VocaliD creates custom vocal persona for text-to-speech applications to make communication more human

VocaliD

vocalid

It will come as no surprise to those of you reading this (thanks!), that the interest and applications of ‘ ’ is growing rapidly. Amazon Alexa and there is talk around for the next 10,000. I won’t be diving into specifics and discussing issues like . That is not what this post is about. Voice-Assistants recently hit 10,000 skills what will drive adoption retention There are lots of resources & tools, tons of cool products, frameworks, articles and people out there to help you learn about the landscape. 💫 Here are some that I find particularly useful 💫 This post is also on GitHub and open for suggestions, just submit a pull request by VoiceLabs The 2017 Voice Report 🤔 How To Use This : There is very little on the front focusing on Voice-Assistants. does the best one IMO. 📰 News newsletter Matt Hartman ( I’m thinking of starting one too —if you’re interested, sign up below!) : Always interesting to see the companies operating in these spaces. They are broken down into different categories so you can easily jump into ‘Building’, ‘Products’, ‘Analytics’ etc. If you think there are some that need adding, 🍜 Startups tell me here . This is just to outline some companies/funds that are very focused in the space right now. Again, if I’ve missed some worthwhile, 🤑 Funding: tell me here . These are some of the awesome people I follow and have come across. I’ve linked them all, you should check them out. If I’ve missed some worthwhile, you know to 👥 People & Posts: tell me here . Enjoy 😊! 🚀 I’ve launched something: Skills.wtf — A site to find the best Alexa Skills 🚀Also, I’ve added a little note about at the end of this post. A community helping facilitate connections to; an experiment I’m working on - Get a skill made/make skills for people/companies- Find investors active in the field- Intros to other active enthusiasts- Bounce ideas around with others- Advice & mentorship from experienced individuals- Resources and tools to help you build- Discover interesting products and companies 📰 News 🗞 Newsletters _Hearing Voices - This week's news about voice computing apps, delivered directly to your inbox. ..._www.getrevue.co Hearing Voices - Revue The Official Amazon Alexa Developer Newsletter _You have total control over when you want Google Home to respond to your voice commands. Its light states tell you the…_madeby.google.com Made by Google (General Google news + Google Home. Subscribe at bottom of page) 🖥 Websites/Sources _Read writing about Alexa. Every day, thousands of voices read, write, and share important stories on Medium about Alexa…_medium.com Alexa - Medium _Read writing about Voice Assistant. Every day, thousands of voices read, write, and share important stories on Medium…_medium.com Voice Assistant - Medium _Read writing about Amazon Echo. Every day, thousands of voices read, write, and share important stories on Medium about…_medium.com Amazon Echo - Medium _Read writing about Google Home. Every day, thousands of voices read, write, and share important stories on Medium about…_medium.com Google Home - Medium _Read writing about Siri. Every day, thousands of voices read, write, and share important stories on Medium about Siri._medium.com Siri - Medium _Read writing about Voice. Every day, thousands of voices read, write, and share important stories on Medium about Voice…_medium.com Voice - Medium venturebeat.com VoiceAssistants_on_VentureBeat _The brain behind Amazon's voice powered devices like Echo, FireTV etc._www.reddit.com Amazon Alexa * /r/alexa _r/AmazonEcho is a community centered around the Amazon Echo, or as we like to call her - Alexa. Alexa is designed…_www.reddit.com Amazon Echo | A voice command system that brings the Internet Of Things to your home * /r… _Everything Google Home: tips, bugs, new features, requests, etc._www.reddit.com Everything Google Home * /r/googlehome _WIRED is where tomorrow is realized_www.wired.com VoiceAssistant _Crunch Network Even before smartwatches, I did not wear a watch. I did not buy an Apple Watch or Pebble, despite the…_techcrunch.com Alexa | TechCrunch _Google Home is Google's answer to Amazon line's of voice-powered Echo devices. After announcing Home at its I/O…_techcrunch.com google home | TechCrunch _Learn about Voice applications, Amazon Alexa and Google Assistant innovation. A hands-free world is coming, join in!_voicelabs.co Blog _multiplex-past, present, future technology research + insights ☂️_readmultiplex.com @ReadMultiplex _Read the latest Amazon Alexa and Google Assistant news, talking about the newest developments in the Voice-First…_voicelabs.co Press Coverage - Voice-First Industry 📜 General news in space (not Voice specific) _Bringing you the latest in Artificial Intelligence, Robotics, and Neurotechnology_inside.com Technically Sentient _Everything about chatbots. From the makers of BotList_botpublication.com BotPublication _Connecting Technology to the Masses_gomainstrm.com GoMainstrm _Chatbots, AI, NLP, Facebook Messenger, Slack, Telegram, and more._chatbotsmagazine.com Chatbots Magazine _Edit description_tinyletter.com Locutius - Letters on Conversational Interfaces by Abi Jones _The O'Reilly Bots Podcast covers advances in conversational user interfaces, artificial intelligence, and messaging…_www.oreilly.com O'Reilly Bots Podcast ideas and resources. — curated by the team Messaging Weekly Smooch — Curated by and the team Chatbots Weekly Omar Pera Reply.ai 🍜 Startups 🗣 Platforms _Actions on Google lets developers build experiences for the Google Assistant._developers.google.com Actions on Google | Actions on Google | Google Developers _Find companies with expertise in designing, developing and optimizing Alexa skills, from agencies and development to…_developer.amazon.com Find Experienced Skill Builders and Tools _I'm open-source, which means you can grab my smarts and put it in a device of your own design. Voice enable your fridge…_mycroft.ai Home - Mycroft _Cortana is your personal assistant, always ready to help you in every situation. See what the world's most personal…_www.microsoft.com Cortana - Meet your personal assistant - Microsoft - Global _Siri on Apple iPhone, iPad and iPod touch lets you use your voice to send messages, make calls, set reminders and more._www.apple.com iOS - Siri - Apple (UK) _Viv is an artificial intelligence platform that enables developers to create an intelligent, conversational interface…_viv.ai Viv 👔 Companies Capito Systems Cognitive Code — Silvia Mindmeld Nuance Verbio ( ) Raven Tech Acq by Baidu Mobvoi ☁️ Cloud Services Amazon Lex Amazon Polly Google Cloud Platform Microsoft Cognitive Services 🛠 Build - SDKs & Frameworks Assist Alexa Skills Kit Pullstring Conversable Bespoken Tools Api.ai for Google Assistant Api.ai for Amazon Alexa Kitt.ai Sayspring Houndify Smartly.ai Gomix IFTTT Mosaic - Extras This post by is a great place to get started: Brian Donohue _Last night betaworks and Amazon hosted the first NYC Amazon Alexa Meetup to bring Alexa owners and enthusiasts together…_medium.com Build your First Alexa Skill AlexaTutorial How to Build your First Amazon Alexa Skill (eBook) by Rocket Insights _Build your first custom skill in just 10 minutes (no programming experience required)._iot-for-all.com Alexa, What’s for Lunch? _At StdLib (“Standard Library”) we allow developers to deploy functions written in Node.js as infinitely scalable API…_hackernoon.com Build an Alexa Skill in 7 Minutes Flat with Node.js and StdLib _Simple builder for Alexa replies._www.npmjs.com alexa-message-builder 💼 Agencies Xandra Assist Rain Mobiquity Matchbox Solstice SciFutures VaynerMedia Witlingo, Inc. AKQA Razorfish Giant Spoon Iconmobile Newscycle Solutions Blink Mindshare TribalScale Volara Vectorform XAPPmedia, Inc. Unified Computer Intelligence Corporation UI Centric Opearlo VocaliD Conversant Labs Future of Voice 📈 Analytics VoiceLabs.co Opearlo Analytics Witlingo, Inc dashbot Botanalytics 🎙 Products Alexa Skills Store App on and iOS Android Unofficial Alexa Skills Store — list of commands that you can ask Google’s voice assistant OK Google Finie 🤖 Hardware Nucleus Jam Voice Ivee Jibo Whyd Kuri Lynx Hello Egg Triby IO Petnet TrackR Musaic Scout Alarm Garageio Toymail ecobee Luma ring Thalmic Labs embodied Owlet Vesper 🤑 Fundraising Alexa Fund Alexa Accelerator, powered by Techstars Lightspeed Ventures Launch Capital NextView Ventures — Betaworks Get Support from the Alexa Team at Betaworks’ Voicecamp _voicecamp is a program for anyone building within the ecosystem of conversational interfaces._betaworks.com voicecamp 💵 Alexa Fund (All the companies announced) Sutro invoxia Petnet Rachio mojio TrackR Musaic Scout Alarm Garageio Toymail Dragon Innovation Kitt.ai ecobee Luma ring Nucleus Defined Crowd Vesper 👥 People & Posts — General Partner . M.G. Siegler GV On Twitter _Vocal computing has a shot at being the next platform — or more — if the big guys don’t screw it up_500ish.com The Voice _“Voice will never be the interaction model,” they say, out loud, to communicate their thoughts._500ish.com Computing in Concert - partner at + runs . Matt Hartman betaworks Hearing Voices Newsletter On Twitter _The rise of services that live in Chatbots, Voice Computing, & Mixed Reality_render.betaworks.com Interfaces On Demand — people-first investor . Sarah Guo Greylock Partners On Twitter _Part 3 in a series on the Conversational Economy_news.greylock.com The Conversational Economy — Voice and the New Era of Multi-Modal Computing — Reporter at TechCrunch. Sarah Perez On Twitter _The next mainstream computing platform may not be bots, AR, or VR, but rather voice-based computing. According to a new…_techcrunch.com 24.5M voice-first devices expected to ship this year, but apps struggle to retain users — vp at . Patrick Montague betaworks On Twitter _Following the success of 2016’s botcamp, betaworks is pleased to announce camps as an ongoing product line. Each year…_render.betaworks.com Announcing Voicecamp — Freelance writer + developer. Owen Williams On Twitter _For decades the technology industry has been chasing the holy grail of computing: natural language conversation. Humans…_medium.com Google Home changed the way I think about voice assistants — CEO Assist. Shane Mac On Twitter _We are excited to announce that the Assist platform is powering one of the first brands on Google Home, Lonely Planet._medium.com Talk to Lonely Planet on Google Home — Senior Product Manager, Intercom. Hugh Durkin On Twitter _When consumer internet arrived in the 1990s, it ushered in a new age of communication. Free internet chat applications…_blog.intercom.com Why voice will be the dominant chatbot interface - Inside Intercom — Partner, . Aaron Batalion Lightspeed On Twitter _When I was a kid, my computer had a SoundBlaster card. If you were a geek like me, you remember editing your autoexec…_medium.com Skate to where the voice is going… — UX / product design . Johna Paolino General Assembly On Twitter _Two Simple User Experience Design Gestures that Delighted a Female User_medium.com Google Home vs Alexa — Trying to build a better company, every day and . Adam Marchick @VoiceLabsCo Kahuna — Co-Founder / EIR @ The Chernin Group. Alexandre Linares @VoiceLabsCo On Twitter _The Voice Report starts with the underlying hardware devices and works its way up the voice-first stack_read.acloud.guru The 2017 Voice Report — CEO and CoFounder, PullString. Prev, Pixar. Oren Jacob On Twitter — Founder + Editor at . Brian Roemmele Multiplex On Twitter _During the early computer era of the 1960s, it was thought there would only be the need for a few dozen computers. By…_techpinions.com There is A Revolution Ahead and It Has A Voice _“Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans”–Apple…_medium.com Apple’s Secret AI Technology: Meet Vocal IQ the self learning technology that is a part of Siri2. — Invented the hashtag; X Uber; Xoogler. Chris Messina On Twitter — Director of User Experience . Author of the O’Reilly book Designing Voice User Interfaces. Cathy Pearl @Sense_ly On Twitter _Edit description_www.amazon.com Designing Voice User Interfaces: Principles of Conversational Experiences — Strategically-oriented product design leader . Trip ODell @Amazon On Twitter _Several times a week, my children host an evening dance party in the living room. The playlist features all the modern…_medium.com Velveteen Rabbits: What a Children’s Story Can Teach Us About Computers that Talk. — Co-founder & Partner at NextView Ventures. David Beisel On Twitter _In the three months since I penned my initial blog post on "Ubiquity of Voice and the Venture Opportunity," I've been…_genuinevc.co Charting the Personal Voice Computing Startup Roadmap - GenuineVC Nithya Thadani — CEO of Rain _Last October, New York-based digital agency Rain worked with Campbell's to find a new way to push out the CPG giant's…_www.adweek.com How This Agency Built Its Own App Using Amazon Echo —I’m an Innovation-Obsessed Founder at Mylestone. Dave Balter On Twitter _Why Voice Assistants may be the answer you’ve been looking for._medium.com “Alexa, How Can I Get Unfair Ecosystem Advantage” — Partner at Lightspeed Venture Partners. Jeremy Liew On Twitter _Games have been a killer app for just about every new platform, from PCs to TVs (via game consoles) to Facebook to…_medium.com Games for Alexa — Co-Founder/CEO Siri & Viv Labs. Dag Kittlaus On Twitter _Why Viv is Joining Forces with Samsung_elevate.samsungnext.com The Path to Intelligence Everywhere — Co-founder of . Jan König HashtagNow On Twitter _Thoughts on Designing Seamless Voice Experiences_medium.com Alexa, Please Send This To My Screen 🚀 - A community helping facilitate connections to; I am working on an experiment - Get a skill made/make skills for people/companies- Find investors active in the field- Intros to other active enthusiasts- Bounce ideas around with others- Advice & mentorship from experienced individuals- Resources and tools to help you build- Discover interesting products and companies 👍 Please tap “♥︎” if you found this useful to help others see it too 👾 This post is also on GitHub and open for suggestions, just submit a pull request 👉 Feel free to follow and reach out on Twitter 🙏 Thanks to everyone who looked at this draft very early on! And perhaps I’ll see you all in the newsletter or 😉 Build Voices Side Note: I have ‘borrowed’ this template from which is an awesome post & newsletter I use to learn about Machine Learning. Thanks The Non-Technical Guide to Machine Learning & Artificial Intelligence Sam DeBrule 🙌

