr
-import soundfile as sf
 from piano_transcription_inference import PianoTranscription, load_audio, sample_rate
 from modelscope import snapshot_download
-from tempfile import NamedTemporaryFile
 from urllib.parse import urlparse
 from convert import midi2xml, xml2abc, xml2mxl, xml2jpg
 
@@ -26,31 +24,12 @@ def clean_cache(cache_dir=CACHE_DIR):
     os.mkdir(cache_dir)
 
 
-def get_audio_file_type(file_path: str):
-    try:
-        with sf.SoundFile(file_path) as audio_file:
-            return "." + str(audio_file.format).lower()
-    except Exception as e:
-        print(f"Error occurred: {e}")
-        return None
-
-
 def download_audio(url: str, save_path: str):
-    with NamedTemporaryFile(delete=False, suffix="_temp") as tmp_file:
-        temp_file_path = tmp_file.name
-        response = requests.get(url, stream=True)
-        if response.status_code == 200:
-            for chunk in response.iter_content(chunk_size=8192):
-                tmp_file.write(chunk)
-
-        else:
-            print(f"Failed to download file: HTTP {response.status_code}")
-            return ""
-
-    ext = get_audio_file_type(temp_file_path)
-    full_path = f"{save_path}{ext}"
-    shutil.move(temp_file_path, full_path)
-    return full_path
+    response = requests.get(url, stream=True)
+    response.raise_for_status()
+    with open(save_path, "wb") as file:
+        for chunk in response.iter_content(chunk_size=8192):
+            file.write(chunk)
 
 
 def is_url(s: str):
@@ -125,7 +104,7 @@ def music163_song_info(id: str):
 def url_infer(song: str):
     clean_cache()
     song_name = ""
-    download_path = f"{CACHE_DIR}/output"
+    download_path = f"{CACHE_DIR}/output.mp3"
     try:
         if is_url(song):
             if "163" in song and "?id=" in song:
@@ -135,7 +114,7 @@ def url_infer(song: str):
                 if not free:
                     raise AttributeError("Unable to parse VIP songs")
 
-            download_path = download_audio(song, download_path)
+            download_audio(song, download_path)
 
         elif song.isdigit():
             song_id = song
@@ -144,7 +123,7 @@ def url_infer(song: str):
             if not free:
                 raise AttributeError("Unable to parse VIP songs")
 
-            download_path = download_audio(song, download_path)
+            download_audio(song, download_path)
 
         midi, title = audio2midi(download_path)
         if song_name:

requirements.txt

@@ -1,7 +1,6 @@
 torch
 pymupdf
 music21
-soundfile
 piano_transcription_inference
 modelscope[framework]==1.18
 librosa==0.9.2