app

Audio_Classifier.py

@@ -0,0 +1,26 @@
+import streamlit as st
+from streamlit_chat import message
+from helper import model, upload, record
+
+title = "Emotion Audio Classifier"
+st.title(title)
+
+st.subheader('This Web App allows user to classify Emotion from Audio File')
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: url("https://img.freepik.com/free-vector/white-grid-line-pattern-gray-background_53876-99015.jpg?     w=996&t=st=1667979021~exp=1667979621~hmac=25b2066b6c3abd08a038e86ad9579fe48db944b57f853bd8ee2a00a98f752d7b");  
+             background-attachment: fixed;
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+add_bg_from_url() 
+
+model()

README.md

@@ -1,11 +1,11 @@
 ---
-title: Audio Emotion Classifier
-emoji: 🐨
-colorFrom: pink
-colorTo: yellow
+title: Emotion Audio Classifier
+emoji: ⚡
+colorFrom: blue
+colorTo: gray
 sdk: streamlit
 sdk_version: 1.10.0
-app_file: app.py
+app_file: Audio Classifier.py
 pinned: false
 license: mit
 ---

helper.py

@@ -0,0 +1,281 @@
+import streamlit as st
+from streamlit_chat import message
+from keras.models import load_model
+import os
+import streamlit as st
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import normalize
+import matplotlib.pyplot as plt
+import librosa
+import librosa.display
+import IPython.display as ipd
+import streamlit.components.v1 as components
+from io import BytesIO
+from scipy.io.wavfile import read, write
+from googlesearch import search
+import requests
+from bs4 import BeautifulSoup
+
+classifier = load_model('./model/bestmodel.h5') 
+
+
+def prepare_test(file):
+    max_size=350
+    features=[]     
+    data_ori, sample_rate = librosa.load(file)
+    data, _ = librosa.effects.trim(data_ori)
+    
+    spec_bw = padding(librosa.feature.spectral_bandwidth(y = data, sr= sample_rate), 20, max_size).astype('float32')
+    cent = padding(librosa.feature.spectral_centroid(y = data, sr=sample_rate), 20, max_size).astype('float32')  
+    mfcc = librosa.feature.mfcc(y=data, sr=sample_rate,n_mfcc=20)
+    mfccs = padding(normalize(mfcc, axis=1), 20, max_size).astype('float32')     
+    rms = padding(librosa.feature.rms(y = data),20, max_size).astype('float32')
+    y = librosa.effects.harmonic(data)
+    tonnetz = padding(librosa.feature.tonnetz(y=y, sr=sample_rate,fmin=75),20, max_size).astype('float32')
+    image = padding(librosa.feature.chroma_cens(y = data, sr=sample_rate,fmin=75), 20, max_size).astype('float32') 
+
+    image=np.dstack((image,spec_bw))
+    image=np.dstack((image,cent))
+    image=np.dstack((image,mfccs))
+    image=np.dstack((image,rms))
+    image=np.dstack((image,tonnetz))            
+    features.append(image[np.newaxis,...])  
+    output = np.concatenate(features,axis=0)
+    return output
+
+def padding(array, xx, yy):
+    h = array.shape[0]
+    w = array.shape[1]
+    a = max((xx - h) // 2,0)
+    aa = max(0,xx - a - h)
+    b = max(0,(yy - w) // 2)
+    bb = max(yy - b - w,0)
+    return np.pad(array, pad_width=((a, aa), (b, bb)), mode='constant')
+
+
+def model():
+    select = st.selectbox('Please select one to find out more about the model',
+        ('Choose','Model Training History', 'Confusion Matrix', 'Train-Test Scores'))
+    if select == 'Model Training History':
+        st.header('Train-Val Accuracy History')
+        st.write('Train and Validation accuracy scores are comparable this indicates that the Model is moderately trained.')
+        st.image('./img/bestmodelacc.png')
+
+        st.header('Train-Val Loss History')
+        st.write('The training was stopped with EarlyStopping() when the Validation loss score starts to saturate.')
+        st.image('./img/bestmodelloss.png')
+
+    if select == 'Confusion Matrix':
+        st.header('Confusion Matrix')
+        st.write('Below is the Confusion Matrix for this Model normalized by rows indicating Recall scores which are over 80%.')
+        st.image('./img/bestmodelcm.png')
+
+    if select == 'Train-Test Scores':
+        st.header('Train-Test Accuracy scores')
+        st.write('Train accuracy score is 94% and Test accuracy score is 90%. There is a 4% difference indicating that the model fitted moderately')
+        acc = [0.943, 0.908]
+        loss = [0.177, 0.304]
+        traintest_df = pd.DataFrame(list(zip(acc,loss)),columns=['Accuracy', 'loss'],index =['Train','Test'])
+        st.dataframe(traintest_df.style.format("{:.3}"))
+
+        st.header('Model Precision, Recall, F1-score & MCC')
