Update app.py

app.py

@@ -3,7 +3,68 @@ import torchaudio
 from torchaudio.transforms import Resample
 import torch
 import gradio as gr
-def separate(audio_path:str,inst_no:int,progress=gr.Progress(True)):
+from io import BytesIO
+import requests
+
+def yt2audio(search:str,format:str='mp3',audioBitrate='320',videoQuality='720'):
+    return requests.get(requests.post('https://cnv.cx/v2/converter', headers={
+    'Accept': '*/*',
+    'Accept-Language': 'en-GB,en;q=0.9',
+    'Connection': 'keep-alive',
+    'Content-Type': 'application/x-www-form-urlencoded',
+    'DNT': '1',
+    'Origin': 'https://frame.y2meta-uk.com',
+    'Referer': 'https://frame.y2meta-uk.com/',
+    'Sec-Fetch-Dest': 'empty',
+    'Sec-Fetch-Mode': 'cors',
+    'Sec-Fetch-Site': 'cross-site',
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36',
+    'key': requests.get('https://cnv.cx/v2/sanity/key', headers={
+    'Accept': '*/*',
+    'Accept-Language': 'en-GB,en;q=0.9',
+    'Connection': 'keep-alive',
+    'Content-Type': 'application/json',
+    'DNT': '1',
+    'If-None-Match': requests.get('https://cdn.jsdelivr.net/npm/@iframe-resizer/child', headers={
+    'sec-ch-ua-platform': '"Windows"',
+    'Referer': 'https://frame.y2meta-uk.com/',
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36',
+    'sec-ch-ua': '"Google Chrome";v="141", "Not?A_Brand";v="8", "Chromium";v="141"',
+    'DNT': '1',
+    'sec-ch-ua-mobile': '?0',
+}).headers['etag'],
+    'Origin': 'https://frame.y2meta-uk.com',
+    'Referer': 'https://frame.y2meta-uk.com/',
+    'Sec-Fetch-Dest': 'empty',
+    'Sec-Fetch-Mode': 'cors',
+    'Sec-Fetch-Site': 'cross-site',
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36',
+    'sec-ch-ua': '"Google Chrome";v="141", "Not?A_Brand";v="8", "Chromium";v="141"',
+    'sec-ch-ua-mobile': '?0',
+    'sec-ch-ua-platform': '"Windows"',
+},verify=False).json()['key'],
+    'sec-ch-ua': '"Google Chrome";v="141", "Not?A_Brand";v="8", "Chromium";v="141"',
+    'sec-ch-ua-mobile': '?0',
+    'sec-ch-ua-platform': '"Windows"',
+}, data={
+    'link': f'https://youtu.be/{requests.get(f'https://wwd.mp3juice.blog/search.php?q={search}').json()['items'][0]['id']}',
+    'format': format,
+    'audioBitrate': audioBitrate,
+    'videoQuality': videoQuality,
+    'filenameStyle': 'pretty',
+    'vCodec': 'h264',
+},verify=False).json()['url'], headers={
+    'DNT': '1',
+    'Referer': 'https://frame.y2meta-uk.com/',
+    'Upgrade-Insecure-Requests': '1',
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36',
+    'sec-ch-ua': '"Google Chrome";v="141", "Not?A_Brand";v="8", "Chromium";v="141"',
+    'sec-ch-ua-mobile': '?0',
+    'sec-ch-ua-platform': '"Windows"',
+},verify=False).content
+
+
+def separate_audio(audio_path:str,inst_no:int,progress=gr.Progress(True)):
     """
     Separate audio into instrument tracks.
 
@@ -22,10 +83,36 @@ def separate(audio_path:str,inst_no:int,progress=gr.Progress(True)):
         resampler = Resample(sr, target_sr)
         wav = resampler(wav)
         sr = target_sr
-    with torch.no_grad():
+    with torch.inference_mode():
+        results = model.forward(wav)
+    for i in results:
+        torchaudio.save(f"{i}.mp3", results[i], sr)
+    return tuple([i+".mp3" for i in results] + [None for _ in range(5-len(results))])
+
+def separate_audio_by_search(query:str,inst_no:int,progress=gr.Progress(True)):
+    """
+    Searches YouTube for the given text query, downloads the audio,
+    and separates it into multiple instruments (e.g., vocals, drums, bass, etc.).
+
+    Args:
+        query (str): Text query to search on YouTube.
+        inst_no (int): Number of instruments to separate.
+
+    Returns:
+        tuple: Up to 5 MP3 file paths for separated tracks.
+    """
+    
+    model = Splitter(inst_no)
+    wav, sr = torchaudio.load(BytesIO(yt2audio(query)),format="mp3")
+    target_sr = 44100
+    if sr != target_sr:
+        resampler = Resample(sr, target_sr)
+        wav = resampler(wav)
+        sr = target_sr
+    with torch.inference_mode():
         results = model.forward(wav)
     for i in results:
         torchaudio.save(f"{i}.mp3", results[i], sr)
     return tuple([i+".mp3" for i in results] + [None for _ in range(5-len(results))])
 
-gr.Interface(separate, [gr.Audio(type="filepath"),gr.Dropdown([2,4,5],2)], [gr.Audio(type="filepath"), gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath")]).launch(mcp_server=True)
+gr.TabbedInterface([gr.Interface(separate_audio_by_search, [gr.Textbox(),gr.Dropdown([2,4,5])], [gr.Audio(type="filepath"), gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath")]),gr.Interface(separate_audio, [gr.Audio(type="filepath"),gr.Dropdown([2,4,5])], [gr.Audio(type="filepath"), gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath")])],"YT2STEM","Audio2STEM").launch(mcp_server=True)