Update app.py

app.py

@@ -1,19 +1,54 @@
 import transformers
 from transformers import pipeline
 import gradio as gr
+import pandas
+import matplotlib.pyplot as plt
+from sklearn import model_selection
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
 
-pipe = pipeline(model="mskov/whisper_esc50")  
 
-def transcribe(audio):
-    text = pipe(audio)["text"]
-    return text
+whisper_esc50 = pipeline(model="mskov/whisper_esc50")  
+whisper_miso= pipeline(model="mskov/whisper_miso")  
+whisper_tiny = whisper.load_model("tiny")
+whisper_base = whisper.load_model("base")
 
-iface = gr.Interface(
-    fn=transcribe, 
-    inputs=gr.Audio(source="microphone", type="filepath"), 
-    outputs="text",
-    title="Model Testing",
-    description="Testing model import.",
-)
+dataset = load_dataset("mskov/miso_test")
 
-iface.launch()
+names = ['path', 'file_name', 'category']
+dataframe = pandas.read_csv(url, names=names)
+array = dataframe.values
+X = array[:,0:2]
+Y = array[:,2]
+# prepare configuration for cross validation test harness
+seed = 7
+# prepare models
+models = [whisper_esc50, whisper_miso, whisper_tiny, whisper_base]
+models.append(('LR', LogisticRegression()))
+models.append(('LDA', LinearDiscriminantAnalysis()))
+models.append(('KNN', KNeighborsClassifier()))
+models.append(('CART', DecisionTreeClassifier()))
+models.append(('NB', GaussianNB()))
+models.append(('SVM', SVC()))
+# evaluate each model in turn
+results = []
+names = []
+scoring = 'accuracy'
+for name, model in models:
+ kfold = model_selection.KFold(n_splits=10, random_state=seed)
+ cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
+ results.append(cv_results)
+ names.append(name)
+ msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
+ print(msg)
+# boxplot algorithm comparison
+fig = plt.figure()
+fig.suptitle('Algorithm Comparison')
+ax = fig.add_subplot(111)
+plt.boxplot(results)
+ax.set_xticklabels(names)
+plt.show()