+        st.write('Macro avg F1 score is 91%.')
+        st.write('Matthew’s correlation coefficient: 0.885') 
+        prec = [0.92, 0.86, 0.91, 0.97, 0.88,0.91]
+        re = [0.92, 0.83, 0.96, 0.92, 0.90, 0.91]
+        f1 = [0.92, 0.85, 0.94, 0.94, 0.89, 0.91]            
+        traintest_df2 = pd.DataFrame(list(zip(prec,re,f1)),columns=['Precision', 'Recall', 'F1-score'],index =['angry', 'happy', 'neutral', 'sad', 'surprise','macro avg'])
+        st.dataframe(traintest_df2.style.format("{:.3}"))
+
+def plot_features(data,sample_rate):
+        fig1, ax1 = plt.subplots(figsize=(6, 2))
+        img = librosa.display.waveshow(y = data, sr=sample_rate, x_axis="time")
+        ax1.set(title = 'Sample Waveform')
+        st.pyplot(plt.gcf())            
+
+        fig2, ax2 = plt.subplots(figsize=(6, 2))
+        cens = librosa.feature.chroma_cens(y = data, sr=sample_rate,fmin=75)
+        img_cens = librosa.display.specshow(cens, y_axis = 'chroma', x_axis='time', ax=ax2)
+        ax2.set(title = 'Chroma_CENS')
+        st.pyplot(plt.gcf())
+
+        fig3, ax3 = plt.subplots(figsize=(6, 2))
+        spec_bw = librosa.feature.spectral_bandwidth(y = data, sr= sample_rate)
+        cent = librosa.feature.spectral_centroid(y = data, sr=sample_rate)
+        times = librosa.times_like(spec_bw)
+        S, phase = librosa.magphase(librosa.stft(y=data))
+        librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max), y_axis='log', x_axis='time', ax=ax3)
+        ax3.fill_between(times, np.maximum(0, cent[0] - spec_bw[0]),
+                        np.minimum(cent[0] + spec_bw[0], sample_rate/2),
+                        alpha=0.5, label='Centroid +- bandwidth')
+        ax3.plot(times, cent[0], label='Spectral centroid', color='w')
+        ax3.legend(loc='lower right')
+        ax3.set(title='log Power spectrogram')  
+        st.pyplot(plt.gcf())
+
+        fig4, ax4 = plt.subplots(figsize=(6, 2))
+        mfcc = librosa.feature.mfcc(y=data, sr=sample_rate,n_mfcc=40)
+        mfccs = normalize(mfcc, axis=1) 
+        img_mfcc = librosa.display.specshow(mfccs, y_axis = 'mel', x_axis='time', ax=ax4)
+        ax4.set(title = 'Sample Mel-Frequency Cepstral Coefficients')    
+        st.pyplot(plt.gcf())
+
+        fig5, ax5 = plt.subplots(figsize=(6, 2))
+        rms = librosa.feature.rms(y=data)
+        times = librosa.times_like(rms)
+        ax5.semilogy(times, rms[0], label='RMS Energy')
+        ax5.set_title(f'RMS Energy')
+        ax5.set(xticks=[])
+        ax5.legend()
+        ax5.label_outer()
+        st.pyplot(plt.gcf())
+
+        fig6, ax6= plt.subplots(figsize=(6, 2))            
+        y = librosa.effects.harmonic(data)
+        tonnetz = librosa.feature.tonnetz(y=y, sr=sample_rate,fmin=75)
+        img_tonnetz = librosa.display.specshow(tonnetz,
+                            y_axis='tonnetz', x_axis='time', ax=ax6)
+        ax6.set(title=f'Tonal Centroids(Tonnetz)')
+        ax6.label_outer()
+        fig6.colorbar(img_tonnetz, ax=ax6)
+        st.pyplot(plt.gcf())
+
+                        
+                        
+def find_definition(emo):
+    word_to_search = emo
+    scrape_url = 'https://www.oxfordlearnersdictionaries.com/definition/english/'  + word_to_search
+
+    headers = {"User-Agent": "mekmek"}
+    web_response = requests.get(scrape_url, headers=headers)
+    
+    if web_response.status_code == 200:
+        soup = BeautifulSoup(web_response.text, 'html.parser')
+
+        try:
+            for sense in soup.find_all('li', class_='sense'):
+                definition = sense.find('span', class_='def').text
+            for example in soup.find_all('ul', class_='examples'):
+                example_1 = example.text.split('.')[0:1]
+        except AttributeError:
+            print('Word not found!!')
+    else:
+        print('Failed to get response...')
+    return definition, example_1
+                           
+def get_search_results(emo):
+    results_lis =[]
+    results = search(f"Understanding {emo}", num_results=1)
+    for result in results:
+        results_lis.append(result)
+    result_1 = results_lis[0]
+    result_2 = results_lis[1]
+    result_3 = results_lis[2] 
+    return result_1, result_2, result_3
+   
+def get_content(emo):
+        definition, example_1 = find_definition(emo)
+        result_1, result_2, result_3 = get_search_results(emo)
+
+
+        with st.expander(f"Word Definition of {emo.capitalize()}"):
+            st.write(definition.capitalize()+'.')
