added tutorial content

.sessions/johndoe/level.txt

@@ -1 +1 @@
-3
+6

0_Introduction.py

@@ -5,20 +5,34 @@ from utils.login import initialize_login
 initialize_login()
 initialize_level()
 
-LEVEL=0
+LEVEL = 0
 
-def intro_page():
-    st.header("AI Eye Tutorial Template")
-    st.subheader("Level 0: Introduction")
 
-    st.write("""Welcome to the AI Eye Tutorial Template! This template is designed to help you create your own AI Eye tutorial.
-    The template is divided into levels, and each level has a set of tasks that you need to complete before you can move on to the next level.
-    You can use this template to create your own tutorial by completing the tasks in each level and adding your own content. You can also use 
-    this template to learn how to use AI Eye by completing the tasks in each level.""")
+def intro_page():
+    st.header("Speech Recognition 🗣️ ")
+    st.markdown(
+        """
+    Welcome to our Speech Recognition Tutorial! In this tutorial, we will explore the fascinating world of AI (Artificial Intelligence). AI is a field of computer science that focuses on creating machines or systems that can perform tasks that normally require human intelligence. It's like teaching computers to think and understand like humans!
+    """
+    )
+    st.image(
+        "https://i.pinimg.com/originals/42/78/76/42787621ed6d40f0c30f0ae423fc572c.gif",
+        use_column_width=True,
+    )
+    st.markdown(
+        """
+    Have you ever heard of voice assistants like Siri or Alexa? They are great examples of AI in action. Voice assistants can answer questions, play music, or even control smart devices in our homes. AI is also used in recommendation systems that suggest movies or shows we might like to watch. So, you see, AI is becoming a part of our daily lives and can help us in many ways.
+    """
+    )
+    st.image(
+        "https://miro.medium.com/max/1024/1*OkNFPAJZs-giAEKlOZI9HQ.png",
+        use_column_width=True,
+    )
 
-    st.info(f"Current Level: {get_level()}")
+    st.info(f"Click on the button below to start the tutorial!")
 
-    if st.button("Complete"):
+    if st.button("I am Ready!"):
         complete_level(LEVEL)
 
-render_page(intro_page, LEVEL)
+
+render_page(intro_page, LEVEL)

README.md

@@ -1,12 +1,12 @@
 ---
-title: Aieye Tutorial Template
-emoji: 🌍
-colorFrom: green
-colorTo: purple
+title: Speech Recognition
+emoji: 🗣️
+colorFrom: yellow
+colorTo: green
 sdk: streamlit
 sdk_version: 1.21.0
 app_file: 0_Introduction.py
-pinned: false
+pinned: true
 license: openrail
 duplicated_from: aieye/aieye_tutorial_template
 ---

