Upload app.py

app.py

@@ -1,49 +1,10 @@
 import gradio as gr
 import numpy
-import librosa
-from pydub import *
-import fastbook
-from fastbook import *
 import pathlib
-path = Path()
-model = load_learner(path/"thedrum.pkl", cpu=True)
 
-def play(x,y,ti,beat,audio):
-
-  sts=0
-  b=0
-  all=[]
-  countfname=1
-  for i in range(int(ti*beat*2)):  
-    sound = AudioSegment.from_mp3(audio)
-    StrtSec = sts
-    EndSec = beat*(i+1)/2
-    StrtTime = StrtSec*1000
-    EndTime = EndSec*1000
-    extract = sound[StrtTime:EndTime]
-    extract.export("Half.wav", format="wav")
-
-      
-
-    x,y = librosa.load('Half.wav')
-    plt.figure(figsize=(12,4))
-    a = librosa.feature.melspectrogram(y=x,sr=y,n_mels=550)
-    b = librosa.power_to_db(a,ref=np.max)
-    librosa.display.specshow(b,sr=y, x_axis='time', y_axis='mel')
-    plt.savefig(f'{countfname}')
-    wit = model.predict(f'{countfname}.png')
-    all.append(wit[0])
-    countfname+=1
-
-    sts=EndSec
-  return all
-
-def transcribe(audio):
-    x,y = librosa.load(audio)
-    ti = librosa.get_duration(y=x,sr=y)
-    beat = 1
-    text = play(x,y,ti,beat,audio)
-    return text
+def transcribe():
+    
+    return "drum sheet"
 
 gr.Interface(
     fn=transcribe,