+        with st.expander(f'Example of {emo.capitalize()}'): 
+            with st.container():
+                st.write(f'1) {example_1[0].capitalize()}'+'.')
+
+        with st.expander(f'The following links will help you understand more on {emo.capitalize()}'): 
+            with st.container():
+                st.write(f"Check out this link ➡ {result_1}")
+                st.write(f"Check out this link ➡ {result_2}")
+                st.write(f"Check out this link ➡ {result_3}") 
+
+        if emo == 'anger':
+            with st.expander(f'Video on {emo.capitalize()}'): 
+                with st.container():
+                    st.video('https://www.youtube.com/watch?v=weMeIh10cLs')
+        if emo in 'happy':
+            with st.expander(f'Video on {emo.capitalize()}'): 
+                with st.container():
+                    st.video('https://www.youtube.com/watch?v=FDF2DidUAyY')                        
+        if emo in 'sad':
+            with st.expander(f'Video on {emo.capitalize()}'): 
+                with st.container():
+                    st.video('https://www.youtube.com/watch?v=34rqQEkuhK4')                        
+        if emo in 'suprise':
+            with st.expander(f'Video on {emo.capitalize()}'): 
+                with st.container():
+                    st.video('https://www.youtube.com/watch?v=UYoBi0EssLE')            
+
+        
+def upload():
+    upload_file = st.sidebar.file_uploader('Upload an audio .wav file. Currently max 8 seconds', type=".wav", accept_multiple_files = False)
+
+    if upload_file:
+        st.write('Sample Audio')
+        st.audio(upload_file, format='audio/wav')
+
+        if st.sidebar.button('Show Features'):
+            with st.spinner(f'Showing....'):
+                data_ori, sample_rate = librosa.load(upload_file)
+                data, _ = librosa.effects.trim(data_ori)
+                
+                plot_features(data,sample_rate)
+            st.sidebar.success("Completed")
+
+        if st.sidebar.button('Classify'):
+            with st.spinner(f'Classifying....'):
+                test = prepare_test(upload_file)
+                pred = classifier.predict(test)
+                pred_df = pd.DataFrame(pred.T,index=['anger', 'happy','neutral','sad','suprise'],columns =['Scores'])
+                emo = pred_df[pred_df['Scores'] == pred_df.max().values[0]].index[0]
+                st.info(f'The predicted Emotion: {emo.upper()}')
+            st.sidebar.success("Classification completed")
+            
+            if emo:
+                get_content(emo)
+                
+def record():
+    with st.spinner(f'Recording....'):
+        st.sidebar.write('To start press Start Recording and stop to finish recording')
+        parent_dir = os.path.dirname(os.path.abspath(__file__))
+        build_dir = os.path.join(parent_dir, "st_audiorec/frontend/build")
+        st_audiorec = components.declare_component("st_audiorec", path=build_dir)
+        val = st_audiorec()
+    
+    
+    if isinstance(val, dict):
+        st.sidebar.success("Audio Recorded")
+        ind, val = zip(*val['arr'].items())
+        ind = np.array(ind, dtype=int)
+        val = np.array(val)
+        sorted_ints = val[ind]
+        stream = BytesIO(b"".join([int(v).to_bytes(1, "big") for v in sorted_ints]))
+        wav_bytes = stream.read()
+        rate, data = read(BytesIO(wav_bytes))
+        reversed_data = data[::-1] 
+        bytes_wav = bytes()
+        byte_io = BytesIO(bytes_wav)
+        write(byte_io, rate, reversed_data)
+
+        if st.sidebar.button('Show Features'):
+            with st.spinner(f'Showing....'):
+                data_ori, sample_rate = librosa.load(byte_io)
+                data, _ = librosa.effects.trim(data_ori)
+
+                plot_features(data,sample_rate)
+            st.sidebar.success("Completed")
+
+        if st.sidebar.button('Classify'):
+            with st.spinner(f'Classifying....'):
+                test = prepare_test(byte_io)
+                pred = classifier.predict(test)
+                pred_df = pd.DataFrame(pred.T,index=['anger', 'happy','neutral','sad','suprise'],columns =['Scores'])
+                emo = pred_df[pred_df['Scores'] == pred_df.max().values[0]].index[0]
+                st.info(f'The predicted Emotion: {emo.upper()}')
+            st.sidebar.success("Classification completed")
+                        
+            if emo:
+                get_content(emo)                        
+                        