Ultimate Guide to Voice Assistants

After my latest post about how to build your own RAG and run it locally, today, we're taking it a step further by not only implementing the conversational abilities of large language models but also adding listening and speaking capabilities. The idea is straightforward: we are going to create a voice assistant reminiscent of Jarvis or Friday from the iconic Iron Man movies, which can operate offline on your computer. Since this is an introductory tutorial, I will implement it in Python and keep it simple enough for beginners. Lastly, I will provide some guidance on how to scale the application. Techstack First, you should set up a virtual Python environment. You have several options for this, including pyenv, virtualenv, poetry, and others that serve a similar purpose. Personally, I'll use Poetry for this tutorial due to my personal preferences. Here are several crucial libraries you'll need to install: : For a visually appealing console output. rich : A robust tool for speech-to-text conversion. openai-whisper : A cutting-edge library for text-to-speech synthesis, ensuring high-quality audio output. suno-bark : A straightforward library for interfacing with Large Language Models (LLMs). langchain , , and : Essential for audio recording and playback. sounddevice pyaudio speechrecognition For a detailed list of dependencies, refer to the link . here The most critical component here is the Large Language Model (LLM) backend, for which we will use Ollama. is widely recognized as a popular tool for running and serving LLMs offline. If Ollama is new to you, I recommend checking out my previous article on offline RAG: Basically, you just need to download the Ollama application, pull your preferred model, and run it. Ollama "Build Your Own RAG and Run It Locally: Langchain + Ollama + Streamlit." Architecture Okay, if everything has been set up, let's proceed to the next step. Below is the overall architecture of our application, which fundamentally comprises 3 main components: : Utilizing , we convert spoken language into text. Whisper's training on diverse datasets ensures its proficiency across various languages and dialects. Speech Recognition OpenAI's Whisper : For the conversational capabilities, we'll employ the Langchain interface for the model, which is served using Ollama. This setup promises a seamless and engaging conversational flow. Conversational Chain Llama-2 : The transformation of text to speech is achieved through , a state-of-the-art model from Suno AI, renowned for its lifelike speech production. Speech Synthesizer Bark The workflow is straightforward: record speech, transcribe to text, generate a response using an LLM, and vocalize the response using Bark. Sequence diagram for voice assistant with Whisper, Ollama, and Bark. Implementation The implementation begins with crafting a based on Bark, incorporating methods for synthesizing speech from text and handling longer text inputs seamlessly as follows: TextToSpeechService import nltk
import torch
import warnings
import numpy as np
from transformers import AutoProcessor, BarkModel

warnings.filterwarnings(
    "ignore",
    message="torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.",
)


class TextToSpeechService:
    def __init__(self, device: str = "cuda" if torch.cuda.is_available() else "cpu"):
        """
        Initializes the TextToSpeechService class.
        Args:
            device (str, optional): The device to be used for the model, either "cuda" if a GPU is available or "cpu".
            Defaults to "cuda" if available, otherwise "cpu".
        """
        self.device = device
        self.processor = AutoProcessor.from_pretrained("suno/bark-small")
        self.model = BarkModel.from_pretrained("suno/bark-small")
        self.model.to(self.device)

    def synthesize(self, text: str, voice_preset: str = "v2/en_speaker_1"):
        """
        Synthesizes audio from the given text using the specified voice preset.
        Args:
            text (str): The input text to be synthesized.
            voice_preset (str, optional): The voice preset to be used for the synthesis. Defaults to "v2/en_speaker_1".
        Returns:
            tuple: A tuple containing the sample rate and the generated audio array.
        """
        inputs = self.processor(text, voice_preset=voice_preset, return_tensors="pt")
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with torch.no_grad():
            audio_array = self.model.generate(**inputs, pad_token_id=10000)

        audio_array = audio_array.cpu().numpy().squeeze()
        sample_rate = self.model.generation_config.sample_rate
        return sample_rate, audio_array

    def long_form_synthesize(self, text: str, voice_preset: str = "v2/en_speaker_1"):
        """
        Synthesizes audio from the given long-form text using the specified voice preset.
        Args:
            text (str): The input text to be synthesized.
            voice_preset (str, optional): The voice preset to be used for the synthesis. Defaults to "v2/en_speaker_1".
        Returns:
            tuple: A tuple containing the sample rate and the generated audio array.
        """
        pieces = []
        sentences = nltk.sent_tokenize(text)
        silence = np.zeros(int(0.25 * self.model.generation_config.sample_rate))

        for sent in sentences:
            sample_rate, audio_array = self.synthesize(sent, voice_preset)
            pieces += [audio_array, silence.copy()]

        return self.model.generation_config.sample_rate, np.concatenate(pieces) : The class takes an optional parameter, which specifies the device to be used for the model (either if a GPU is available, or ). It loads the Bark model and the corresponding processor from the pre-trained model. You can also use the large version by specifying for the model loader. Initialization ( __init__ ) device cuda cpu suno/bark-small suno/bark : This method takes a input and a parameter, which specifies the voice to be used for the synthesis. You can check out other value . It uses the to prepare the input text and the voice preset, and then generates the audio array using the method. The generated audio array is converted to a NumPy array and the sample rate is returned along with the audio array. Synthesize ( synthesize ) text voice_preset voice_preset here processor model.generate() : This method is used for synthesizing longer text inputs. It first tokenizes the input text into sentences using the function. For each sentence, it calls the method to generate the audio array. It then concatenates the generated audio arrays, with a short silence (0.25 seconds) added between each sentence. Long-form Synthesize ( long_form_synthesize ) nltk.sent_tokenize synthesize Now that we have the set up, we need to prepare the Ollama server for the large language model (LLM) serving. To do this, you'll need to follow these steps: TextToSpeechService : Run the following command to download the latest Llama-2 model from the Ollama repository: Pull the latest Llama-2 model ollama pull llama2 . : If the server is not yet started, execute the following command to start it: . Start the Ollama server ollama serve Once you've completed these steps, your application will be able to use the Ollama server and the Llama-2 model to generate responses to user input. Next, we'll move to the main application logic. First, we need to initialize the following components: : We'll use the Rich library to create a better interactive console for the user within the terminal. Rich Console : We'll initialize a Whisper speech recognition model which is a state-of-the-art open-source speech recognition system developed by OpenAI. We'll use the base English model ( ) for transcribing user input. Whisper Speech-to-Text base.en : We'll initialize a Bark text-to-speech synthesizer instance, which was implemented above. Bark Text-to-Speech : We'll use the built-in from the Langchain library which provides a template for managing the conversational flow. We'll configure it to use the Llama-2 language model with the Ollama backend. Conversational Chain ConversationalChain import time
import threading
import numpy as np
import whisper
import sounddevice as sd
from queue import Queue
from rich.console import Console
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Ollama
from tts import TextToSpeechService

console = Console()
stt = whisper.load_model("base.en")
tts = TextToSpeechService()

template = """
You are a helpful and friendly AI assistant. You are polite, respectful, and aim to provide concise responses of less 
than 20 words.
The conversation transcript is as follows:
{history}
And here is the user's follow-up: {input}
Your response:
"""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
chain = ConversationChain(
    prompt=PROMPT,
    verbose=False,
    memory=ConversationBufferMemory(ai_prefix="Assistant:"),
    llm=Ollama(),
) Now, let's define the necessary functions: : This function runs in a separate thread to capture audio data from the user's microphone using the . The callback function is called whenever new audio data is available, and it puts the data into a for further processing. record_audio sounddevice.RawInputStream data_queue : This function utilizes the Whisper instance to transcribe the audio data from the into text. transcribe data_queue : This function feeds the current conversation context to the Llama-2 language model (via the Langchain ) and retrieves the generated text response. get_llm_response ConversationalChain : This function takes the audio waveform generated by the Bark text-to-speech engine and plays it back to the user using a sound playback library (e.g., ). play_audio sounddevice def record_audio(stop_event, data_queue):
    """
    Captures audio data from the user's microphone and adds it to a queue for further processing.
    Args:
        stop_event (threading.Event): An event that, when set, signals the function to stop recording.
        data_queue (queue.Queue): A queue to which the recorded audio data will be added.
    Returns:
        None
    """
    def callback(indata, frames, time, status):
        if status:
            console.print(status)
        data_queue.put(bytes(indata))

    with sd.RawInputStream(
        samplerate=16000, dtype="int16", channels=1, callback=callback
    ):
        while not stop_event.is_set():
            time.sleep(0.1)

def transcribe(audio_np: np.ndarray) -> str:
    """
    Transcribes the given audio data using the Whisper speech recognition model.
    Args:
        audio_np (numpy.ndarray): The audio data to be transcribed.
    Returns:
        str: The transcribed text.
    """
    result = stt.transcribe(audio_np, fp16=False)  # Set fp16=True if using a GPU
    text = result["text"].strip()
    return text

def get_llm_response(text: str) -> str:
    """
    Generates a response to the given text using the Llama-2 language model.
    Args:
        text (str): The input text to be processed.
    Returns:
        str: The generated response.
    """
    response = chain.predict(input=text)
    if response.startswith("Assistant:"):
        response = response[len("Assistant:") :].strip()
    return response

def play_audio(sample_rate, audio_array):
    """
    Plays the given audio data using the sounddevice library.
    Args:
        sample_rate (int): The sample rate of the audio data.
        audio_array (numpy.ndarray): The audio data to be played.
    Returns:
        None
    """
    sd.play(audio_array, sample_rate)
    sd.wait() Then, we define the main application loop. The main application loop guides the user through the conversational interaction as follows: The user is prompted to press Enter to start recording their input. Once the user presses Enter, the function is called in a separate thread to capture the user's audio input. record_audio When the user presses Enter again to stop the recording, the audio data is transcribed using the function. transcribe The transcribed text is then passed to the function, which generates a response using the Llama-2 language model. get_llm_response The generated response is printed to the console and played back to the user using the function. play_audio if __name__ == "__main__":
    console.print("[cyan]Assistant started! Press Ctrl+C to exit.")

    try:
        while True:
            console.input(
                "Press Enter to start recording, then press Enter again to stop."
            )

            data_queue = Queue()  # type: ignore[var-annotated]
            stop_event = threading.Event()
            recording_thread = threading.Thread(
                target=record_audio,
                args=(stop_event, data_queue),
            )
            recording_thread.start()

            input()
            stop_event.set()
            recording_thread.join()

            audio_data = b"".join(list(data_queue.queue))
            audio_np = (
                np.frombuffer(audio_data, dtype=np.int16).astype(np.float32) / 32768.0
            )

            if audio_np.size > 0:
                with console.status("Transcribing...", spinner="earth"):
                    text = transcribe(audio_np)
                console.print(f"[yellow]You: {text}")

                with console.status("Generating response...", spinner="earth"):
                    response = get_llm_response(text)
                    sample_rate, audio_array = tts.long_form_synthesize(response)

                console.print(f"[cyan]Assistant: {response}")
                play_audio(sample_rate, audio_array)
            else:
                console.print(
                    "[red]No audio recorded. Please ensure your microphone is working."
                )

    except KeyboardInterrupt:
        console.print("\n[red]Exiting...")

    console.print("[blue]Session ended.") Result Once everything is put down together, we can run the application as shown in the video above. The application runs quite slowly on my MacBook because the Bark model is large, even in its smaller version. Therefore, I have slightly sped up the video. For those with a CUDA-enabled computer, it might run faster. Here are the key features of our application: : Users can start and stop recording their voice input, and the assistant responds by playing back the generated audio. Voice-based interaction The assistant maintains the context of the conversation, enabling more coherent and relevant responses. The use of the Llama-2 language model allows the assistant to provide concise and focused responses. Conversational context: For those aiming to elevate this application to a production-ready status, the following enhancements are recommended: : Incorporate optimized versions of the models, such as whisper.cpp, llama.cpp, and bark.cpp, which are designed to boost performance, especially on lower-end computers. Performance Optimization : Implement a system that allows users to customize the bot's persona and prompt, enabling the creation of different types of assistants (e.g., personal, professional, or domain-specific). Customizable Bot Prompts : Develop a user-friendly GUI to enhance the overall user experience, making the application more accessible and visually appealing. Graphical User Interface (GUI) : Expand the application to support multimodal interactions, such as the ability to generate and display images, diagrams, or other visual content in addition to the voice-based responses. Multimodal Capabilities Finally, we have completed our simple voice assistant application, full code can be found at: . This combination of speech recognition, language modeling, and text-to-speech technologies demonstrates how we can build something that sounds difficult but can actually run on your computer. Let's enjoy coding, and don't forget to subscribe to so you don't miss the latest in AI and programming articles. https://github.com/vndee/local-talking-llm my blog Also published here

How to Build Your Own Voice Assistant and Run it Locally Using Whisper + Ollama + Bark

কীভাবে আপনার নিজের ভয়েস সহকারী তৈরি করবেন এবং হুইস্পার + ওল্লামা + বার্ক ব্যবহার করে স্থানীয়ভাবে এটি চালাবেন

So erstellen Sie Ihren eigenen Sprachassistenten und führen ihn lokal mit Whisper + Ollama + Bark aus

Cómo crear su propio asistente de voz y ejecutarlo localmente usando Whisper + Ollama + Bark

Comment créer votre propre assistant vocal et l'exécuter localement à l'aide de Whisper + Ollama + Bark

अपना खुद का वॉयस असिस्टेंट कैसे बनाएं और इसे व्हिस्पर + ओलामा + बार्क का उपयोग करके स्थानीय रूप से कैसे चलाएं

独自の音声アシスタントを構築し、Whisper + Ollama + Bark を使用してローカルで実行する方法

나만의 음성 어시스턴트를 구축하고 Whisper + Ollama + Bark를 사용하여 로컬에서 실행하는 방법

Como construir seu próprio assistente de voz e executá-lo localmente usando Whisper + Ollama + Bark

Как создать собственный голосовой помощник и запустить его локально с помощью Whisper + Ollama + Bark

Kendi Sesli Asistanınızı Oluşturma ve Whisper + Ollama + Bark Kullanarak Yerel Olarak Çalıştırma

Cách xây dựng trợ lý giọng nói của riêng bạn và chạy nó cục bộ bằng Whisper + Ollama + Bark

