Upload 5 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+KNN_age_model filter=lfs diff=lfs merge=lfs -text
+KNN_gender_detection filter=lfs diff=lfs merge=lfs -text

KNN_age_model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60178b35d81a0bb4b4e6efbdb0647a512c7723cee5bc98969b837b3aad700e5a
+size 15619746

KNN_gender_detection

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a5ab450de9fb1fc8e5729300a88653dc15692724f5c5b79e3220932951ff96a7
+size 15619698

app.py

@@ -0,0 +1,62 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+import librosa
+import joblib
+import gradio as gr
+
+## Voice Data Feature Extraction
+
+### extract the features from the audio files using mfcc
+def feature_extracter(fileName):
+    audio,sample_rate = librosa.load(fileName,sr=None, mono=True,  dtype=np.float32,res_type='kaiser_fast')
+    mfcc_features = librosa.feature.mfcc(y=audio,sr=sample_rate,n_mfcc=30)
+    mfccs_scaled_features = np.mean(mfcc_features.T, axis=0)
+    
+    return list(mfccs_scaled_features)
+
+def prediction_age_gender(fileName):
+
+  col_name = ['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4', 'Feature_5','Feature_6', 'Feature_7', 'Feature_8', 'Feature_9', 'Feature_10','Feature_11', 'Feature_12', 'Feature_13', 'Feature_14', 'Feature_15','Feature_16', 'Feature_17', 'Feature_18', 'Feature_19', 'Feature_20','Feature_21', 'Feature_22', 'Feature_23', 'Feature_24', 'Feature_25','Feature_26', 'Feature_27', 'Feature_28', 'Feature_29', 'Feature_30']
+
+  observation = [feature_extracter(fileName)]
+  observation = pd.DataFrame(observation, columns = col_name)
+
+  ## scaling the observation
+  scaler = joblib.load('/content/scaler.pkl')
+  scaled_observation = scaler.transform(observation)
+  scaled_observation = pd.DataFrame(scaled_observation, columns = col_name)  
+
+  ### Gender classification model
+  gender_model = joblib.load('/content/KNN_gender_detection.pkl')
+  gender_predict = gender_model.predict_proba(scaled_observation)
+  ## considering the labels 1 = male 0 = female
+  gender_dict = {}
+  gender_dict['Female'] = gender_predict[0][0]
+  gender_dict['Male'] = gender_predict[0][1]
+
+  ### Age classification model
+  age_model = joblib.load('/content/KNN_age_model.pkl')
+  age_predict = age_model.predict_proba(scaled_observation)
+  age_dict = {}
+  age_dict['Eighties'] = age_predict[0][0]
+  age_dict['Fifties'] = age_predict[0][1]
+  age_dict['Fourties'] = age_predict[0][2]
+  age_dict['Seventies'] = age_predict[0][3]
+  age_dict['Sixties'] = age_predict[0][4]
+  age_dict['Teens'] = age_predict[0][5]
+  age_dict['Thirties'] = age_predict[0][6]
+  age_dict['Twenties'] = age_predict[0][7]
+  age_dict['Other'] = 1 - age_dict['Eighties'] - age_dict['Fifties'] - age_dict['Fourties'] - age_dict['Seventies'] - age_dict['Sixties']  - age_dict['Teens'] - age_dict['Thirties'] - age_dict['Twenties']
+
+  #final = "The person is a: " + gender + " of the age group: " + age
+  return gender_dict, age_dict
+
+demo = gr.Interface(
+    prediction_age_gender,
+    inputs = [gr.Audio(sources=["microphone","upload"], type = 'filepath')],
+    outputs = [gr.Label(num_top_classes=2, label = 'Gender'), gr.Label(num_top_classes=9, label = 'Age Class')],
+    #gr.Text()
+).launch(share=True, debug = True)

requirements.txt

@@ -0,0 +1,419 @@
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