model/bestmodel.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3a036e70d32322fee44116c4103c95c18ee4bd9219075cb8673743d290d25cf
+size 29266612

pages/.ipynb_checkpoints/Classify by file upload-checkpoint.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from streamlit_chat import message
+from helper import model, upload, record
+
+title = "Emotion Audio Classifier with uploaded file"
+st.title(title)
+
+st.write('Click show features to see the extracted features used for prediction.')
+st.write('Click classify to get predictions.')
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: url("https://img.freepik.com/free-vector/white-grid-line-pattern-gray-background_53876-99015.jpg?     w=996&t=st=1667979021~exp=1667979621~hmac=25b2066b6c3abd08a038e86ad9579fe48db944b57f853bd8ee2a00a98f752d7b");  
+             background-attachment: fixed;
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+add_bg_from_url() 
+
+upload()

pages/.ipynb_checkpoints/Classify by recordings-checkpoint.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from streamlit_chat import message
+from helper import model, upload, record
+
+title = "Emotion Audio Classifier with recordings"
+st.title(title)
+
+st.write('Click show features to see the extracted features used for prediction.')
+st.write('Click classify to get predictions.')
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: url("https://img.freepik.com/free-vector/white-grid-line-pattern-gray-background_53876-99015.jpg?     w=996&t=st=1667979021~exp=1667979621~hmac=25b2066b6c3abd08a038e86ad9579fe48db944b57f853bd8ee2a00a98f752d7b");  
+             background-attachment: fixed;
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+add_bg_from_url() 
+
+record()

pages/Classify by file upload.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from streamlit_chat import message
+from helper import model, upload, record
+
+title = "Emotion Audio Classifier with uploaded file"
+st.title(title)
+
+st.write('Click show features to see the extracted features used for prediction.')
+st.write('Click classify to get predictions.')
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: url("https://img.freepik.com/free-vector/white-grid-line-pattern-gray-background_53876-99015.jpg?     w=996&t=st=1667979021~exp=1667979621~hmac=25b2066b6c3abd08a038e86ad9579fe48db944b57f853bd8ee2a00a98f752d7b");  
+             background-attachment: fixed;
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+add_bg_from_url() 
+
+upload()

pages/Classify by recordings.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from streamlit_chat import message
+from helper import model, upload, record
+
+title = "Emotion Audio Classifier with recordings"
+st.title(title)
+
+st.write('Click show features to see the extracted features used for prediction.')
+st.write('Click classify to get predictions.')
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: url("https://img.freepik.com/free-vector/white-grid-line-pattern-gray-background_53876-99015.jpg?     w=996&t=st=1667979021~exp=1667979621~hmac=25b2066b6c3abd08a038e86ad9579fe48db944b57f853bd8ee2a00a98f752d7b");  
+             background-attachment: fixed;
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+add_bg_from_url() 
+
+record()

requirements(old).txt

@@ -0,0 +1,14 @@
+googlesearch_python==1.1.0
+ipython==8.6.0
+tensorflow==2.10.0
+keras==2.10.0
+librosa==0.9.2
+matplotlib==3.6.0
+numpy==1.22.1
+pandas==1.4.4
+scikit_learn==1.1.3
+soundfile==0.11.0
+scipy==1.7.3
+streamlit==1.14.0
+streamlit_chat==0.0.2.1
+helper==2.5.0

requirements.txt

@@ -0,0 +1,16 @@
+beautifulsoup4==4.9.3
+googlesearch_python==1.1.0
+ipython==8.6.0
+keras==2.10.0
+tensorflow==2.10.0
+librosa==0.9.2
+matplotlib==3.6.0
+numpy==1.22.1
+pandas==1.4.4
+requests==2.25.1
+scikit_learn==1.1.3
+soundfile==0.11.0
+scipy==1.7.3
+streamlit==1.14.0
+streamlit_chat==0.0.2.1
+helper==2.5.0

st_audiorec/.ipynb_checkpoints/__init__-checkpoint.py

@@ -0,0 +1 @@
+

st_audiorec/__init__.py

@@ -0,0 +1 @@
+

st_audiorec/frontend/.env

@@ -0,0 +1,6 @@
+# Run the component's dev server on :3001
+# (The Streamlit dev server already runs on :3000)
+PORT=3001
+
+# Don't automatically open the web browser on `npm run start`.
+BROWSER=none

st_audiorec/frontend/.prettierrc

@@ -0,0 +1,5 @@
+{
+  "endOfLine": "lf",
+  "semi": false,
+  "trailingComma": "es5"
+}