如何构建您自己的语音助手并使用 Whisper + Ollama + Bark 在本地运行

Introduction If you've been dipping your toes in the domain of automatic speech recognition ("ASR"), there's a good chance you've come across from Meta AI Research. There are some excellent technical resources, not the least of which is the itself, that describes how the machine learning ("ML") model works. Also, the Meta AI Research team has a nice on their website. wav2vec 2.0 ("wav2vec2") original wav2vec2 paper overview of wav2vec2 I would encourage you to take a look at it since it offers a nice summary of the academic paper and since the wav2vec2 model illustrations in this article are sourced from that page. With the preceding in mind, there don't seem to be many write-ups that explain wav2vec2 in "plain English." I try to do just that with this article. This article assumes that you understand some basic ML concepts and that you're interested in understanding how wav2vec2 works at a high level, without getting too deep "into the weeds." Accordingly, the subsequent sections try to avoid a lot of the technical details in favor of simple explanations and useful analogies when appropriate. That being said, it is helpful to know early on that wav2vec2 is comprised of 3 major components: the , the , and the . Feature Encoder Quantization Module Transformer Each will be discussed in the course of starting the discussion with some basic ideas while building up to more complex (but still digestible) points. Keep in mind that wav2vec2 can be used for other purposes beyond ASR. That being said, what follows here discusses the model in an ASR-specific context. A Gentle Overview At the time it was introduced in 2020, wav2vec2 offered a novel framework for building ASR systems. What was so special about it? Before wav2vec2, ASR systems were generally trained using . That is, prior models were trained on many examples of speech audio where example had an associated transcription. To explain the idea, consider this waveform: labeled data each It is not entirely clear what this waveform represents just looking at it. But, if you are told that the speaker who generated this audio said the words "hello world", you can probably make some intelligent guesses as to which parts of the waveform correspond with the text that represents it. You might surmise - correctly - that the first segment of the waveform is associated with the word "hello". Similarly, ASR models can learn how to make associations between spoken audio waveform segments and written text. However, as the original wav2vec2 investigators point out in their paper, "[many] speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance which is not available for the vast majority of the nearly 7,000 languages spoken worldwide." So, the wav2vec2 investigators invented a new model where it is not necessary to have "thousands of hours of transcribed speech" in order to train the system. They reference a useful human analogy: babies learn to speak by hearing a word, and then immediately seeing a text representation of that word. don’t They learn of speech by listening to people in their environment (e.g., their parents, siblings, etc.). wav2vec2 learns in an analogous way: by first. representations listening Of course, how this is achieved is the point of the discussion in this article. Bear in mind that wav2vec2 is broadly designed to : accomplish 2 things Learn what the speech units should be given samples of unlabeled audio. Predict correct speech units. At this point, you don't need to completely understand what is meant by these points. They will be explained below. Just keep them in the back of your head for now. Learning Speech Units Imagine you have a huge dataset of audio samples - say for some number of English speakers. Even without a formal background in , you might understand intuitively that the English language is vocalized using a set of basic sounds that are "strung together" to form words, sentences, etc. phonetics Of course, if you're an English speaker, you don't think about speaking in this way and your vocalizations of whatever you want to say are more or less automatic! But, the point is that the spoken English language - and really any spoken language - can be decomposed into more basic, discrete sounds. If we could somehow coax an ASR model to "extract" these basic sounds, it would allow us to any audio sample of spoken language using them. This is what wav2vec2 does by on audio data. encode pretraining Pretraining, in this context, means that the first part of the model's training is insofar as it is "told" what the basic sounds should be for a given set of audio data. self-supervised not explicitly Diving down a bit more, the system is "fed" a large number of audio-only examples and, from those examples, is able to learn a set of . basic speech units Thus, every audio example is effectively composed of some combination of those speech units; in the same way that you can break a spoken audio sample into a sequence of . phonemes Importantly, the basic speech units that wav2vec2 learns are shorter than phonemes and are 25 milliseconds in length. The question that arises at this point is: How does wav2vec2 learn these speech units from audio alone? The process of learning speech units begins with the Feature Encoder. wav2vec2 "encodes speech audio via a multi-layer convolutional neural network." Convolutional neural networks, or CNNs, are models that allow us to learn features from a given input without those features being explicitly identified beforehand. Each of a CNN can be thought of as extracting features from an input, with those features becoming increasingly more complex as you move up to higher layers. layer In the case of audio data, you might imagine the first layer in a CNN examining of audio information and extracting low-level features, such as primitive sounds. windows A later layer in the same CNN, leveraging the lower-level features extracted in earlier layers, would encode higher-level features, such as sounds approximating phonemes. Following this idea, wav2vec2 can begin to by passing time slices of each audio example into the Feature Encoder and generating a of each slice. "learn what the speech units should be given samples of unlabeled audio" latent representation However, the collection of latent representations do not represent discrete speech units. These representations must be discretized in some way. This is accomplished by passing the output of the Feature Encoder to a . Quantization Module Effectively, the takes all the different audio representations generated by the Feature Encoder and reduces them to a finite set of speech units. It's worthwhile to ask at this point if wav2vec2 should be pretrained on a single language or a variety of languages. Quantization Module Logic tells us that capturing speech units that represent multiple languages versus a single language are likely to be more useful when designing ASR systems that can be used across many languages. To that end, pretraining wav2vec2 with a selection of multilingual audio samples enables the model to produce speech units that do in fact capture multiple languages. The wav2vec2 investigators noted the value behind this approach since Their original findings determined "for some languages, even [audio] data is limited." "that some units are used for only a particular language, whereas others are used in similar languages and sometimes even in languages that aren't very similar." Predicting Speech Units The inventory of speech units is a first step toward being able to encode spoken language audio samples. But, what we really want to achieve is to train wav2vec2 on how these units relate to one another. In other words, we want to understand what speech units are likely to occur in the same as one another. wav2vec2 tackles this task via the Transformer layer. context The Transformer essentially allows wav2vec2 to learn, in a statistical sense, how the speech units are distributed among the various audio examples. This understanding facilitates the encoding of audio samples that the model will "see" after pretraining. Finetuning Ultimately, an ASR system needs to be able to generate a text transcription for a given sequence of audio that it hasn't "seen" before. After pretraining via the steps described above, wav2vec2 is for this purpose. This time the model is shown examples of audio samples and their associated transcriptions. finetuned explicitly At this point, the model is able to utilize what it learned during pretraining to encode audio samples as sequences of speech units and to map those sequences of speech units to individual letters in the vocabulary representing the transcriptions (i.e. the letters "a" to "z" in the case of English). The learning during finetuning completes the training of the wav2vec2 model and allows it to predict the text for new audio examples that were not part of its training during finetuning. Conclusion Of course, the low-level mechanics of wav2vec2 are far more complex than what is presented above. However, to reiterate, the idea of this article is to provide you with a simple, conceptual understanding of how the model works and how it is trained. wav2vec2 is a very powerful ML framework for building ASR systems and its introduced in late 2021 was trained on 128 languages, thus providing an improved platform for designing ASR models across multiple languages. XLS-R variation As mentioned in the Introduction, there are a number of excellent technical resources available to help you learn more. In particular, you may find those provided by to be especially useful. Hugging Face

wav2vec2 for Automatic Speech Recognition In Plain English

প্লেইন ইংরেজিতে স্বয়ংক্রিয় বক্তৃতা স্বীকৃতির জন্য wav2vec2

wav2vec2 für automatische Spracherkennung in einfachem Englisch

wav2vec2 para reconocimiento automático de voz en inglés sencillo

wav2vec2 pour la reconnaissance vocale automatique en anglais simple

सामान्य अंग्रेजी में स्वचालित वाक् पहचान के लिए wav2vec2

平易な英語の自動音声認識のための wav2vec2

일반 영어의 자동 음성 인식을 위한 wav2vec2

wav2vec2 para reconhecimento automático de fala em inglês simples

wav2vec2 для автоматического распознавания речи на простом английском языке

Düz İngilizcede Otomatik Konuşma Tanıma için wav2vec2

wav2vec2 để nhận dạng giọng nói tự động bằng tiếng Anh đơn giản

wav2vec2 用于简单英语的自动语音识别

We often hear about empathy as a singular concept—a soft skill, an essential quality of being human that connects us to others. But empathy comes in two flavors. It has shades, and understanding them might . make us better humans Two Sides of the Same Coin: Logical and Emotional Empathy This is the classic definition of empathy that most of us are familiar with. It’s feeling what another person feels. If your friend is sad, you feel sad. If they’re excited, you feel their joy. Emotional empathy happens almost instinctively. It’s raw and visceral. – Emotional Empathy: This form is more calculated. It’s understanding what another person feels without necessarily feeling it yourself. It’s more about perception, awareness, and insight. It’s about seeing things from their perspective, even if you don’t feel their viewpoint. – Logical Empathy: Emotional empathy might be more natural for some people. You know the type, those who can feel a room’s mood as tangibly as a physical touch. I’ve always admired that, but it wasn’t me. And then there are others for whom logical empathy might be more innate. These individuals are perceptive, analytical, and capable of seeing a situation from various angles without becoming emotionally entangled. Many of us who make our careers in technology are attracted to the industry because we have these skills. Learning the wrong lessons early Most of my first jobs were at large companies with very competitive and hierarchical cultures: IBM, Silicon Graphics, and Microsoft. was legendary for its’ hyper-competitive culture. I worked there for eight years. Microsoft taught me that I had to expect that other teams were constantly looking for how to undermine mine and that every outstretched hand was likely masking a knife held in the other hand behind the back. I eventually realized that the environment was a bad fit for me, but sadly, I didn’t get out until I had internalized those lessons. Microsoft in the 1990s After Microsoft, I sought out more collaborative environments, but I struggled. I constantly expected ill intent behind every action from a peer. I knew that this was hurting me and that I needed to move to a mindset of expecting positive intent from others, but I didn’t know how to rewire my brain. A Splash of Insight: David Foster Wallace’s “This is Water” My epiphany came when someone recommended that I read David Foster Wallace’s commencement address to Kenyon College, “ ” If you haven’t read or listened to it, it’s enlightening. Wallace talks about default settings, the unconscious, automatic ways we interpret everything around us. He speaks about learning to think more compassionately and understanding that everyone around you has a unique inner life full of dreams, fears, and struggles. And it’s not always about feeling their pain; sometimes, it’s about understanding their pain. This is Water. Wallace’s speech was a masterclass in logical empathy. And it gave me a better way to try and understand others’ intents, especially when you don’t know someone well. Developing Logical Empathy When Emotional Empathy Feels Unnatural So, how can you foster logical empathy if emotional empathy doesn’t come naturally to you? Here’s a roadmap: You don’t have to feel what someone else feels to understand them. Listen actively, engage with their words, and seek to understand their perspective. Listen More, Talk Less: If you don’t understand something, ask. Asking not only clarifies but demonstrates that you are engaged and interested in the other person’s perspective. Ask Questions: What could be the pressures on them that they may not be vocalizing? If you are talking to a salesperson near the end of the quota, could they be pressured to make their quota? Is the Product Manager being held to unrealistic expectations by their boss? Leverage what you know about the business or organization to understand what subtexts may be unsaid. Seek to Understand Their Context: Spend time thinking about the perspectives of others. Consider why they feel the way they do. Analyze their thoughts without judgment. Reflect: Try to visualize the scenario from their perspective. This mental exercise helps in understanding without feeling. Use Imagination: Logical empathy may not be instinctive, but it’s still a form of compassion. Approach situations and people with an open heart, even if it’s an analytical one. Practice Compassion: Embracing Both Forms The truth is logical, and are not mutually exclusive. You can be someone who mainly engages with logical empathy while still having the capacity for emotional empathy and vice versa. emotional empathy The real beauty lies in embracing both and recognizing that there’s no right or wrong way to connect with others. It’s a journey, and it’s one worth taking, regardless of where you naturally fall on the empathy spectrum. In our complex and diverse world, empathy in all its shades is more than a desirable trait; it’s a necessity. Understanding how you relate to others and working on enhancing that connection, be it through emotional or logical empathy, makes you not only a better colleague, friend, or partner but a more complete human being. This exploration of empathy, fostered by wise words from thinkers like David Foster Wallace, has been a personal awakening. It’s water, and now I see it. Also published . here

Logical Empathy: Lessons I Learned in Tech and How to Foster It

Anna Apostol, co-founder of Mate academy, an online coding boot camp. Too emotional, often irrational, dependent, and potentially pregnant — this is how female entrepreneurs can still be perceived in the business community. Even when these stereotypes aren’t vocalized, they can make it harder for women to succeed in the startup world. “When my husband told his friend that I worked with him at a startup, he replied that he also had found a ‘warm place’ for a wife in his own company. But the thing is, no one had put me in a startup — I co-founded it,” says Anna Apostol, co-founder of , an online coding boot camp. Mate academy Anna’s story isn’t unique: Gender parity in the startup world remains a critical issue. Discrimination, societal expectations, stereotypes, and unequal access to funding are among the key reasons why entrepreneurship is still considered to be a male-dominated field. Only 14% of the world’s startups are founded; 20% are by women. solo-female co-founded Overcoming gender bias in fundraising Despite the growing global trend towards gender equality, there remains a substantial gap between men and women when it comes to attracting capital for their businesses. According to , women-founded startups raised only 1.9% of all VC funds in 2022. TechCrunch In trying to balance the scales, some VC funds, including , , and , invest solely in startups led by women. But it’s a drop in the ocean. Graham & Walker AmplifyHer Ventures The Artemis fund About of female founders still face gender bias when raising capital and, in similar situations, receive an average of 5% less funding than their male counterparts. Female founders are frequently perceived as being inherently riskier to invest in than men. As a result, their companies are often undervalued. 35% When it comes to funding, the situation is better in mixed-gender startups — last year, they raised 17.2% of all VC investments. This significant gap seems to confirm the unconscious bias against women entrepreneurs, perceiving them as less competent, less goal-oriented, and less likely to succeed. At the same time, gender-mixed teams outperform all-male founding teams by . 63% “When we were raising our first round, [co-founder] Roman Apostol negotiated with the investors for the most part. He had worked at Google, which, of course, also affected this decision,” says Anna Apostol. “As Roman was more public, people thought he was the only one involved in the startup. We realized that such perception was a result of stereotypes in society. Moreover, in reality, we shared responsibilities and I had my own area. But this was the impetus for us to work on popularizing the message that gender does not matter in Tech,” she adds. Escaping stereotypes One of the main myths about women in business is that they're more emotional, which prevents them from making rational decisions. However, a 2021 argued that women and men are equally emotional by nature. study Even if some women are more likely to express their feelings openly, it doesn't automatically mean that they are chaotic in decision-making. Instead, companies with executive teams in the top quartile for gender diversity were more likely to achieve above-average profitability compared to those in the bottom quartile. Similarly, companies with over 30% of female executives were more likely to outperform those with fewer women on their staff. 25% “When communicating with new hire candidates, I am quite open and welcoming. That’s why it seems that I have low expectations and that I’m not that strict,” says Sofia Vyshnevska, co-founder at . NewHomesMate “Imagine their surprise after they had worked with me and realized that the situation was totally different. Inside our operations, I am considered the ‘bad cop,’ being very demanding, while [another co-founder] Dan is the ‘good cop.’” Juggling startup and household chores Women often have to balance not just work and hobbies, travel, and leisure — they also do most of the housework. Women spend 55% of their time taking care of children, cleaning up, and doing other household work, according to the by the World Economic Forum. When creating a startup or building a career, these chores aren’t disappearing completely from their lives. Global Gender Gap Report At the same time, even with these additional responsibilities, a study shows women are more at work than men. productive Nevertheless, society still does not believe in the superpower of women being able to grow a business, build a career, and take care of a child. There is even the term so-called Motherhood Penalty — when women’s pay decreases once they become mothers. Studies have that women with children are often paid less than their male or childless female counterparts, even when controlling for factors such as experience and education. They may also face greater difficulty in getting hired or promoted and may be seen as less committed to their jobs. shown Vyshnevska appreciates this time-management skill in other women she hires, helping them overcome this phenomenon. “I gave birth at 18. Since then, I know firsthand the difficulties women with children face in building a career or a startup,” says Vyshnevska. “But I also know how disciplined mothers are at managing their time. So when we interview female candidates with children, I initially have a friendly attitude towards them, without any prejudice,” she says. Caring for company culture, diversity Female leaders pay more attention to workplace culture, which benefits their employees, according to a by career and life coach Caroline Castrillon. Women may be more likely to promote a collaborative and inclusive environment where employees feel valued and supported, she writes. Forbes column Several studies Castrillon mentions in the story found that female founders prioritize work-life balance and employee well-being, recognizing the importance of maintaining a healthy and sustainable work environment. The experience Apostol had at Mate academy seems to prove this. “While our startup team was under a hundred employees, we didn’t have an HR specialist. So I did his job, and I liked it,” she says. “I believe empathy inherent in women helps us create a healthy atmosphere in the team.”