pages/1_History of Speech Recognition.py

@@ -0,0 +1,55 @@
+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+from utils.login import initialize_login
+
+LEVEL = 1
+
+initialize_login()
+initialize_level()
+
+
+def step_page():
+    st.header("History of Speech Recognition")
+    st.markdown(
+        """
+    The history of speech recognition dates back several decades, and it's interesting to see how this technology has evolved over time. Let's take a journey through its key milestones:
+
+    1. Early Attempts
+    In the 1950s and 1960s, scientists began exploring the idea of using computers to understand human speech. One of the first notable systems was IBM's "Audrey" in 1952, which could recognize single spoken digits. However, these early attempts were limited by the technology available at the time, and accurate speech recognition remained a significant challenge."""
+    )
+    st.image(
+        "https://www.beyonddesign.com/wp-content/uploads/2018/09/blog1.jpg.webp",
+        use_column_width=True,
+    )
+    st.markdown(
+        """
+    2. Introduction of Hidden Markov Models (HMMs)
+    In the 1970s and 1980s, researchers made significant progress in speech recognition by adopting Hidden Markov Models (HMMs). HMMs allowed for the modeling of speech sounds and their transitions, enabling more accurate recognition of spoken words and phrases. This breakthrough laid the foundation for many subsequent advancements.
+
+    3. The DARPA Challenge
+    In the 1980s and 1990s, the U.S. Defense Advanced Research Projects Agency (DARPA) initiated a series of speech recognition challenges to promote research in the field. These challenges played a crucial role in advancing the state of speech recognition technology. Notable milestones include the DARPA Speech Understanding Research (SUR) and the Resource Management (RM) evaluations.
+
+    4. The Rise of Neural Networks
+    In the 2000s, neural networks reemerged as a powerful tool for speech recognition. Deep learning, a subfield of machine learning, revolutionized the accuracy and performance of speech recognition systems. Neural networks allowed for the automatic extraction of features from speech data, making it easier to recognize and understand spoken language.
+
+    5. Voice Assistants and Commercial Applications
+    With the widespread adoption of smartphones and the Internet, speech recognition became a fundamental component of voice assistants. Apple's Siri, introduced in 2011, and subsequent voice assistants like Amazon's Alexa and Google Assistant, brought speech recognition to millions of users worldwide. These voice assistants could understand natural language commands, answer questions, and perform tasks based on spoken instructions.
+
+    6. Ongoing Advancements
+    In recent years, advancements in deep learning and the availability of large-scale datasets have further improved speech recognition accuracy. State-of-the-art models, such as OpenAI's Whisper, have achieved remarkable levels of performance, enabling applications in transcription services, call centers, language translation, and more."""
+    )
+    st.image(
+        "https://www.gosmar.eu/machinelearning/wp-content/uploads/2020/05/cnn_img_480-1.png",
+        use_column_width=True,
+    )
+    st.markdown(
+        """The history of speech recognition is a testament to the persistence and innovation of researchers and scientists in overcoming challenges. As technology continues to progress, we can expect further improvements in accuracy, speed, and usability, opening up exciting possibilities for the future of speech recognition."""
+    )
+
+    st.info("Click on the button below to continue!")
+
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+
+render_page(step_page, LEVEL)

pages/1_Step_1.py

@@ -1,15 +0,0 @@
-import streamlit as st
-from utils.levels import complete_level, render_page, initialize_level
-LEVEL = 1
-
-initialize_level()
-
-def step1_page():
-    st.header("Step 1")
-    st.subheader("Level 1: Introduction")
-    st.write("This is the first step of the tutorial. You can add your own content here.")
-
-    if st.button("Complete"):
-        complete_level(LEVEL)
-
-render_page(step1_page, LEVEL)

pages/2_Step_2.py

@@ -1,16 +0,0 @@
-import streamlit as st
-from utils.levels import complete_level, render_page, initialize_level
-
-initialize_level()
-
-LEVEL = 2
-
-def step2_page():
-    st.header("Step 2")
-    st.subheader("Level 2: Introduction")
-    st.write("This is the second step of the tutorial. You can add your own content here.")
-
-    if st.button("Complete"):
-        complete_level(LEVEL)
-
-render_page(step2_page, LEVEL)

pages/2_Uses of Speech Recognition.py

@@ -0,0 +1,48 @@
+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+from utils.login import initialize_login
+
+LEVEL = 2
+
+initialize_login()
+initialize_level()
+
+
+def step_page():
+    st.header("Uses of Speech Recognition")
+    st.markdown(
+        """
+    Speech recognition has found applications in various areas. Let's explore some of them:
+    1. Voice assistants: Voice assistants like Siri, Alexa, or Google Assistant are popular examples of speech recognition technology. They can listen to our voice commands and respond by providing information, playing music, setting reminders, or even telling jokes!"""
+    )
+    st.image(
+        "https://cyfuture.com/blog/ojycekam/2020/05/voice-assistants-transforming-the-lives.jpg",
+        use_column_width=True,
+    )
+    st.markdown(
+        """
+    2. Transcription services: Speech recognition technology is also used to convert spoken words into written text. This makes it easier to create transcriptions of meetings, interviews, or lectures. Instead of typing everything, we can use speech recognition to automatically transcribe what is being said.
+
+    3. Accessibility: Speech recognition plays a crucial role in making technology more accessible to people with disabilities. Individuals who have difficulty using a keyboard or mouse can interact with computers and devices through voice commands. This empowers them to use technology in a way that suits their needs. """
+    )
+    st.image(
+        "data:image/png;base64,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",
+        use_column_width=True,
+    )
+    st.markdown(
+        """
+    4. Call centers: Have you ever wondered how call centers handle a large number of customer calls? Well, speech recognition technology can analyze phone conversations to provide better customer service. It helps in understanding customer needs and routing calls to the appropriate departments. It can also assist with quality assurance by identifying areas where customer service can be improved."""
+    )
+
+    st.image(
+        "https://www.timedoctor.com/blog/images/2021/06/offshore-call-center.jpg",
+        use_column_width=True,
+    )
+
+    st.info("Click on the button below to continue!")
+
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+
+render_page(step_page, LEVEL)