st_audiorec/frontend/build/asset-manifest.json

@@ -0,0 +1,22 @@
+{
+  "files": {
+    "main.js": "./static/js/main.833ba252.chunk.js",
+    "main.js.map": "./static/js/main.833ba252.chunk.js.map",
+    "runtime-main.js": "./static/js/runtime-main.11ec9aca.js",
+    "runtime-main.js.map": "./static/js/runtime-main.11ec9aca.js.map",
+    "static/css/2.bfbf028b.chunk.css": "./static/css/2.bfbf028b.chunk.css",
+    "static/js/2.270b84d8.chunk.js": "./static/js/2.270b84d8.chunk.js",
+    "static/js/2.270b84d8.chunk.js.map": "./static/js/2.270b84d8.chunk.js.map",
+    "index.html": "./index.html",
+    "precache-manifest.4829c060d313d0b0d13d9af3b0180289.js": "./precache-manifest.4829c060d313d0b0d13d9af3b0180289.js",
+    "service-worker.js": "./service-worker.js",
+    "static/css/2.bfbf028b.chunk.css.map": "./static/css/2.bfbf028b.chunk.css.map",
+    "static/js/2.270b84d8.chunk.js.LICENSE.txt": "./static/js/2.270b84d8.chunk.js.LICENSE.txt"
+  },
+  "entrypoints": [
+    "static/js/runtime-main.11ec9aca.js",
+    "static/css/2.bfbf028b.chunk.css",
+    "static/js/2.270b84d8.chunk.js",
+    "static/js/main.833ba252.chunk.js"
+  ]
+}

st_audiorec/frontend/build/index.html

@@ -0,0 +1 @@
+<!doctype html><html lang="en"><head><title>Streamlit Audio Recorder Component</title><meta charset="UTF-8"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Streamlit Audio Recorder Component"/><link rel="stylesheet" href="bootstrap.min.css"/><link rel="stylesheet" href="./styles.css"/><link href="./static/css/2.bfbf028b.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function t(t){for(var n,l,a=t[0],p=t[1],i=t[2],c=0,s=[];c<a.length;c++)l=a[c],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in p)Object.prototype.hasOwnProperty.call(p,n)&&(e[n]=p[n]);for(f&&f(t);s.length;)s.shift()();return u.push.apply(u,i||[]),r()}function r(){for(var e,t=0;t<u.length;t++){for(var r=u[t],n=!0,a=1;a<r.length;a++){var p=r[a];0!==o[p]&&(n=!1)}n&&(u.splice(t--,1),e=l(l.s=r[0]))}return e}var n={},o={1:0},u=[];function l(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,l),r.l=!0,r.exports}l.m=e,l.c=n,l.d=function(e,t,r){l.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},l.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,t){if(1&t&&(e=l(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(l.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)l.d(r,n,function(t){return e[t]}.bind(null,n));return r},l.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(t,"a",t),t},l.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},l.p="./";var a=this.webpackJsonpstreamlit_component_template=this.webpackJsonpstreamlit_component_template||[],p=a.push.bind(a);a.push=t,a=a.slice();for(var i=0;i<a.length;i++)t(a[i]);var f=p;r()}([])</script><script src="./static/js/2.270b84d8.chunk.js"></script><script src="./static/js/main.833ba252.chunk.js"></script></body></html>

st_audiorec/frontend/build/precache-manifest.4829c060d313d0b0d13d9af3b0180289.js

@@ -0,0 +1,26 @@
+self.__precacheManifest = (self.__precacheManifest || []).concat([
+  {
+    "revision": "de27ef444ab2ed520b64cb0c988a478a",
+    "url": "./index.html"
+  },
+  {
+    "revision": "1a47c80c81698454dced",
+    "url": "./static/css/2.bfbf028b.chunk.css"
+  },
+  {
+    "revision": "1a47c80c81698454dced",
+    "url": "./static/js/2.270b84d8.chunk.js"
+  },
+  {
+    "revision": "3fc7fb5bfeeec1534560a2c962e360a7",
+    "url": "./static/js/2.270b84d8.chunk.js.LICENSE.txt"
+  },
+  {
+    "revision": "3478f4c246f37a2cbb97",
+    "url": "./static/js/main.833ba252.chunk.js"
+  },
+  {
+    "revision": "7c26bca7e16783d14d15",
+    "url": "./static/js/runtime-main.11ec9aca.js"
+  }
+]);