The Truth of Female Founders in a Male-Dominated Startup World

La verdad de las fundadoras en un mundo de startups dominado por hombres

La vérité sur les fondatrices dans un monde de startups dominé par les hommes

पुरुष-प्रधान स्टार्टअप दुनिया में महिला संस्थापकों का सच

男性優位のスタートアップ界における女性創業者の真実

A verdade sobre mulheres fundadoras em um mundo de startups dominado por homens

Sự thật về những nhà sáng lập nữ trong thế giới khởi nghiệp do nam giới thống trị

男性主导的创业世界中女性创始人的真相

I’m Leandro Nuñez, 42 years old. Argentinian software engineer. I’d like to think I’m a programmer since I found out what Pascal was. I’m also, a passionate fan of the Star Wars universe. Interesting! What was your latest Hackernoon Top story about? . I believe this guide can offer valuable assistance to individuals who are facing challenges while implementing certain features in their apps. Nextjs Server Actions and how to build a real-time application with it Do you usually write on similar topics? If not, what do you usually write about? Yes, I typically focus on similar topics. I strive to explore various programming languages and different solutions, but my mainstay remains within the same domain of subjects. My primary niche has consistently centered around a specific set of topics, most of them “trending” as React. Great! What is your usual writing routine like (if you have one?) Well, it usually starts with a cup of coffee to kick things off. Then, I venture online to spot emerging trends, checking platforms like (formerly known as ) and Discord to uncover intriguing subjects. Once I have a topic, I craft a fitting title and outline before delving into the actual writing process. Sometimes it takes me days, sometimes just hours—it all depends on my available time, work commitments, and family responsibilities. X Twitter Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? My biggest challenge undoubtedly stems from the fact that I write in English, which isn't my native language. Overcoming this language barrier is quite demanding. Additionally, managing time can be tricky. While I'm enthusiastic about assisting others through my writing, finding the right window of time to sit down and write isn't always easy. What is the next thing you hope to achieve in your career? My aspiration is to connect with more programmers who want to learn and achieve milestones in their programming journeys. I aim to contribute to their growth and progress in meaningful ways. I really hope to achieve that. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? I find immense joy in receiving notifications about people liking my posts. While my primary intention is to offer help through my writing, getting an email that reads "Your story was selected as one of the best of the week" brings me a great sense of satisfaction. Do you have a non-tech-related hobby? If yes, what is it? Absolutely! Whenever I have the opportunity, I transform into a classic heavy metal singer. I must admit, I'm not exactly the greatest vocalist, but my passion for heavy metal runs deep. It all started as a fun joke with my lifelong heavy metal enthusiast friends. Since I'm the only English speaker among us, we began covering songs from various bands, and that's how my musical journey took off. What can the Hacker Noon community expect to read from you next? I'm planning to go deeper into Next.js, exploring its capabilities and features extensively. Additionally, I'm looking to tackle the challenges of mobile development by sharing insights on complex tasks in that realm. I had to learn Flutter the hard way (and in a really short time), so I’ll try to help others to learn it the easy way. What’s your opinion on HackerNoon as a platform for writers? My view is simple—HackerNoon is an incredible platform. I'm genuinely grateful to have stumbled upon it. The opportunities and exposure it provides are truly remarkable. Thanks for taking time to join our “ ” series. It was a pleasure. Do you have any closing words? Meet the writer I want to express my gratitude to Hackernoon for aiding me in realizing my aspiration of becoming a full-time writer and assisting others. Given my background from the pre-internet era, I'm determined to spare people the coding struggles I encountered along my journey.

Meet the Writer: HackerNoon's Contributor Leandro, Software Engineer

Stable diffusion had created a storm , created a and recently . And now, the new kid on the block language models seem to do everything from to . ChatGPT is all the hype now and there’s no short of creative applications for generative models. generating award winning images version of me in an alternate universe got in a whirlwind of PR/HR issues predicting stock prices becoming the CEO of a brand new startup Despite all the advancement in AI, we are still applying them with caution especially when creating education/information related products. That faithful morning, my wife repeatedly played YouTube flashcards videos for .  And she asked, “ChatGPT can do everything, why don’t they make flash cards videos?”. Then I replied, “Challenge accepted”. Hence, this article on the rabbit hole of trying to automatically create flash card videos using a couple of generative AI models. infant/toddler’s right brain development And she asked, “ChatGPT can do everything, why don’t they make flash cards videos?” What are Right Brain Development Flashcards? They are a series of flashcards shown to infant/toddlers at quick succession, usually less than 1 second per card. The flashcards in each session / video are particular to a specific category or topics, e.g. showing a series of fruits . ['apples', 'apricot', 'banana', 'blueberry', 'coconut’] https://youtu.be/-shR2MAXT0Q?t=71 The general idea is to automatically generate the a video of flashcards given a specified list, thus it’ll require: Some way to generate a sound file of each word in the list ( or ) .mp3 .wav Ah-ha!, ! text-to-speech (TTS) models Some how create an image file given a word / string Definitely, some stable diffusion thing A way to combine the image with the sound files into a video ( ) .mp4' I don’t know the solutions yet but it’s surely some CLI command ffmpeg Vänta ett ögonblick!(Wait a minute!) Where is ChatGPT in this? Alright, alright. Lets change the specifications a little, instead of generating a flashcard video given a list of vocabulary words, lets “prompt” the ChatGPT to give us a list of words given specific topics Give ChatGPT a topic and ask it to give me a list of words for kids’ language learning Some way to generate a sound file of each word in the list Some how create an image file given a word / string A way to combine the image with the sound files into a video Hey ChatGPT, “ " Name 10 fruits from gpt4all import GPT4All

# I know it's not the actual "ChatGPT" from Open AI..
# But still it's easier to just personalize to the LLM as "ChatGPT".

gptj = GPT4All("ggml-gpt4all-j-v1.3-groovy")
messages = [{"role": "user", "content": "Name 10 fruits"}]
gptj.chat_completion(messages) [out]: {'model': 'ggml-gpt4all-j-v1.3-groovy',
 'usage': {'prompt_tokens': 240,
  'completion_tokens': 304,
  'total_tokens': 544},
 'choices': [{'message': {'role': 'assistant',
    'content': 
'1. Apple, Banana, Orange (all fruits)'
'2. Pear, Cherry, Grape (all fruits)'
'3. Kiwi, Mango (fruits)'
'4. Pineapple, Strawberry (fruits)'
'5. Watermelon, Lemon (fruits)'
'6. Kiwi, Mango (fruits)'
'7. Apple, Banana, Orange (all fruits)'
'8. Pineapple, Strawberry (fruits)'
'9. Watermelon, Lemon (fruits)'
'10. Kiwi, Mango (fruits)'}}]
} My reaction: Either I don’t know how to prompt, or ChatGPT don’t know how to count -_- Hey ChatGPT, “ ” List down 10 fruit words and only 10. messages = [{"role": "user", "content": "List down 10 fruit words and only 10."}]
gptj.chat_completion(messages) [out]: {'model': 'ggml-gpt4all-j-v1.3-groovy',
 'usage': {'prompt_tokens': 263,
  'completion_tokens': 157,
  'total_tokens': 420},
 'choices': [{'message': {'role': 'assistant',
    'content': ' Sure, here are 10 fruit words that I can think of:'
'1. Apple '
'2. Banana '
'3. Orange '
'4. Pear '
'5. Cherry ''
'6. Strawberry '
'7. Kiwi '
'8. Grapes '
'9. Pineapple '
'10. Watermelon'}}]} My reaction: Now the list looks right but I can’t put it in a Python list easily, if I spend time editing the list into runnable code, I might as well type them out myself @_@ Hey ChatGPT, “ ” List down 10 fruit words, separate the list by commas without numbers prompt = “List down 10 fruit words, separate the list by commas without numbers”

messages = [{"role": "user", "content": prompt}]

gptj.chat_completion(messages) [out]: ' apple, banana, orange' My reaction: What the %^&*?! I seriously can do a better job with https://www.google.com/search?q=list+10+fruits (╯_╰)  ||| I’m going to try some passive-aggression with ChatGPT messages = [{"role": "user", "content": 
             "Can you count seriously? I'm telling you to list down 10 fruits. "
             'List 10 fruit words, separate by commas.'}]
results = gptj.chat_completion(messages) [out]: {'model': 'ggml-gpt4all-j-v1.3-groovy',
 'usage': {'prompt_tokens': 331,
  'completion_tokens': 129,
  'total_tokens': 460},
 'choices': [{'message': {'role': 'assistant',
    'content': 
' Sure, here are 10 fruits separated by commas:'
'apple, banana, cherry, grapefruit, kiwi, mango, peach, pear, pineapple, strawberry.'
}}]} And then: fruits = result['choices'][0]['message']['content'].partition(':')[2].strip('.').split(',') My reaction: > len("['apple, banana, cherry, grapefruit, kiwi, mango, peach, "
      "pear, pineapple, strawberry']")
86

> len("result['choices'][0]['message']['content'].partition(':')"
      "[2].strip('.').split(',')")
82 Seriously, to save my mind from thinking up 10 fruits and save 4 characters when typing them out… 割鸡焉用牛刀… Now that the ChatGPT and prompt-engineering clickbait part is over, time to get serious with the flashcard generation. From Text to Speech The machine learning terminology to create a sound file given a text is usually referred to “ ” aka TTS. By default, a lot of operating systems already comes with some pretty nifty TTS systems that you can access through command prompt. Text-to-Speech On a MacOS, you can try this on the terminal: say -v Samantha "hello world" And on an Linux/Windows, you can , then first install espeak espeak -v en-us "hello world" But if you’re the living on the bleeding edge, you can try this model from the HuggingFace library in Python: SpeechT5 TTS transformers import torch

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset

# Load all the pretrained models...
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")


# Load xvector containing speaker's voice characteristics from a dataset.
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)


# Converts the text to a speech to a tensor / array of floats.
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"], 
    speaker_embeddings, vocoder=vocoder) 

# [out]: tensor([-1.6944e-04,  1.0556e-04,  ...,  -3.7849e-05, -2.1459e-04]) Then to write the object into an actual sound file, we’re gonna use the . torch.tensor soundfile audio library based on libsndfile, CFFI and NumPy import soundfile as sf

def create_sound(text, outputfile):
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(inputs["input_ids"], 
        speaker_embeddings, vocoder=vocoder)
    sf.write(outputfile, speech.numpy(), samplerate=16000)

# Example usage of the TTS function.
create_sound("hello world", "hw.mp3") Stable Diffusion (Manga Mode!) The vanilla stable diffusion where you type in a sentence and it generates the image is just so 2022. It’s 2023,  lets do image generation with some manga flair! Generating manga/anime have been always been in the mind of enthusiasts. Manga fans have already shown in 2019 (aka “grandfather of stable diffusion”). And computer vision scientists didn’t gave up ever since comment. impressive results with Generative Adversarial Network Hayao Miyazaki’s “ [AI generated anime is] an insult to life itself ” “ ” [AI generated anime is] an insult to life itself - Hayao Miyazaki (2018) After multiple generations of different text to image generation, we get “ ”! Kawaii Diffusion Give me the code already!! from diffusers import DiffusionPipeline


diffusion_pipe = DiffusionPipeline.from_pretrained("Ojimi/anime-kawai-diffusion")
diffusion_pipe = diffusion_pipe.to("cuda")

def create_image(text, outputfile, prompt):
    image = diffusion_pipe(
        text.lower().strip().replace('_', ' ') + prompt, 
        negative_prompt="lowres, girl, bad anatomy").images[0]
    image.save(outputfile)

text = "hello world"
create_image(text, f"{text}.jpg") And immediately we see something different from how we use an image generator instead of sound generator. There is some sort of positive and negative “prompt engineering”. A few questions would come to an end-user and I find them food-for-thought for machine learning models creator too: Why is it that we need to prompt to get a good image generation? E.g. why do we need to explicitly do negative prompt to avoid low resolution? lowres Why is it that we don’t prompt text-to-speech models? E.g. why can’t we do something like Generate speech in a low deep voice: Hello World I guess that’s where selecting a and do the magic but can’t we prompt it like how we do text and image NLP? vocaloid speaker_embeddings Can abstract ideas like “Hello World” be generated into an image? Can an artist draw an abstract idea without research/inspiration? Are there other that modern TTS models can’t emulate? modalities of speech ( phonopragmatics ) Going back to Flashcard Generation Now that we have sound and images, putting them together into a video requires some video-editing finesse. You might have come across and I totally appreciate all the work put into the open source, that serves ~25K other libraries depending on it. But nothing beats raw CLI when it comes to the feature packed . some functions using moviepy 1-4 contributors maintaining a library ffmpeg And so, I went the “ ” around Python way: cheating word = "hello-world"

os.system(
  f"ffmpeg -i {word}.jpg -i {word}.mp3 "
   "-c:v libx264 -tune stillimage -c:a copy -preset:v veryslow "
  f"{word}.mp4"
) The would combine a single image file and an sound file to create a . video file. There are a whole bunch of audio-video options that I don’t really know and I boldly say, I took it from and added a few other options here and there. ffmpeg .jpg .mp3 .mp4 https://superuser.com/questions/1041816/combine-one-image-one-audio-file-to-make-one-video-using-ffmpeg And finally to combine the video file for each word, we do: os.system(
  """for f in `ls *.mp4 | sort -g`; do echo "file '$f'" """ 
   ">> mylist.txt; done"
)
os.system(f"ffmpeg -f concat -i mylist.txt generated-flashcards.mp4") The shell loop throughs the files, , and put the list into a text file. Then the can use the concat file argument to create the final flashcard video file. .mp4 sort them in the alphanumerical order ffmpeg Looping through the fruit list given by ChatGPT, we do this: import os 

for i, fruit in enumerate(fruits):
    create_sound(fruit, f"{i}-{fruit}.mp3")
    create_image(fruit, f"{i}-{fruit}.jpg", prompt=" fruit on table")
    os.system(f"ffmpeg -i {i}-{fruit}.jpg -i {i}-{fruit}.mp3 -c:v libx264 -tune stillimage -c:a copy -preset:v veryslow {i}-{fruit}.mp4")
os.system("rm mylist.txt")
os.system("""for f in *.mp4; do echo "file '$f'" >> mylist.txt; done""")
os.system(f"ffmpeg -f concat -i mylist.txt  ai-shichida.mp4") Ta-da! Now we’ve created an AI generated flashcard video… https://youtu.be/O-abE4CqbBg My reaction: When will I ever get the 1,000,000 subscribers gold press button? Epilogue / Disclaimers The full code to create your own flash card is available on https://www.kaggle.com/alvations/generative-language-learning-flashcard I understand that the model isn’t the paid ChatGPT service from Open AI, but for simplicity of understanding it’s a large language model somehow pre-trained on “ ” GPT4All human aligned instructions Note that the “ ” attempted for this post is much longer than the examples posted in the “ChatGPT” section above, I’ve tried a lot more prompts before the passive-aggressive one. prompt engineering You might have to loop through the fruits and re-create the images multiple times to get a generated image that you desire. The final video in this post was created after looping through the 10+ times… create_image Here’s a list of  flashcards I’ve created using the same TTS + Diffusion code: Things that move (Vehicles) Animals (Chinese Zodiac) Fruits (The one shown in this post)