pages/3_How Current(Whisper) Systems Works?.py

@@ -0,0 +1,38 @@
+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+from utils.login import initialize_login
+
+LEVEL = 3
+
+initialize_login()
+initialize_level()
+
+
+def step_page():
+    st.header("How Current(Whisper) Systems Works?")
+    st.markdown(
+        """Now, let's talk about how speech recognition systems like Whisper work. Whisper is a cutting-edge speech recognition system developed by OpenAI. But how does it understand what we say?
+        When we speak, our voices create sound waves. These sound waves are converted into digital signals that computers can understand. Whisper uses advanced machine learning algorithms to process these signals and extract meaningful information."""
+    )
+    st.image(
+        "https://uploads-ssl.webflow.com/614c82ed388d53640613982e/63eb5ebedd3a9a738e22a03f_open%20ai%20whisper.jpg",
+        use_column_width=True,
+    )
+    st.markdown(
+        """
+    It works by analyzing patterns and features in spoken language. The system has been trained on vast amounts of data to recognize words, phrases, and sentences. This training allows Whisper to accurately transcribe speech and understand what we're saying.
+
+    Whisper is constantly improving and learning from new data, which helps it become even better at recognizing speech over time. It's like a clever student who keeps learning and getting smarter with practice!"""
+    )
+    st.image(
+        "https://miro.medium.com/v2/resize:fit:1400/1*ixOo69Ze7IS0TKU1sjrGKA.png",
+        use_column_width=True,
+    )
+
+    st.info("Click on the button below to continue!")
+
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+
+render_page(step_page, LEVEL)

pages/4_Trying Out(Guess the Phrase).py

@@ -0,0 +1,63 @@
+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+from utils.login import initialize_login, get_login
+import requests
+import os
+
+
+def check_sentence_similarity(source_sentence, sentence):
+    response = requests.post(
+        "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2",
+        headers={"Authorization": "Bearer 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"},
+        json={
+            "inputs": {"source_sentence": source_sentence, "sentences": [sentence]},
+        },
+    )
+    return response.json()[0]
+
+
+def get_audio_transcription(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(
+        "https://api-inference.huggingface.co/models/openai/whisper-medium",
+        headers={"Authorization": "Bearer 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"},
+        data=data,
+    )
+    return response.json()
+
+
+LEVEL = 4
+
+initialize_login()
+initialize_level()
+
+
+def step_page():
+    st.header("Tryit Out")
+
+    uploaded_file = st.file_uploader("Upload a file", type=["wav", "mp3", "flac"])
+    if uploaded_file:
+        audio_file = os.path.join(".sessions", get_login()["username"], "audio.flac")
+        with open(audio_file, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+    else:
+        audio_file = "assets/sample1.flac"
+
+    st.audio(audio_file, format="audio/flac")
+
+    transcript = st.text_input("What did you hear?")
+    if st.button("Check") and transcript:
+        with st.spinner("Checking..."):
+            output = get_audio_transcription(audio_file)
+        similarity = check_sentence_similarity(output["text"], transcript)
+        if similarity > 0.75:
+            st.success(
+                f"Correct! You are {similarity * 100}% similar to the original sentence."
+            )
+        else:
+            st.error("You are not similar enough to the original sentence.")
+            st.info(f"Original sentence: {output['text']}")
+
+
+render_page(step_page, LEVEL)