st_audiorec/frontend/build/service-worker.js

@@ -0,0 +1,39 @@
+/**
+ * Welcome to your Workbox-powered service worker!
+ *
+ * You'll need to register this file in your web app and you should
+ * disable HTTP caching for this file too.
+ * See https://goo.gl/nhQhGp
+ *
+ * The rest of the code is auto-generated. Please don't update this file
+ * directly; instead, make changes to your Workbox build configuration
+ * and re-run your build process.
+ * See https://goo.gl/2aRDsh
+ */
+
+importScripts("https://storage.googleapis.com/workbox-cdn/releases/4.3.1/workbox-sw.js");
+
+importScripts(
+  "./precache-manifest.4829c060d313d0b0d13d9af3b0180289.js"
+);
+
+self.addEventListener('message', (event) => {
+  if (event.data && event.data.type === 'SKIP_WAITING') {
+    self.skipWaiting();
+  }
+});
+
+workbox.core.clientsClaim();
+
+/**
+ * The workboxSW.precacheAndRoute() method efficiently caches and responds to
+ * requests for URLs in the manifest.
+ * See https://goo.gl/S9QRab
+ */
+self.__precacheManifest = [].concat(self.__precacheManifest || []);
+workbox.precaching.precacheAndRoute(self.__precacheManifest, {});
+
+workbox.routing.registerNavigationRoute(workbox.precaching.getCacheKeyForURL("./index.html"), {
+  
+  blacklist: [/^\/_/,/\/[^/?]+\.[^/]+$/],
+});

st_audiorec/frontend/build/static/css/2.bfbf028b.chunk.css

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+._3ybTi{margin:2em;padding:.5em;border:2px solid #000;font-size:2em;text-align:center}
+/*# sourceMappingURL=2.bfbf028b.chunk.css.map */

st_audiorec/frontend/build/static/css/2.bfbf028b.chunk.css.map

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st_audiorec/frontend/build/static/js/2.270b84d8.chunk.js.LICENSE.txt

@@ -0,0 +1,58 @@
+/*
+object-assign
+(c) Sindre Sorhus
+@license MIT
+*/
+
+/**
+ * @license
+ * Copyright 2018-2021 Streamlit Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/** @license React v0.19.1
+ * scheduler.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v16.13.1
+ * react-is.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v16.14.0
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/** @license React v16.14.0
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */

st_audiorec/frontend/build/static/js/main.833ba252.chunk.js

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+(this.webpackJsonpstreamlit_component_template=this.webpackJsonpstreamlit_component_template||[]).push([[0],{17:function(t,e,a){t.exports=a(28)},28:function(t,e,a){"use strict";a.r(e);var n=a(6),o=a.n(n),r=a(15),c=a.n(r),i=a(0),l=a(1),s=a(2),u=a(3),d=a(8),p=a(11),m=(a(27),function(t){Object(s.a)(a,t);var e=Object(u.a)(a);function a(){var t;Object(l.a)(this,a);for(var n=arguments.length,r=new Array(n),c=0;c<n;c++)r[c]=arguments[c];return(t=e.call.apply(e,[this].concat(r))).state={isFocused:!1,recordState:null,audioDataURL:"",reset:!1},t.render=function(){var e=t.props.theme,a={},n=t.state.recordState;if(e){var r="1px solid ".concat(t.state.isFocused?e.primaryColor:"gray");a.border=r,a.outline=r}return o.a.createElement("span",null,o.a.createElement("div",null,o.a.createElement("button",{id:"record",onClick:t.onClick_start},"Start Recording"),o.a.createElement("button",{id:"stop",onClick:t.onClick_stop},"Stop"),o.a.createElement("button",{id:"reset",onClick:t.onClick_reset},"Reset"),o.a.createElement("button",{id:"continue",onClick:t.onClick_continue},"Download"),o.a.createElement(p.b,{state:n,onStop:t.onStop_audio,type:"audio/wav",backgroundColor:"rgb(255, 255, 255)",foregroundColor:"rgb(255,76,75)",canvasWidth:450,canvasHeight:100}),o.a.createElement("audio",{id:"audio",controls:!0,src:t.state.audioDataURL})))},t.onClick_start=function(){t.setState({reset:!1,audioDataURL:"",recordState:p.a.START}),d.a.setComponentValue("")},t.onClick_stop=function(){t.setState({reset:!1,recordState:p.a.STOP})},t.onClick_reset=function(){t.setState({reset:!0,audioDataURL:"",recordState:p.a.STOP}),d.a.setComponentValue("")},t.onClick_continue=function(){if(""!==t.state.audioDataURL){var e=(new Date).toLocaleString(),a="streamlit_audio_"+(e=(e=(e=e.replace(" ","")).replace(/_/g,"")).replace(",",""))+".wav",n=document.createElement("a");n.style.display="none",n.href=t.state.audioDataURL,n.download=a,document.body.appendChild(n),n.click()}},t.onStop_audio=function(e){!0===t.state.reset?(t.setState({audioDataURL:""}),d.a.setComponentValue("")):(t.setState({audioDataURL:e.url}),fetch(e.url).then((function(t){return t.blob()})).then((function(t){return new Response(t).arrayBuffer()})).then((function(t){d.a.setComponentValue({arr:new Uint8Array(t)})})))},t}return Object(i.a)(a)}(d.b)),f=Object(d.c)(m);d.a.setComponentReady(),d.a.setFrameHeight(),c.a.render(o.a.createElement(o.a.StrictMode,null,o.a.createElement(f,null)),document.getElementById("root"))}},[[17,1,2]]]);
+//# sourceMappingURL=main.833ba252.chunk.js.map