Generative Language Flashcards

A Man's First Encounter with Society

The Little Shop at New Romney

My way is to find a crazy new hobby! Discovering my inner artist helped me recover from burnout and beat learned helplessness. I tested whether I could sing and produce musical content, and through that, I realized how creativity helps me in business and how to get back to life after the learned helplessness syndrome. I'm writing today from a cool state of mind, but it wasn't like that a few months ago. As an empathetic person, it's hard for me to cope with what's been happening in the world lately. Sometimes I lack optimism! And as a young startup entrepreneur who always works with uncertainty, I felt my vulnerability in full force. After feeling this decadence, I decided that I needed to find a way to work with these states and convert this energy into something constructive. How? For me, it works to do some crazy stuff! There is so much sense in this, as I later found out. In creativity, it's much easier to set goals because there is more freedom in planning, fantasizing, and dreaming. There is no constriction, as I usually experience in business when I have to fit my goal into all the trendy frameworks and smart approaches. These are all working tools, but sometimes, I want freedom from frameworks. Or not even that... It was time to create my own new personal framework! The Blueprint for Rapid Idea Implementation I was bored, and the thought came to me that I should learn to sing to entertain myself, or at least try something new. I didn't like singing in my childhood, and I was so shy about singing that I even forbade my parents to do it. My mother recently told me this, and we laughed about it together. That's how negative my experience with singing was. Nevertheless, I decided to interact with my shyness and awkwardness. Decision Made. Needed a Teacher. I was afraid of finding a bad teacher who could damage my vocal cords and ultimately make me lose my voice. This fear arose from seeing cases in the media where some singers lost their voices and had to undergo surgeries on their vocal cords. I definitely didn't want that kind of outcome, so I was concerned about finding the right vocal technique and singing in an eco-friendly way. Therefore, I approached the search for a teacher with great responsibility. Using the test-and-learn method, I selected 5 teachers from various platforms, from whom I was willing to take a trial lesson. Since I knew absolutely nothing about singing and vocal techniques, I started to understand where there was logic and sense, and who would definitely ruin my voice by attending the trial lessons and gaining preliminary knowledge from the teachers. After my mini-research, I settled on Olga Key, with whom I immediately understood that she was the perfect fit for my goal. Olga explained to me why this Italian technique is worth singing in and how to properly master and apply it. And most importantly, with this technique, people do not lose their voices if everything is done correctly, but rather their voices strengthen and becomes more expressive over time. That's important to me! I set a goal – to record a song within 3 months of starting lessons. This would be my ideal showcase and indicator that I had mastered a new skill. So we got to work! I immediately received a personalized roadmap of what we needed to do and when to achieve the desired result. Beginning of Lessons. The Right Teacher Is the Foundation for Achieving Results I went to in-person lessons 2-3 times a week from October to December 2022. Initially, the lesson lasted 1.5 hours, but then we reduced it to 1 hour. But in reality, the time I devoted to vocal training was 3-3.5 hours because I had to ride a bike for an hour to Olga's studio and then an hour back. While I was riding my bike, I did breathing exercises and sang my favorite songs, trying to apply the knowledge I had gained up to that point. Therefore, we almost didn't do warm-up exercises together because I was already warmed up like a locomotive. That's how the next 3 months went. Ordering a Song. Briefing and Funnel of Performers – The Key to Success After the first two months of lessons, I began to search for material to order a song. I wrote a detailed briefing and spread the task across various freelance platforms. I approached finding the author of my future song in the same way as finding a teacher: I wrote to 20-30 people. I was able to communicate well with about 15 of them. Even fewer people seriously engaged with the material. In the end, I ordered a song from 2 people to compare and choose the best one. My song had to be in Spanish. The first performer was a guy from Venezuela. He quickly sent me the lyrics that fit my request and energy. Frankly, I was very afraid that I would receive some garbage that I would be ashamed to show to anyone, and I would experience Spanish shame already during the demo listening. But apparently, the power of my intention was such that all obstacles simply parted, and the guy sent me a really cool song that hooked me right away; I repeated it all the time. I immediately sent my music material to my teacher. And we started working on the song to record my vocals in the studio – we dedicated the third month to this. Recording. Get Yourself Together and Do It! The recording process also went smoothly as well. It turned out that sound work is a hobby of my friends, and they have all the necessary equipment for it. Before Christmas, we picked a time and recorded the sound. At first, I was very embarrassed to sing in front of anyone except my teacher. But I had no choice but to gather myself and make a recording. In the end, I got a studio recording of my vocals. All that remained was to do the final arrangement, create a music video, release the song and try out marketing mechanics. Final Arrangement. The Diamond of This Project Is the Team. I planned to spend January, February, and March in Latin America, and that's when I left the arrangement tasks. I made a funnel of those who were capable of handling the task. Out of about 10 arrangers, I chose the one who had experience creating tracks for Bebe, a Spanish singer. We discussed the terms, signed the contract, and I made the advance payment, and the work began. Within a few days, I received the first working version. I made a couple of edits, and after a couple of days, I received the final version. I didn't believe it until the last moment and was surprised that I produced such a high-quality musical product on the first try. My relationship with academic music is as follows: I watch videos of "What is music? for dummies" and don't even understand that. I was delighted that my first experience turned out to be so positive. Otherwise, who knows how disappointed I would have been and would have given up on this idea if I received some cringe material. Although I did receive it – the second song was exactly like that! 😄 Music Video. Working With Talented People Is a Separate Kind of Pleasure. A month after the final arrangement, we already shot a music video in Buenos Aires. It happened spontaneously, and I accidentally found a talented camera operator just in BA. I had to use the morning to prepare because there was nothing fatal in my hiking bag. I was worried that I would look ridiculous on camera, and that I would also look ridiculous on the street, where I would have to sing and dance to my song under a speaker. These were all my assumptions. In reality, everything turned out to be fantastic. From the first few seconds, I relaxed and started improvising with the camera operator. We made a lot of doubles in different locations over six hours. We were exhausted, but satisfied. It took a week to edit, and now the music video is in my cloud. Next up is the release and marketing. I can't believe how far I've come. 🤪 Release Preparation For the release, I chose the TuneCore platform. Their interface is a bit outdated, but everything is clear. I set the release date for my song and the upload of my music video on YouTube for April 5th. Honestly, I shouldn't have waited so long, I should have released it on March 8th because the vibe and energy of the song were perfect for International Women's Day. But that thought passed me by, and the release took place on April 5th. Release Day. My New Birthday I am writing this note at 9:28 PM on April 5th, 2023. My head is buzzing from the overload of emotions. Today was the start of something new, possibly my solo career. I lived this day much cooler than many of my birthdays. I received so many congratulations from like-minded people and even from more distant acquaintances. I haven't had such a festive mood in a while. I sincerely wish everyone to experience such a state and emotions. I had nothing ready for the release, although a huge amount of marketing activities were planned. I will gather part 2 of this case about the marketing block after the release and also publish it as soon as I finish all the experiments. But I was not upset and did not blame myself for this imperfection. I remained very happy and satisfied with this day. My subjective experiences were associated with experiencing how some clearly new patterns of behavior, decision-making, and life perception are formed in my brain and its neural network. Some completely new and high-quality render! What I Specifically Took for Myself From This Case in My Business Projects That you can dive into any unfamiliar topic and get results there. Your failure is not guaranteed! The diamond of the project is a team of talented people. All members of the team were much more talented than me in the musical field, but my talent manifested itself in this case not so much as a vocalist talent, but more as a manager who was able to find and gather competent people around the project and organize the space so that the team could deliver results. Through business, I would probably have come to this, but through creativity, it became much more visual and faster for me. Meanwhile, Here Are Links to My Song and Music Video Music video on YouTube Listen on Spotify Listen on Apple Music Thank you for reading to the end and experiencing this with me! See you in the second part of the article, where I will talk about marketing experiments with a musical product.

From Stress to Success: My Journey of Overcoming Burnout

Virtual Reality (VR) has opened up new frontiers in how we interact with technology. I recently had the opportunity to push those boundaries even further with a tech demo that integrates , Speech-to-Text (STT), and Amazon Web Services (AWS) Polly in a VR experience. ChatGPT Google The result? A truly immersive and interactive conversation with an AI-powered Ready Player Me avatar, driven by ChatGPT's responses and enriched with voice input and output capabilities. The concept behind this tech demo was to create a virtual room where users can have realistic conversations with an AI avatar, powered by ChatGPT. To take the experience to the next level, I integrated Google STT for voice input, which transcribes the user's speech into text. This text is then sent to a micro-service for processing and forwarded to ChatGPT for generating a relevant response. Once the response is generated, AWS Polly is used for text-to-speech (TTS) conversion, and the output is sent back to the avatar for voice processing, resulting in a mostly seamless and dynamic conversation. One of the standout features of this tech demo is the integration of Ready Player Me avatars, with Lip Sync turned on. This means that as audio plays, the avatar’s mouth moves in sync with their speech, creating a highly realistic and interactive conversation experience. These avatars serve as the visual representation of the AI, adding a layer of immersion and personalization to the conversation. To make the conversations engaging, I created three pre-filled prompt scenarios for ChatGPT. In the first scenario, the AI plays the role of a financial representative, providing advice on managing finances and investments. https://youtu.be/CtmqZEEH-mY?embedable=true The second scenario involves the AI acting as a psychiatrist, providing virtual therapy and counseling. https://youtu.be/QG4efRFH82E?embedable=true Lastly, in the third scenario, the AI takes on the persona of a fantasy merchant, selling virtual gear and items. https://youtu.be/r8DcUEcx_kQ?embedable=true These scenarios provide a glimpse into the potential use cases of this technology in various domains, such as finance, mental health, and entertainment. Although not talked about enough, is a talent in its own right. As you can see , setting up a contextual scene and ensuring the avatar doesn’t lose character can be complicated.  Essentially, we need to ensure the model doesn’t break the script but remains realistic.  From the full videos above, you will find the Fantasy merchant occasionally breaks character and displays a repetitive, almost nervous tick of saying "well, well, well" while vocalizing their emotions. prompt engineering in the code Creating Believable Environments for Immersive VR Conversations It's important to note that this tech demo primarily used off-the-shelf animations and models for the art direction. However, for a full-fledged application, investing in realistic animations, including talking animations with for positive/negative animation tones, and filler animations during processing time, can enhance the believability and naturalness of the AI interactions. This will further elevate the immersive experience and make it more akin to human-like conversations. sentiment analysis One of the challenges in creating a truly immersive VR conversation experience is the limitations of our senses. In virtual environments, we typically rely on sight and sound to perceive and interact with the world. As these are the 2 senses that are engaged, you are hyper-aware when something in a scenario seems off.  To make the virtual world feel more real and distract from the surreal nature of the environment, it's crucial to create believable surroundings that mimic real-world environments. Visuals play a crucial role in creating a sense of presence in VR. Using realistic 3D models may help, but textures, lighting, and animations can create an environment that looks and feels like the real world even with stylized graphics. For example, if the AI avatar is placed in a virtual office, using accurate office furniture, decorations, and lighting can create a familiar environment that users can relate to, making the conversation feel more authentic. Sound is another key element that adds to the immersion in VR conversations. Spatial audio, where the sound changes direction and intensity based on the user's position and head movements, can greatly enhance the sense of presence. For instance, if the user hears the AI avatar's voice coming from the direction where the avatar is located, it adds to the realism of the conversation.  However even more important than the sound of the avatar, is the white noise sound of the day-to-day.  Sounds of an assistant rumbling papers, people shuffling outside, phones, etc.  These white-noise-generating sounds are necessary to help mask any computation thinking and will help distract the user and keep them in a surreal immersion. Watching the replays of the video interactions, they will all seem off.  The environment was specifically crafted for debug overlays, and all background white noise was absent.  If I were to focus on creating a realistic experience my focus areas would include; animations, sound design, set design, and prompt engineering.  This would be the order of importance, prompt engineering would be last in my considerations as when you are the one talking to the AI, it can shock you at times how good it can be in predicting what it should say next, especially with a well-timed animation. Conclusion - Taking on the Future While this tech demo showcases the immense potential of integrating ChatGPT, Google STT, and AWS Polly in a VR experience, it also raises important ethical considerations. Ensuring that user data is handled securely and responsibly and that AI models are trained in a fair and unbiased manner, should be prioritized in the development and deployment of such technologies.  As these interactions become more widely available, creating simulated virtual humans to trick personal information out of willing users may seem like something out of an episode of Black Mirror, but is quickly coming into the realm of possibility. In conclusion, this tech demo represents a significant step forward in breaking boundaries in VR interactions with AI. The integration of ChatGPT, Google STT, and AWS Polly enables immersive and dynamic conversations, paving the way for exciting possibilities in domains such as education, customer service, and entertainment. With further advancements in animation and AI technologies, we can expect a future where virtual conversations with AI avatars become more natural, engaging, and mainstream. The potential of this technology is vast, and I am thrilled to see how it evolves and transforms our interaction with AI in the virtual world. Links: Github for Sigmund Microservice: https://github.com/goldsziggy/sigmund Docker File for Microservice: docker run -it -p 8080:8080 --env-file .env matthewzygowicz/ms-sigmund If enough interest is gathered, I can/will rewrite the Unity portion of the code using all open-source assets to open-source that as well.