pages/5_Quiz.py

@@ -0,0 +1,62 @@
+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+from utils.login import initialize_login
+import random
+
+LEVEL = 5
+
+initialize_login()
+initialize_level()
+
+questions = [
+    {
+        "question": "Which of the following best describes speech recognition?",
+        "options": [
+            "Teaching computers to understand human speech",
+            "Teaching humans to understand computer languages",
+            "Teaching computers to write code",
+            "Teaching humans to speak multiple languages",
+        ],
+        "answer": "Teaching computers to understand human speech",
+    },
+    {
+        "question": "What is one of the popular applications of speech recognition?",
+        "options": [
+            "Facial recognition",
+            "Virtual reality gaming",
+            "Voice assistants",
+            "Emotion detection",
+        ],
+        "answer": "Voice assistants",
+    },
+]
+if "questions" not in st.session_state:
+    for i in range(len(questions)):
+        random.shuffle(questions[i]["options"])
+    random.shuffle(questions)
+    st.session_state["questions"] = questions
+
+
+def step_page():
+    st.header("Quiz")
+    st.markdown(
+        """Now that you've learned about how speech recognition systems work, let's test your knowledge with a quiz!"""
+    )
+
+    for i in range(len(st.session_state["questions"])):
+        st.subheader(f"Question {i + 1}")
+        question = st.session_state["questions"][i]
+        st.markdown(question["question"])
+        answer = st.radio("Select an answer:", question["options"], key=f"radio{i}")
+
+        if answer == question["answer"]:
+            st.success("Correct!")
+        else:
+            st.error("Incorrect!")
+
+    st.info("Click on the button below to complete the tutorial!")
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+
+render_page(step_page, LEVEL)

pages/{3_Congratulations.py → 6_Congratulations.py}

@@ -3,11 +3,13 @@ from utils.levels import complete_level, render_page, initialize_level
 
 initialize_level()
 
-LEVEL = 3
+LEVEL = 6
+
 
 def step1_page():
     st.header("Congratulations!")
     st.subheader("You have completed the tutorial!")
-    st.write("You can now use this template to create your own tutorial.")
+    st.balloons()
+
 
-render_page(step1_page, LEVEL)
+render_page(step1_page, LEVEL)

utils/levels.py

@@ -2,35 +2,41 @@ import streamlit as st
 from utils.login import get_login
 import os
 
+
 def initialize_level():
-    if 'level' not in st.session_state:
+    if "level" not in st.session_state:
         if get_login()["status"]:
             if not os.path.exists(f".sessions/{get_login()['username']}/level.txt"):
                 with open(f".sessions/{get_login()['username']}/level.txt", "w") as f:
                     f.write("0")
-                st.session_state['level'] = 0
+                st.session_state["level"] = 0
             else:
                 with open(f".sessions/{get_login()['username']}/level.txt", "r") as f:
-                    st.session_state['level'] = int(f.read())
+                    st.session_state["level"] = int(f.read())
+
 
 def get_level():
-    return st.session_state['level']
+    return st.session_state["level"]
+
 
 def render_page(page, level):
     if get_login()["status"]:
-        if st.session_state['level'] < level:
+        if st.session_state["level"] < level:
             st.error(f"You need to complete Level {st.session_state['level']} first!")
         else:
             page()
     else:
         st.error("You need to login first!")
 
+
 def complete_level(level):
-    if st.session_state['level'] > level:
-        st.info(f'You have Already completed Level {level}!')
+    if st.session_state["level"] > level:
+        st.info(f"You have Already completed Level {level}!")
     else:
-        st.session_state['level'] = level + 1
+        st.session_state["level"] = level + 1
         with open(f".sessions/{get_login()['username']}/level.txt", "w") as f:
-            f.write(str(st.session_state['level']))
+            f.write(str(st.session_state["level"]))
         st.balloons()
-        st.success(f'You have completed Level {level}! You can now move on to the next level.')
+        st.success(
+            f"You have completed Level {level}! You can now move on to the next level."
+        )