st_audiorec/frontend/build/static/js/main.833ba252.chunk.js.map

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Alternatively, the\n      // theme style is defined in CSS vars.\n      const borderStyling = `1px solid ${\n        this.state.isFocused ? theme.primaryColor : \"gray\"}`\n      style.border = borderStyling\n      style.outline = borderStyling\n    }\n\n    return (\n      <span>\n        <div>\n          <button id='record' onClick={this.onClick_start}>\n            Start Recording\n          </button>\n          <button id='stop' onClick={this.onClick_stop}>\n            Stop\n          </button>\n          <button id='reset' onClick={this.onClick_reset}>\n            Reset\n          </button>\n\n          <button id='continue' onClick={this.onClick_continue}>\n            Download\n          </button>\n\n          <AudioReactRecorder\n            state={recordState}\n            onStop={this.onStop_audio}\n            type='audio/wav'\n            backgroundColor='rgb(255, 255, 255)'\n            foregroundColor='rgb(255,76,75)'\n            canvasWidth={450}\n            canvasHeight={100}\n          />\n\n          <audio\n            id='audio'\n            controls\n            src={this.state.audioDataURL}\n          />\n\n        </div>\n      </span>\n    )\n  }\n\n\n  private onClick_start = () => {\n    this.setState({\n      reset: false,\n      audioDataURL: '',\n      recordState: RecordState.START\n    })\n    Streamlit.setComponentValue('')\n  }\n\n  private onClick_stop = () => {\n    this.setState({\n      reset: false,\n      recordState: RecordState.STOP\n    })\n  }\n\n  private onClick_reset = () => {\n    this.setState({\n      reset: true,\n      audioDataURL: '',\n      recordState: RecordState.STOP\n    })\n    Streamlit.setComponentValue('')\n  }\n\n  private onClick_continue = () => {\n    if (this.state.audioDataURL !== '')\n    {\n      // get datetime string for filename\n      let datetime = new Date().toLocaleString();\n      datetime = datetime.replace(' ', '');\n      datetime = datetime.replace(/_/g, '');\n      datetime = datetime.replace(',', '');\n      var filename = 'streamlit_audio_' + datetime + '.wav';\n\n      // auromatically trigger download\n      const a = document.createElement('a');\n      a.style.display = 'none';\n      a.href = this.state.audioDataURL;\n      a.download = filename;\n      document.body.appendChild(a);\n      a.click();\n    }\n  }\n\n  private onStop_audio = (data) => {\n    if (this.state.reset === true)\n    {\n      this.setState({\n        audioDataURL: ''\n      })\n      Streamlit.setComponentValue('')\n    }else{\n      this.setState({\n        audioDataURL: data.url\n      })\n\n      fetch(data.url).then(function(ctx){\n        return ctx.blob()\n      }).then(function(blob){\n        // converting blob to arrayBuffer, this process step needs to be be improved\n        // this operation's time complexity scales exponentially with audio length\n        return (new Response(blob)).arrayBuffer()\n      }).then(function(buffer){\n        Streamlit.setComponentValue({\n          \"arr\": new Uint8Array(buffer)\n        })\n      })\n\n    }\n\n\n  }\n}\n\n// \"withStreamlitConnection\" is a wrapper function. It bootstraps the\n// connection between your component and the Streamlit app, and handles\n// passing arguments from Python -> Component.\n// You don't need to edit withStreamlitConnection (but you're welcome to!).\nexport default withStreamlitConnection(StAudioRec)\n\n// Tell Streamlit we're ready to start receiving data. We won't get our\n// first RENDER_EVENT until we call this function.\nStreamlit.setComponentReady()\n\n// Finally, tell Streamlit to update our initial height. We omit the\n// `height` parameter here to have it default to our scrollHeight.\nStreamlit.setFrameHeight()\n","import React from \"react\"\nimport ReactDOM from \"react-dom\"\nimport StAudioRec from \"./StreamlitAudioRecorder\"\n\nReactDOM.render(\n  <React.StrictMode>\n    <StAudioRec />\n  </React.StrictMode>,\n  document.getElementById(\"root\")\n)\n"],"sourceRoot":""}