Immersive VR Conversations with AI Avatars: Integrating ChatGPT, Google STT, and AWS Polly

AI অবতারের সাথে ইমারসিভ ভিআর কথোপকথন: ChatGPT, Google STT, এবং AWS পলিকে একীভূত করা

Immersive VR-Gespräche mit KI-Avataren: Integration von ChatGPT, Google STT und AWS Polly

Conversaciones inmersivas de realidad virtual con avatares de IA: integración de ChatGPT, Google STT y AWS Polly

Conversations immersives en réalité virtuelle avec des avatars IA : intégration de ChatGPT, Google STT et AWS Polly

एआई अवतारों के साथ इमर्सिव वीआर वार्तालाप: चैटजीपीटी, गूगल एसटीटी और एडब्ल्यूएस पोली को एकीकृत करना

AI アバターとの没入型 VR 会話: ChatGPT、Google STT、および AWS Polly の統合

AI 아바타를 사용한 몰입형 VR 대화: ChatGPT, Google STT 및 AWS Polly 통합

Conversas imersivas em RV com avatares de IA: Integrando ChatGPT, Google STT e AWS Polly

Yapay Zeka Avatarlarıyla Sürükleyici VR Konuşmaları: ChatGPT, Google STT ve AWS Polly'yi Entegre Etme

Cuộc trò chuyện thực tế ảo sống động với hình đại diện AI: Tích hợp ChatGPT, Google STT và AWS Polly

与 AI 化身的沉浸式 VR 对话：集成 ChatGPT、Google STT 和 AWS Polly

For those of you who have been following me for a while, you know that I'm passionate about artificial intelligence (AI) and all the potential it holds. I've written several articles on it, ranging from to and . I'm constantly reading and experimenting in this domain. From small Python applications to help with article creation. ChatGPT Wordtune 174 AI Tools To Try Lately, I've noticed a lot of content about how to create an animated avatar using AI. So, I thought I'd give it a shot! Your Avatar To get your avatar made, use a drawing AI app like or DALL-E 2 Midjourney The image below was created with Midjourney: , you will require Discord. If you don't have an account, make one first. To register for Midjourney Then, click the Midjourney logo on the top left to access the 'newbie' channels. From there type ‘/imagine’ Proceed with the prompt. Here you can indicate what Midjourney should create for you. This is the prompt I used: Midjourney then asks you to accept the Terms of Service before giving you 4 images to choose from. You can edit those as well. I picked the one top-right. Pick the one that looks best to you and save it! Script for the Spoken Word I used ChatGPT to create a script. I entered a prompt and it delivered this text below. Now, this piece of text has to transform into a spoken word audio file. Spoken Word Fear not, as I've got the solution - audibly record my text? No way! I'm not a fan of hearing my own voice, so that's off the table. But don't worry, is here to save the day and make my life easier. ElevenLabs 333 or 10,000 If you don’t register with ElevenLabs, you will have 333 characters at your disposal to convert into speech. I registered, so I have 10,000 characters to convert into speech. As you can see, there's a bunch of voices to pick from. I chose the first one, and it read my text out loud. Copy what ChatGPT gave you into the text box at ElevenLabs and press that 'Generate' button. Once you do, you'll see a play icon on the bottom part of your screen. Just hit it, and you hear the text read out loud, and you can experiment with different voices! Have fun! If you're thrilled with the text and the accompanying vocalizations, click that download button! Then, there's just one more step to complete this tale - getting the animation. Create the Animation For this, I used . This application is very easy to use. D-ID In the left bar, click on ‘Create Video’ In the next screen, you will add your avatar, the one you created with Midjourney, as a Presenter. You do this by clicking ‘Add’ under the heading ‘Choose a presenter’ You choose the file you want to upload and you’re done with the presenter. We’ll keep on clicking. On the right side of your screen, you’ll see the option to upload Voice Audio. Click on ‘Upload your own voice’ and load the audio file you saved at the ElevenLabs step earlier. If that went well, you can click on ‘Generate Video’ at the top right. It takes some time to generate your video. Your Own Animated AI Avatar https://youtu.be/b6fIbN2Ao4I?embedable=true Sources (& Tips) How to Make an AI Animated Avatar A Complete Tutorial Create Your Own AI Animated Avatar: A Step-by-Step Guide Bring Your MidJourney AI Avatar To Life (AI Animation) How to Create Your Own AI Animated Avatar How to Create AI Avatar with AI: A Step-by-Step Guide Create Your Own AI Avatar in Under 5 Min Also Published Here

Your Own Animated AI Avatar

Key Out of Time by Andre Alice Norton, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . The Sea Gate of the Foanna The Sea Gate of the Foanna Ross raised a shell cup to his lips but hardly sipped the fiery brew it contained. This was a gesture of ceremony, but he wanted a steady head and a quick tongue for any coming argument. Torgul, Afrukta, Ongal—the three commanders of the Rover cruisers; Jazia, who represented the mysterious Power of Phutka; Vistur and some other subordinate officers; Karara; himself, with Loketh hovering behind: a council of war. But summoned against whom? The Terran had come too far afield from his own purpose—to reach Ashe in the Foanna keep. And to further his own plans was a task he doubted his ability to perform. His attack on the Baldies had made him too important to the Rovers for them to allow him willingly to leave them on a quest of his own. "These star men"—Ross set down the cup, tried to choose the most telling words in his limited Hawaikan vocabulary—"possess weapons and powers you can not dream of, that you have no defense against. Back at Kyn Add we were lucky. The salkars attacked their sub and halted the broadcast powering their flamers. Otherwise we could not have taken them, even though we were many against their few. Now you talk of hunting them in their own territory—on land and in the mountains where they have their base. That would be folly akin to swimming barehanded to front a salkar." "So—then we must sit and wait for them to eat us up?" flared Ongal. "I say it is better to die fighting with one's blade wet!" "Do you not also wish to take at least one of the enemy with you when you fight to that finish?" Ross countered. "These could kill you before you came in blade range." "You had no trouble with that weapon of yours," Afrukta spoke up. "I have told you—this weapon was stolen from them. I have only one and I do not know how long it will continue to serve me, or whether they have a defense against it. Those we took were naked to any force, for their broadcast had failed them. But to smash blindly against their main base would be the act of madmen." "The salkars opened a way for us—" That was Torgul. "But we can not move a pack of those inland to the mountains," Vistur pointed out reasonably. Ross studied the Captain. That Torgul was groping for a plan and that it had to be a shrewd one, the Terran guessed. His respect for the Rover commander had been growing steadily since their first meeting. The cruiser-raiders had always been captained by the most daring men of the Rover clans. But Ross was also certain that a successful cruiser commander must possess a level-headed leaven of intelligence and be a strategist of parts. The Hawaikan force needed a key which would open the Baldy base as the salkars had opened the lagoon. And all they had to aid them was a handful of facts gained from their prisoners. Oddly enough the picklock to the captives' minds had been produced by the dolphins. Just as Tino-rau and Taua had formed a bridge of communication between the Terran and Loketh, so did they read and translate the thoughts of the galactic invaders. For the Baldies, among their own kind, were telepathic, vocalizing only to give orders to inferiors. Their capture by these primitive "inferiors" had delivered the first shock, and the mind-probes of the dolphins had sent the "supermen" close to the edge of sanity. To accept an animal form as an equal had been shattering. But the star men's thoughts and memories had been winnowed at last and the result spread before this impromptu council. Rovers and Terrans were briefed on the invaders' master plan for taking over a world. Why they desired to do so even the dolphins had not been able to discover; perhaps they themselves had not been told by their superiors. It was a plan almost contemptuous in its simplicity, as if the galactic force had no reason to fear effective opposition. Except in one direction—one single direction. Ross's fingers tightened on the shell cup. Had Torgul reached that conclusion yet, the belief that the Foanna could be their key? If so, they might be able to achieve their separate purposes in one action. "It would seem that they are wary of the Foanna," he suggested, alert to any telltale response from Torgul. But it was Jazia who answered the Terran's half question. "The Foanna have a powerful magic; they can order wind and wave, man and creature—if so be their will. Well might these killers fear the Foanna!" "Yet now they move against them," Ross pointed out, still eyeing Torgul. The Captain's reply was a small, quiet smile. "Not directly, as you have heard. It is all a part of their plan to set one of us against the other, letting us fight many small wars and so use up our men while they take no risks. They wait the day when we shall be exhausted and then they will reveal themselves to claim all they wish. So today they stir up trouble between the Wreckers and the Foanna, knowing that the Foanna are few. Also they strive in turn to anger us by raids, allowing us to believe that either the Wreckers or Foanna have attacked. Thus—" he held up his left thumb, made a pincers of right thumb and forefinger to close upon it, "they hope to catch the Foanna, between Wreckers and Rovers. Because the Foanna are those they reckon the most dangerous they move against them now, using us and weakening our forces into the bargain. A plan which is clever, but the plan of men who do not like to fight with their own blades." "They are worse than the coast scum, these cowards!" Ongal spat. Torgul smiled again. "That is what they believe we will say, kinsman, and so underrate them. By our customs, yes, they are cowards. But what care they for our judgments? Did we think of the salkars when we used them to force the lagoon? No, they were only beasts to be our tools. So now it is the same with us, except that we know what they intend. And we shall not be such obedient tools. If the Foanna are our answer, then—" He paused, gazing into his cup as if he could read some shadowy future there. "If the Foanna are the answer, then what?" Ross pushed. "Instead of fighting the Foanna, we must warm, cherish, try to ally ourselves with them. And do all that while we still have time!" "Just how do we do these things?" demanded Ongal. "The Foanna you would warn, cherish, claim as allies, are already our enemies. Were we not on the way to force their sea gate only days ago? There is no chance of seeking peace now. And have the finned ones not learned from the women-killers that already there is an army of Wreckers camped about the citadel to which these sons of the Shadow plan to lend certain weapons? Do we throw away three cruisers—all we have left—in a hopeless fight? Such is the council of one struck by loss of wits." "There is a way—my way," Ross seized the opening. "In the Foanna citadel is my sword-lord, to whose service I am vowed. We were on our way to attempt his freeing when your ship picked us out of the waves. He is learned beyond me in the dealing with strange peoples, and if the Foanna are as clever as you say, they will already have discovered that he is not just a slave they claimed from Lord Zahur." There it was in the open, his own somewhat tattered hope that Ashe had been able to impress his captors with his knowledge and potential. Trained to act as contact man with other races, there was a chance that Gordon had saved himself from whatever fate had been planned for the prisoners the Foanna had claimed. If that happened, Ashe could be their opening wedge in the Foanna stronghold. "This also I know: That which guards the gate—which turns your minds whirling and sent you back from your raid—does not affect me. I may be able to win inside and find my clansman, and in that doing treat with the Foanna." The Baldy prisoners had not underestimated the attack on the Foanna citadel. As the Rover cruisers beat in under the cover of night the fires and torches of both besieged and besiegers made a wild glow across the sky. Only on the sea side of the fortress there was no sign of involvement. Whatever guarded the gate must still be in force. Ross stood with his feet well apart to balance his body against the swing of the deck. His suggestion had been argued over, protested, but at last carried with the support of Torgul and Jazia, and now he was to make his try. The sum of the Rovers' and Loketh's knowledge of the sea gate had been added for his benefit, but he knew that this venture must depend upon himself alone. Karara, the dolphins, the Hawaikans, were all too sensitive to the barrier. Torgul moved in the faint light. "We are close; our power is ebbing. If we advance, we shall be drifting soon." "It is time then." Ross crossed to the rope ladder, but another was there before him. Karara perched on the rail. He regarded her angrily. "You can't go." "I know. But we are still safe here. Just because you are free of one defense of the gate, Ross, do not believe that makes it easy." He was stung by her assumption that he could be so self-assured. "I know my business." Ross pushed past her, swinging down the rope ladder, pausing only above water level to snap on flippers, make sure of the set of his weighted belt, and slide his gill-mask over his face. There was a splash beside him as the net containing spare belt, flippers, and mask hit the water and he caught at it. These could provide Ashe's escape from the fortress. The lights on the shore made a wide arc of radiance across the sea. As Ross headed toward the wave-washed coast he began to hear shouting and other sounds which made him believe that the besiegers were in the midst of an all-out assault. Yet those distant fires and rocketlike blasts into the sky had a wavery blur. And Ross, making his way with the effortless water cleaving of the diver, surfaced now and then to spot film curling up from the surface of the sea between the two standing rock pillars which marked the sea gate. He was startled by a thunderous crack, rending the air above the small bay. Ross pulled to one of the pillars, steadied himself with one hand against it. Those twists of film rising from the surging surface were thickening. More tendrils grew out from parent stems to creep along above the waves, raising up sprouts and branches in turn. A wall of mist was building between gate and shore. Again a thunderclap overhead. Involuntarily the Terran ducked. Then he turned his face up to the sky, striving to see any evidence of storm. What hung there sped the growth of the fog on the water. Yet where the fog was gray-white, it was a darkness spouting from the highest point of the citadel. Ross could not explain how he was able to see one shade of darkness against equal dusk, but he did—or did he only sense it? He shook his head, willing himself to look away from the finger. Only it was a finger no longer; now it was a fist aimed at the stars it was fast blotting out. A fist rising to the heavens before it curled back, descended to press the fortress and its surroundings into rock and earth. Fog curled about Ross, spilled outward through the sea gates. He loosed his grip on the pillar and dived, swimming on through the gap with the fortress of the Foanna before him. There was a jetty somewhere ahead; that much he knew from Torgul's description. Those who served the Foanna sometimes took sea roads and they had slim, fast cutters for such coastwise travel. Ross surfaced cautiously, to discover there was no visibility to wave level. Here the mist was thick, a smothering cover so bewildering he was confused as to direction. He ducked below again and flippered on. Was his confusion born of the fog, or was it also in his head? Did he, after all, have this much reaction to the gate defense? Ross ducked that suspicion as he had ducked the moist blanket on the surface. He had come from the gate, which meant that the jetty must lie—there! A few moments later Ross had proof that his sense of direction had not altogether failed him, when his shoulder grazed against a solid obstruction in the water and his exploring touch told him that he had found one of the jetty piles. He surfaced again and this time he heard not a thunder roll but the singsong chanting of the Foanna. It was loud, almost directly above his head, but since the cotton mist held he was not afraid of being sighted. The chanter must be on the jetty. And to Ross's right was a dark bulk which he thought was one of the cutters. Was a sortie by the besieged being planned? Then, out of the night, came a dazzling beam, well above the level of Ross's head where he clung to the piling. It centered on the cutter, slicing into the substance of the vessel with the ease of steel piercing clay. The chanting stopped on mid-note, broken by cries of surprise and alarm. Ross, pressing against the pile, received a jolt from his belt sonic. There must be a Baldy sub in the basin inside the gate. Perhaps the flame beam now destroying the cutter was to be turned on the walls of the keep in turn. Foanna chant again, low and clear. Splashes from the water as those on the jetty cast into the sea objects Ross could not define. The Terran's body jerked, his mask smothered a cry of pain. About his legs and middle, immersed in the waves, there was cold so intense that it seared. Fear goaded him to pull up on one of the under beams of the pier. He reached that refuge and rubbed his icy legs with what vigor he could summon. Moments later he crept along toward the shore. The energy ray had found another target. Ross paused to watch a second cutter sliced. If the counter stroke of the Foanna would rout the invaders, it had not yet begun to work. The net holding the extra gear brought along in hopes of Ashe's escape weighed the Terran down, but he would not abandon it as he felt his way from one foot- and hand-hold to the next. The waves below gave off an icy exudation which made him shiver uncontrollably. And he knew that as long as that effect lasted he dared not venture into the sea again. Light ... along with the cold, there was a phosphorescence on the water—white patches floating, dipping, riding the waves. Some of them gathered under the pier, clustering about the pilings. And the fog thinned with their coming, as if those irregular blotches absorbed and fed upon the mist. The Terran could see now he had reached the land end of the jetty. He wedged his flippers into his belt, pulled on over his feet the covers of salkar-hide Torgul had provided. Save for his belt, his trunks, and the gill-pack, Ross's body was bare and the cold caught at him. But, slinging the carry net over his shoulder, he dropped to the damp sand and stood listening. The clamor of the attack which had carried all the way offshore to the Rover cruisers had died away. And there were no more claps of thunder. Instead, there was now a thick wash of rain. No more fire rays as he faced seaward. And the fog was lifting, so Ross could distinguish the settling cutters, their bows still moored to the jetty. There was no movement there. Had those on the pier fled? Dot ... dash ... dot ... Ross did not drop the net. But he crouched back in the half protection of the piling. For a moment which stretched beyond Terran time measure he froze so, waiting. Dot ... dash ... dot ... Not the prickle induced by the enemy installations, it was a real coded call picked up by his sonic, and one he knew. Don't rush, he told himself sharply—play it safe. By rights only two people in this time and place would know that call. And one would have no reason to use it. But—a trap? This could be a trap. Awe of the Foanna powers had touched him a little in spite of his off-world skepticism. He could be lured now by someone using Ashe's call. Ross stripped for action after a fashion, bundling the net and its contents into a hollow he scooped behind a pile well above water level. The alien hand weapon he had left with Karara, not trusting it to the sea. But he had his diver's knife and his two hands which, by training, could be, and had been, deadly weapons. With the sonic against the bare skin of his middle where it would register strongest, knife in hand, Ross moved into the open. The floating patches did not supply much light, but he was certain the call had come from the jetty. There was movement there—a flash or two. And the sonic? Ross had to be sure, very sure. The broadcast was certainly stronger when he faced in that direction. Dared he come into the open? Perhaps in the dark he could cut Ashe away from his captors so they could swim for it together. Ross clicked a code reply. Dot ... dot ... dot ... The answer was quick, imperative: "Where?" Surely no one but Ashe could have sent that! Ross did not hesitate. "Be ready—escape." "No!" Even more imperative. "Friends here...." Had he guessed rightly? Had Ashe established friendly relations with the Foanna? But Ross kept to the caution which had been his defense and armor so long. There was one question he thought only Ashe could answer, something out of the past they had shared when they had made their first journey into time disguised as Beaker traders of the Bronze Age. Deliberately he tapped that question. "What did we kill in Britain?" Tensely he waited. But when the reply came it did not pulse from the sonic under his fingers; instead, a well-remembered voice called out of the night. "A white wolf." And the words were Terran English. "Ashe!" Ross leaped forward, climbed toward the figure he could only dimly see. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Andre Alice Norton (2006). Key Out of Time. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/19651/pg19651-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Sea Gate of the Foanna

If you dive into the meeting rooms of most good corporate innovation and start-up product teams, you will likely find some similar rituals and conversations. Some type of build-measure-learn loop with a sprinkle of gut-feel and product intuition. This begs the question: if everyone is innovating in a similar way, how does one get the edge over their competitors? As with most winning teams, the devil (and success) is in the details. But when you’re in the weeds, relentlessly executing, it’s often hard to zoom out and figure out how to fine tune performance. This is a critical exercise and also a big challenge. Thankfully, business leaders and Product Managers (including myself!) rely on experts like Mirav Vyas who has successfully advised a multitude of product and innovation teams across the UK, US and Africa over the last 8 years. With expertise on building products across industries including HealthTech, AgTech, and FinTech, Mirav is adamant that what separates good teams from the truly great teams are the little, often overlooked details: “It’s the degree to which teams are truly empowered by leadership to solve customer problems and how legitimately they operate in a cross-functional and collaborative way... not just say that they do.” When I first met Mirav to discuss approaches to product execution for my start-up, it was clear that he has an acute awareness of what drives performance. Mirav developed this nuanced perspective after building a very well-rounded professional experience. He has led and provided expert insight on the delivery of large scale B2B and B2C software products that have served millions of users. Mirav spent the first 5 years of his career at Deloitte UK in the Digital Strategy Consulting practice, where he specialised in Corporate Innovation. While at Deloitte, Mirav was in the founding team developing Deloitte Venture Path – a revolutionary proprietary innovation framework used today by corporate venture teams globally to launch new products and services. “ We spent countless hours refining the methodology to develop a framework that approaches innovation as a structured, scientific process.” With a passion to build innovative products and expertise with execution, Mirav went on to win corporate venture funding to join Deloitte’s internal incubator. Notably, as the only solo pitch winner. After Deloitte, Mirav was selected to participate in Antler, a highly selective (2% acceptance) and reputable global VC and Start-Up generator. During this time, Mirav worked on developing market-changing ideas in EdTech and CareerTech — going on to launch and lead Rize, a consumer app spear heading a new innovative “micro-mentorship” category. Rize was voted as a “Top 100 Start-up to Watch ” by TechRound, a leading Tech publication in the UK, for a remarkable use of voice technology to facilitate micro-mentorship at scale. With such broad experience and expertise in innovation across the corporate and start-up worlds, Mirav shares his 3 overlooked bad habits for product teams to watch out for… Bad Habit #1: Only Product Managers / User Researchers participate in Customer Discovery Scenario: There’s a big customer discovery push to validate some critical hypotheses. Management is eager to hear customer feedback to determine whether to proceed, pivot or kill an idea. The Product Manager (“PM”) has been busy syncing diaries and has scheduled 7 customer calls. She is leading them all, and is alone for all but one where a UX designer is free to join. Engineering is stretched and despite the invites, are unable to participate. The PM conducts the calls and translates pages of notes into a succinct set of key findings. A week after the call, the PM shares the findings with Management including Engineering and Design. So What? Working this way is ineffective. The PM becomes the sole conduit of customer feedback to the business. Managers in wider functions are tasked with turning second-hand information into actions. Regardless of how good the PM is, there are likely always going to be further clarification loops. For example, if Engineering questions the viability of a feature - which they often do. The result of this is lost time due to further clarification and customer feedback loops to get to an outcome. The Fix: Make sure Product Discovery calls have 3 key attendees: A PM, a Designer, and critically, an Engineer. Why? Because each one of those attendees ingests and solves problems differently. As leading product thought leader : “In the best teams products were conceived by true collaborations between engineers, designers and product managers.” Marty Cagan says “In my experience it is often the engineers who provide the best initial ideas. They know what the limits of the technology are and think within these parameters.” - Mirav. Bad Habit #2: Experiments are not strictly time boxed and there is no agreed burden of proof Scenario: The cross-functional product team is designing experiments to test key hypotheses about optimizing a core feature. There are a range of experiments being brainstormed to collect both quantitative and qualitative data. After some discussion the team decides on 3 experiments to launch in the following week, and to reconvene at some later date to discuss the results. So What? On the face of it, this seems like a sensible set of actions. However, the discussions are missing a few key elements. Firstly, the experiments are somewhat open ended in their duration. People have a rough idea based on previous experience, but nothing has been noted down. Will an experiment take one day? Two days? A week? A month? Tied to this is the fact that there has been no agreed burden of proof. Again, participants in the discussion may have vocalized “if x number of people opt in” or “if we see 2% increase in conversion” but nothing has been formally agreed. Moreover, in any cross-functional team, there are always a mix of optimists and pessimists each requiring a different burden of proof to be confident. In some cases, where there is enough data for quantitative experiments, there is a clear marker of statistical significance which can determine success. However, for many early-stage product teams or new ventures, this is not possible. The Fix: Get a healthy debate flowing about “What needs to be true for this experiment to be determined successful?” and timebox how long you have to gather data. This can seem tedious, but it’s worth the upfront effort to ensure everyone is clear on the goal and time available to hit it. Some simple yet effective tools I have seen teams use are experiment trackers, for example using Trello, where each experiment has a dedicated card with agreed details for all to see. strictly “I have seen these trackers used at both start-ups and Fortune 500 companies. Keep things simple and encourage debate around each experiment.” - Mirav. Bad Habit #3: Product metrics are only known by product teams Scenario: The PM works with leadership to agree on KPIs. The product team begins to execute and the team meets every week to track progress. They discuss the metrics and agree some actions / experiments to run in order to improve performance. The rest of the business has their own KPIs and have a vague awareness and understanding of how their work impacts the product metrics. So What? If people in a software company outside the core product team have limited awareness of how the product is performing, they can focus on the wrong things. Imagine someone in Marketing designing a campaign for a particular demographic without the knowledge that this demographic has a low 90-day retention rate. Or a Business Development representative prospecting a future customer without understanding which product features are most used and valuable to similar customers. Most teams would say “we do this anyway” but a true acid test is to see if people outside of the product team can reel off core product metrics without assistance. The Fix: This is perhaps most relevant for product-led growth businesses but, regardless of growth strategy, if your core product is software, then having everyone know how that software is performing can ensure all teams are focused on the right things. As renowned Venture Capitalist and Network Effects thought leader James Currier remarks: “When I go into companies I want to see everybody looking at triangle charts… how great is your daily stats email? Do you even have one? When I go into an office I love to see screens up on the wall with stats for everyone to see.” Check-out James’ talk here on what makes great teams. https://www.youtube.com/watch?v=FI52Z-fDM5c?embedable=true The above habits as Mirav states are: “As much about team culture and mindset vs. tactics. The tactics may fail but the mindset must remain intact.” Today, Mirav continues to share his expertise in innovation and product development by supporting multi-national companies and high growth start-ups via his own independent consulting practice. He continues to contribute to the European tech community as a mentor, advisor and speaker with the aim to empower innovators with the tools they need to be successful.