st_audiorec/frontend/build/static/js/runtime-main.11ec9aca.js

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+!function(e){function t(t){for(var n,l,a=t[0],p=t[1],i=t[2],c=0,s=[];c<a.length;c++)l=a[c],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in p)Object.prototype.hasOwnProperty.call(p,n)&&(e[n]=p[n]);for(f&&f(t);s.length;)s.shift()();return u.push.apply(u,i||[]),r()}function r(){for(var e,t=0;t<u.length;t++){for(var r=u[t],n=!0,a=1;a<r.length;a++){var p=r[a];0!==o[p]&&(n=!1)}n&&(u.splice(t--,1),e=l(l.s=r[0]))}return e}var n={},o={1:0},u=[];function l(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,l),r.l=!0,r.exports}l.m=e,l.c=n,l.d=function(e,t,r){l.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},l.r=function(e){"undefined"!==typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,t){if(1&t&&(e=l(e)),8&t)return e;if(4&t&&"object"===typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(l.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)l.d(r,n,function(t){return e[t]}.bind(null,n));return r},l.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(t,"a",t),t},l.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},l.p="./";var a=this.webpackJsonpstreamlit_component_template=this.webpackJsonpstreamlit_component_template||[],p=a.push.bind(a);a.push=t,a=a.slice();for(var i=0;i<a.length;i++)t(a[i]);var f=p;r()}([]);
+//# sourceMappingURL=runtime-main.11ec9aca.js.map

st_audiorec/frontend/build/static/js/runtime-main.11ec9aca.js.map

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st_audiorec/frontend/build/styles.css

@@ -0,0 +1,59 @@
+*{
+    margin: 0;
+    padding: 0;
+}
+
+.container{
+    margin: 0 auto;
+    text-align: center;
+}
+
+.display{
+    width: 100%;
+    padding: 5px 0;
+    margin: 15px 0;
+}
+
+.controllers{
+    width: 100%;
+    padding: 5px 0;
+    margin-top: 15px;
+    margin-bottom: 35px;
+}
+
+button{
+    padding-top: 0.25rem;
+    padding-bottom: 0.25rem;
+    padding-right: 0.75rem;
+    padding-left: 0.75rem;
+    margin-right: 0.5rem;
+    margin-left: 0.1rem;
+    font-size: 16px;
+    background-color: #ffffff;
+    color: #000000;
+    border: 1px solid rgba(49, 51, 63, 0.2);
+    border-radius: 0.25rem;
+    margin-top: 0.75rem;
+    margin-bottom: 0.25rem;
+}
+
+button:hover{
+    padding-top: 0.25rem;
+    padding-bottom: 0.25rem;
+    padding-right: 0.75rem;
+    padding-left: 0.75rem;
+    margin-right: 0.5rem;
+    margin-left: 0.1rem;
+    font-size: 16px;
+    background-color: #ffffff;
+    color: #ff4c4b;
+    border: 1px solid #ff4c4b;
+    border-radius: 0.25rem;
+    margin-top: 0.75rem;
+    margin-bottom: 0.25rem;
+}
+
+audio {
+  width: 450px;
+  height: 45px;
+}

st_audiorec/frontend/node_modules/.bin/acorn

@@ -0,0 +1 @@
+../acorn/bin/acorn

st_audiorec/frontend/node_modules/.bin/ansi-html

@@ -0,0 +1 @@
+../ansi-html/bin/ansi-html

st_audiorec/frontend/node_modules/.bin/arrow2csv

@@ -0,0 +1 @@
+../apache-arrow/bin/arrow2csv.js

st_audiorec/frontend/node_modules/.bin/atob

@@ -0,0 +1 @@
+../atob/bin/atob.js

st_audiorec/frontend/node_modules/.bin/autoprefixer

@@ -0,0 +1 @@
+../autoprefixer/bin/autoprefixer

st_audiorec/frontend/node_modules/.bin/babylon

@@ -0,0 +1 @@
+../babylon/bin/babylon.js

st_audiorec/frontend/node_modules/.bin/browserslist

@@ -0,0 +1 @@
+../browserslist/cli.js

st_audiorec/frontend/node_modules/.bin/css-blank-pseudo

@@ -0,0 +1 @@
+../css-blank-pseudo/cli.js

st_audiorec/frontend/node_modules/.bin/css-has-pseudo

@@ -0,0 +1 @@
+../css-has-pseudo/cli.js

st_audiorec/frontend/node_modules/.bin/css-prefers-color-scheme

@@ -0,0 +1 @@
+../css-prefers-color-scheme/cli.js

st_audiorec/frontend/node_modules/.bin/cssesc

@@ -0,0 +1 @@
+../cssesc/bin/cssesc

st_audiorec/frontend/node_modules/.bin/detect

@@ -0,0 +1 @@
+../detect-port-alt/bin/detect-port

st_audiorec/frontend/node_modules/.bin/detect-port

@@ -0,0 +1 @@
+../detect-port-alt/bin/detect-port

st_audiorec/frontend/node_modules/.bin/errno

@@ -0,0 +1 @@
+../errno/cli.js