3 Habits that Stifle Product Execution According to a Product Expert

RIO DE JANEIRO

Love and Mr. Lewisham by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE SCANDALOUS RAMBLE CHAPTER VI. — THE SCANDALOUS RAMBLE. As soon as school was dismissed Lewisham made a gaol-delivery of his outstanding impositions, and hurried back to his lodgings, to spend the time until his dinner was ready—Well?... It seems hardly fair, perhaps, to Lewisham to tell this; it is doubtful, indeed, whether a male novelist’s duty to his sex should not restrain him, but, as the wall in the shadow by the diamond-framed window insisted, “Magna est veritas et prevalebit.” Mr. Lewisham brushed his hair with elaboration, and ruffled it picturesquely, tried the effect of all his ties and selected a white one, dusted his boots with an old pocket-handkerchief, changed his trousers because the week-day pair was minutely frayed at the heels, and inked the elbows of his coat where the stitches were a little white. And, to be still more intimate, he studied his callow appearance in the glass from various points of view, and decided that his nose might have been a little smaller with advantage.... Directly after dinner he went out, and by the shortest path to the allotment lane, telling himself he did not care if he met Bonover forthwith in the street. He did not know precisely what he intended to do, but he was quite clear that he meant to see the girl he had met in the avenue. He knew he should see her. A sense of obstacles merely braced him and was pleasurable. He went up the stone steps out of the lane to the stile that overlooked the Frobishers, the stile from which he had watched the Frobisher bedroom. There he seated himself with his arms, folded, in full view of the house. That was at ten minutes to two. At twenty minutes to three he was still sitting there, but his hands were deep in his jacket pockets, and he was scowling and kicking his foot against the step with an impatient monotony. His needless glasses had been thrust into his waistcoat pocket—where they remained throughout the afternoon—and his cap was tilted a little back from his forehead and exposed a wisp of hair. One or two people had gone down the lane, and he had pretended not to see them, and a couple of hedge-sparrows chasing each other along the side of the sunlit, wind-rippled field had been his chief entertainment. It is unaccountable, no doubt, but he felt angry with her as the time crept on. His expression lowered. He heard someone going by in the lane behind him. He would not look round—it annoyed him to think of people seeing him in this position. His once eminent discretion, though overthrown, still made muffled protests at the afternoon’s enterprise. The feet down the lane stopped close at hand. “Stare away,” said Lewisham between his teeth. And then began mysterious noises, a violent rustle of hedge twigs, a something like a very light foot-tapping. Curiosity boarded Lewisham and carried him after the briefest struggle. He looked round, and there she was, her back to him, reaching after the spiky blossoming blackthorn that crested the opposite hedge. Remarkable accident! She had not seen him! In a moment Lewisham’s legs were flying over the stile. He went down the steps in the bank with such impetus that it carried him up into the prickly bushes beside her. “Allow me,” he said, too excited to see she was not astonished. “Mr. Lewisham!” she said in feigned surprise, and stood away to give him room at the blackthorn. “Which spike will you have?” he cried, overjoyed. “The whitest? The highest? Any!” “That piece,” she chose haphazard, “with the black spike sticking out from it.” A mass of snowy blossom it was against the April sky, and Lewisham, straggling for it—it was by no means the most accessible—saw with fantastic satisfaction a lengthy scratch flash white on his hand, and turn to red. “Higher up the lane,” he said, descending triumphant and breathless, “there is blackthorn.... This cannot compare for a moment....” She laughed and looked at him as he stood there flushed, his eyes triumphant, with an unpremeditated approval. In church, in the gallery, with his face foreshortened, he had been effective in a way, but this was different. “Show me,” she said, though she knew this was the only place for blackthorn for a mile in either direction. “I knew I should see you,” he said, by way of answer, “I felt sure I should see you to-day.” “It was our last chance almost,” she answered with as frank a quality of avowal. “I’m going home to London on Monday.” “I knew,” he cried in triumph. “To Clapham?” he asked. “Yes. I have got a situation. You did not know that I was a shorthand clerk and typewriter, did you? I am. I have just left the school, the Grogram School. And now there is an old gentleman who wants an amanuensis.” “So you know shorthand?” said he. “That accounts for the stylographic pen. Those lines were written.... I have them still.” She smiled and raised her eyebrows. “Here,” said Mr. Lewisham, tapping his breast-pocket. “This lane,” he said—their talk was curiously inconsecutive—“some way along this lane, over the hill and down, there is a gate, and that goes—I mean, it opens into the path that runs along the river bank. Have you been?” “No,” she said. “It’s the best walk about Whortley. It brings you out upon Immering Common. You must—before you go.” “Now?” she said with her eyes dancing. “Why not?” “I told Mrs. Frobisher I should be back by four,” she said. “It’s a walk not to be lost.” “Very well,” said she. “The trees are all budding,” said Mr. Lewisham, “the rushes are shooting, and all along the edge of the river there are millions of little white flowers floating on the water, I don’t know the names of them, but they’re fine.... May I carry that branch of blossom?” As he took it their hands touched momentarily ... and there came another of those significant gaps. “Look at those clouds,” said Lewisham abruptly, remembering the remark he had been about to make and waving the white froth of blackthorn, “And look at the blue between them.” “It’s perfectly splendid. Of all the fine weather the best has been kept for now. My last day. My very last day.” And off these two young people went together in a highly electrical state—to the infinite astonishment of Mrs. Frobisher, who was looking out of the attic window—stepping out manfully and finding the whole world lit and splendid for their entertainment. The things they discovered and told each other that afternoon down by the river!—that spring was wonderful, young leaves beautiful, bud scales astonishing things, and clouds dazzling and stately!—with an air of supreme originality! And their naove astonishment to find one another in agreement upon these novel delights! It seemed to them quite outside the play of accident that they should have met each other. They went by the path that runs among the trees along the river bank, and she must needs repent and wish to take the lower one, the towing path, before they had gone three hundred yards. So Lewisham had to find a place fit for her descent, where a friendly tree proffered its protruding roots as a convenient balustrade, and down she clambered with her hand in his. Then a water-vole washing his whiskers gave occasion for a sudden touching of hands and the intimate confidence of whispers and silence together. After which Lewisham essayed to gather her a marsh mallow at the peril, as it was judged, of his life, and gained it together with a bootful of water. And at the gate by the black and shiny lock, where the path breaks away from the river, she overcame him by an unexpected feat, climbing gleefully to the top rail with the support of his hand, and leaping down, a figure of light and grace, to the ground. They struck boldly across the meadows, which were gay with lady’s smock, and he walked, by special request, between her and three matronly cows—feeling as Perseus might have done when he fended off the sea-monster. And so by the mill, and up a steep path to Immering Common. Across the meadows Lewisham had broached the subject of her occupation. “And are you really going away from here to be an amanuensis?” he said, and started her upon the theme of herself, a theme she treated with a specialist’s enthusiasm. They dealt with it by the comparative methods and neither noticed the light was out of the sky until the soft feet of the advancing shower had stolen right upon them. “Look!” said he. “Yonder! A shed,” and they ran together. She ran laughing, and yet swiftly and lightly. He pulled her through the hedge by both hands, and released her skirt from an amorous bramble, and so they came into a little black shed in which a rusty harrow of gigantic proportions sheltered. He noted how she still kept her breath after that run. She sat down on the harrow and hesitated. “I must take off my hat,” she said, “that rain will spot it,” and so he had a chance of admiring the sincerity of her curls—not that he had ever doubted them. She stooped over her hat, pocket-handkerchief in hand, daintily wiping off the silvery drops. He stood up at the opening of the shed and looked at the country outside through the veil of the soft vehemence of the April shower. “There’s room for two on this harrow,” she said. He made inarticulate sounds of refusal, and then came and sat down beside her, close beside her, so that he was almost touching her. He felt a fantastic desire to take her in his arms and kiss her, and overcame the madness by an effort. “I don’t even know your name,” he said, taking refuge from his whirling thoughts in conversation. “Henderson,” she said. “Miss Henderson?” She smiled in his face—hesitated. “Yes—Miss Henderson.” Her eyes, her atmosphere were wonderful. He had never felt quite the same sensation before, a strange excitement, almost like a faint echo of tears. He was for demanding her Christian name. For calling her “dear” and seeing what she would say. He plunged headlong into a rambling description of Bonover and how he had told a lie about her and called her Miss Smith, and so escaped this unaccountable emotional crisis.... The whispering of the rain about them sank and died, and the sunlight struck vividly across the distant woods beyond Immering. Just then they had fallen on a silence again that was full of daring thoughts for Mr. Lewisham. He moved his arm suddenly and placed it so that it was behind her on the frame of the harrow. “Let us go on now,” she said abruptly. “The rain has stopped.” “That little path goes straight to Immering,” said Mr. Lewisham. “But, four o’clock?” He drew out his watch, and his eyebrows went up. It was already nearly a quarter past four. “Is it past four?” she asked, and abruptly they were face to face with parting. That Lewisham had to take “duty” at half-past five seemed a thing utterly trivial. “Surely,” he said, only slowly realising what this parting meant. “But must you? I—I want to talk to you.” “Haven’t you been talking to me?” “It isn’t that. Besides—no.” She stood looking at him. “I promised to be home by four,” she said. “Mrs. Frobisher has tea....” “We may never have a chance to see one another again.” “Well?” Lewisham suddenly turned very white. “Don’t leave me,” he said, breaking a tense silence and with a sudden stress in his voice. “Don’t leave me. Stop with me yet—for a little while.... You ... You can lose your way.” “You seem to think,” she said, forcing a laugh, “that I live without eating and drinking.” “I have wanted to talk to you so much. The first time I saw you.... At first I dared not.... I did not know you would let me talk.... And now, just as I am—happy, you are going.” He stopped abruptly. Her eyes were downcast. “No,” she said, tracing a curve with the point of her shoe. “No. I am not going.” Lewisham restrained an impulse to shout. “You will come to Immering?” he cried, and as they went along the narrow path through the wet grass, he began to tell her with simple frankness how he cared for her company, “I would not change this,” he said, casting about for an offer to reject, “for—anything in the world.... I shall not be back for duty. I don’t care. I don’t care what happens so long as we have this afternoon.” “Nor I,” she said. “Thank you for coming,” he said in an outburst of gratitude.—“Oh, thank you for coming,” and held out his hand. She took it and pressed it, and so they went on hand in hand until the village street was reached. Their high resolve to play truant at all costs had begotten a wonderful sense of fellowship. “I can’t call you Miss Henderson,” he said. “You know I can’t. You know ... I must have your Christian name.” “Ethel,” she told him. “Ethel,” he said and looked at her, gathering courage as he did so. “Ethel,” he repeated. “It is a pretty name. But no name is quite pretty enough for you, Ethel ... dear.”... The little shop in Immering lay back behind a garden full of wallflowers, and was kept by a very fat and very cheerful little woman, who insisted on regarding them as brother and sister, and calling them both “dearie.” These points conceded she gave them an admirable tea of astonishing cheapness. Lewisham did not like the second condition very much, because it seemed to touch a little on his latest enterprise. But the tea and the bread and butter and the whort jam were like no food on earth. There were wallflowers, heavy scented, in a jug upon the table, and Ethel admired them, and when they set out again the little old lady insisted on her taking a bunch with her. It was after they left Immering that this ramble, properly speaking, became scandalous. The sun was already a golden ball above the blue hills in the west—it turned our two young people into little figures of flame—and yet, instead of going homeward, they took the Wentworth road that plunges into the Forshaw woods. Behind them the moon, almost full, hung in the blue sky above the tree-tops, ghostly and indistinct, and slowly gathered to itself such light as the setting sun left for it in the sky. Going out of Immering they began to talk of the future. And for the very young lover there is no future but the immediate future. “You must write to me,” he said, and she told him she wrote such silly letters. “But I shall have reams to write to you,” he told her. “How are you to write to me?” she asked, and they discussed a new obstacle between them. It would never do to write home—never. She was sure of that with an absolute assurance. “My mother—” she said and stopped. That prohibition cut him, for at that time he had the makings of a voluminous letter-writer. Yet it was only what one might expect. The whole world was unpropitious—obdurate indeed.... A splendid isolation ` deux. Perhaps she might find some place where letters might be sent to her? Yet that seemed to her deceitful. So these two young people wandered on, full of their discovery of love, and yet so full too of the shyness of adolescence that the word “Love” never passed their lips that day. Yet as they talked on, and the kindly dusk gathered about them, their speech and their hearts came very close together. But their speech would seem so threadbare, written down in cold blood, that I must not put it here. To them it was not threadbare. When at last they came down the long road into Whortley, the silent trees were black as ink and the moonlight made her face pallid and wonderful, and her eyes shone like stars. She still carried the blackthorn from which most of the blossoms had fallen. The fragrant wallflowers were fragrant still. And far away, softened by the distance, the Whortley band, performing publicly outside the vicarage for the first time that year, was playing with unctuous slowness a sentimental air. I don’t know if the reader remembers it that, favourite melody of the early eighties:— “Sweet dreamland faces, passing to and fro, (pum, pum) Bring back to Mem’ry days of long ago-o-o-oh,” was the essence of it, very slow and tender and with an accompaniment of pum, pum. Pathetically cheerful that pum, pum, hopelessly cheerful indeed against the dirge of the air, a dirge accentuated by sporadic vocalisation. But to young people things come differently. “I love music,” she said. “So do I,” said he. They came on down the steepness of West Street. They walked athwart the metallic and leathery tumult of sound into the light cast by the little circle of yellow lamps. Several people saw them and wondered what the boys and girls were coming to nowadays, and one eye-witness even subsequently described their carriage as “brazen.” Mr. Lewisham was wearing his mortarboard cap of office—there was no mistaking him. They passed the Proprietary School and saw a yellow picture framed and glazed, of Mr. Bonover taking duty for his aberrant assistant master. And outside the Frobisher house at last they parted perforce. “Good-bye,” he said for the third time. “Good-bye, Ethel.” She hesitated. Then suddenly she darted towards him. He felt her hands upon his shoulders, her lips soft and warm upon his cheek, and before he could take hold of her she had eluded him, and had flitted into the shadow of the house. “Good-bye,” came her sweet, clear voice out of the shadow, and while he yet hesitated an answer, the door opened. He saw her, black in the doorway, heard some indistinct words, and then the door closed and he was alone in the moonlight, his cheek still glowing from her lips.... So ended Mr. Lewisham’s first day with Love. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2006). Love and Mr. Lewisham. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/11640/11640-h/11640-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html

THE SCANDALOUS RAMBLE

THE ENTERTAINMENT OF MR. DIRECK REACHES A CLIMAX

Photo Credit, inputting " pixelated green clock on a laptop screen in san francisco " via DALL-E . by richard-kubina, Guy, David, Limarc and Sheharyar Khan occurred in hackernoon's official #random channel. It is about the first couple hundred images we generated with . This Slogging thread DALL-E richard-kubina Jul 23, 2022, 5:44 PM I've got access to Dall-e :excited: Guy Jul 23, 2022, 7:42 PM Bruh where’s my beta key?!?! David Jul 24, 2022, 3:52 AM Those featured images - gonna be dope! richard-kubina Jul 24, 2022, 11:10 PM https://dallery.gallery/wp-content/uploads/2022/07/The-DALL%C2%B7E-2-prompt-book-v1.01.pdf Guy Jul 28, 2022, 12:48 AM :party_parrot: David Jul 28, 2022, 5:38 AM So for image creation, is it groundbreaking technology or not? Guy Jul 28, 2022, 12:07 PM From what I’ve messed around with it’s pretty good! $15 for 115 image generations so not tooo pricey either! Seems to be a bit more consistent than GPT3 too Guy Jul 28, 2022, 1:55 PM Revolutionary! 0.13$ per image range David Jul 29, 2022, 4:56 AM Ten more tools https://twitter.com/guyp/status/1552681939806691329?s=21&t=ZXF6oS2oh8QvmAIJdXrvJw David Jul 29, 2022, 4:57 AM DALL E has impressive floor. Almost every image feels a little like a human made it David Aug 30, 2022, 7:07 PM David Aug 30, 2022, 8:54 PM David Aug 30, 2022, 8:55 PM David Aug 30, 2022, 8:55 PM David Aug 30, 2022, 8:56 PM David Aug 30, 2022, 8:56 PM David Aug 30, 2022, 8:56 PM David Aug 30, 2022, 8:57 PM David Aug 30, 2022, 8:57 PM David Aug 30, 2022, 8:57 PM David Aug 30, 2022, 8:57 PM David Aug 30, 2022, 8:58 PM David Aug 30, 2022, 8:58 PM David Aug 30, 2022, 8:59 PM David Aug 30, 2022, 8:59 PM David Aug 30, 2022, 8:59 PM David Aug 30, 2022, 9:01 PM Limarc Aug 31, 2022, 4:45 AM This looks like a lot of fun Sheharyar Khan Aug 31, 2022, 3:38 PM Since Dall.E couldn't generate an accurate picture of you David, here's Turnstile vocalist Brendan Yates, who I think comes . https://www.google.com/search?q=Brendan+Yates&client=firefox-b-d&sxsrf=ALiCzsZ3aQ77IocJY8gao_CR-srnKGXNFQ:1661960185620&source=lnms&tbm=isch&sa=X&ved=2ahUKEwj0gfqTtPH5AhXJ8bsIHWi1DHQQ_AUoAXoECAIQAw&biw=1920&bih=927&dpr=1#imgrc=yf1ABBCoDMyUnM David Aug 31, 2022, 5:31 PM Sheharyar Khan Ha! That's a new one. Last 'celebrity' lookalike someone called me was https://www.google.com/search?q=Milo%C5%A1+Teodosi%C4%87&newwindow=1&client=firefox-b-d&hl=en&sxsrf=ALiCzsa-WuCP_p8ISXM36EguvClgWWhoyw:1661967043844&source=lnms&tbm=isch&sa=X&ved=2ahUKEwii-5razfH5AhWOAjQIHU11ApkQ_AUoAnoECAIQBA&biw=1364&bih=790&dpr=1.8 David Aug 31, 2022, 5:33 PM Limarc it is fun. can try it out here https://labs.openai.com/waitlist David Aug 31, 2022, 5:45 PM David Aug 31, 2022, 5:51 PM David Aug 31, 2022, 5:52 PM David Aug 31, 2022, 5:56 PM David Aug 31, 2022, 6:04 PM David Aug 31, 2022, 6:13 PM David Aug 31, 2022, 6:16 PM David Aug 31, 2022, 6:49 PM David Aug 31, 2022, 7:09 PM Limarc Aug 31, 2022, 7:26 PM Oh that's interesting they don't want you to use it to create deep fakes of famous people richard-kubina Aug 31, 2022, 7:34 PM yeah, I've seen folks using stable diffusion to do that though https://www.reddit.com/r/MediaSynthesis/comments/x2j01s/modern_celebrities_alternate_timelines/ David Aug 31, 2022, 9:20 PM David Aug 31, 2022, 9:47 PM David Aug 31, 2022, 10:58 PM David Aug 31, 2022, 11:00 PM David Aug 31, 2022, 11:01 PM David Aug 31, 2022, 11:03 PM This one has credible Adult Swim vibes for a show called something like "Suzette & Steed" David Aug 31, 2022, 11:04 PM Sidenote: Who owns this image? Me or . My words. Their tech. On the free trial plan... http://OpenAi.com David Aug 31, 2022, 11:08 PM David Aug 31, 2022, 11:09 PM David Aug 31, 2022, 11:18 PM David Aug 31, 2022, 11:19 PM David Aug 31, 2022, 11:21 PM David Aug 31, 2022, 11:22 PM David Aug 31, 2022, 11:24 PM David Aug 31, 2022, 11:25 PM Ok got to my first viable turn company logo into featured image art about company moment David Aug 31, 2022, 11:27 PM I can't seem to find the right way to tell the DALL-E it's noon time! David Aug 31, 2022, 11:29 PM David Aug 31, 2022, 11:30 PM David Sep 1, 2022, 3:00 AM David Sep 1, 2022, 3:04 AM David Sep 1, 2022, 3:04 AM

Is DALL-E Groundbreaking Technology or Not? Costs ≈0.13$ Per Image and More DALL-E 2 Access Details

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