00;00;00;00 - 00;00;05;20
Speaker 1
Gemini. I'm recording this for a podcast. Is that okay?
00;00;05;22 - 00;00;10;07
Speaker 2
Okay, I can help with that. What kind of podcast are you thinking of starting?
00;00;10;09 - 00;00;29;07
Speaker 1
Okay, so we're actually I was actually in the middle of a two parter with, ChatGPT on something else, but, we're going to not do that because I had an idea that I wanted to explore. For for make something for the studio.
00;00;29;09 - 00;00;43;11
Speaker 2
Okay, that sounds interesting. So you're thinking of having a podcast where you and a friend explore different creative projects? That's a fun idea. We can brainstorm some names that would like that. Do you want, one of the fun or more serious and professional?
00;00;43;12 - 00;00;55;24
Speaker 1
And I start what I wanted to explore today was, a type of probably image generating, but.
00;00;55;27 - 00;01;11;05
Speaker 2
Okay, so you want to explore AI art generation for your podcast. That's a super trendy topic right now to make sure I'm on the right track. Is your podcast geared towards people who are already into AI art, or is it more for beginners who are curious about it?
00;01;11;07 - 00;01;32;13
Speaker 1
I'm creating something. I'm creating an image generator. I want to I want to learn about creating an image generator. Don't worry about the specifics about what it's for yet. Just. I want you to tell me about how I can create an image generator.
00;01;32;15 - 00;01;49;27
Speaker 2
Okay. I can definitely help with that. Creating an image generator is a pretty complex project, but it's totally doable. There are a few different ways you could go about it. Would you prefer to use a preexisting model and fine tune it, or build one from scratch?
00;01;50;00 - 00;01;56;02
Speaker 1
I'm interested in llama. The the metal one.
00;01;56;04 - 00;02;17;03
Speaker 2
Okay. Using metas llama model is a great starting point. It's a powerful language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Now, to turn the llama into an image generator, you'll need to combine it with another model that specializes in image generation. There are a few popular options like stable diffusion, dalle, or Midjourney.
00;02;17;09 - 00;02;23;03
Speaker 2
Do you have any experience working with AI models before?
00;02;23;05 - 00;02;39;10
Speaker 1
Well, I just recommended long term, because the image taken on, meta has always been fast. But it's getting more accurate now, which is kind of what I assumed would happen.
00;02;39;12 - 00;02;54;11
Speaker 2
Okay, that makes sense. LMR is known for its speed. The accuracy improvements you've seen are likely due to Meta's ongoing research and development in AI. They're constantly working to make their models better.
00;02;54;14 - 00;03;06;09
Speaker 1
Okay, so how do they do it? Like on the meta AI to they use the llama model or do they use something else?
00;03;06;12 - 00;03;22;01
Speaker 2
Meta AI uses a combination of models for image generation. They've developed their own state of the art models, but they also incorporate llama for the language processing part of understanding your text from.
00;03;22;03 - 00;03;41;28
Speaker 1
Okay, so, how difficult would it be to kind of clone, an image gen bot and make it kind of, work under a certain criteria?
00;03;42;00 - 00;04;07;11
Speaker 2
Cloning an image generation bot and customizing it to work under specific criteria is definitely achievable, but it's not a simple task. It would require a solid understanding of machine learning, especially generative models. You'd likely need to work with a framework like TensorFlow or PyTorch, and have access to a good amount of training data. Is this something you're looking to dive deep into, or are you more interested in the general concept?
00;04;07;13 - 00;04;36;19
Speaker 1
I want to make something for the studio, and I'm into I'm up to learning it. What I would like to make is I would like to make a, I guess, kind of what you would call is like a listening bot, and I would like it to kind of create images, maybe every minute to start based on the last minute of conversation.
00;04;36;22 - 00;04;57;21
Speaker 2
Okay, that's a cool idea. So you want to create a bot that listens to conversations and generates images based on what it hears. That's definitely a unique and creative project to make this happen. You'll need to break it down into a few steps. First, you'll need to capture the audio, then transcribe it into text. Next, you'll feed that text into your image generation model and finally display the image.
00;04;57;23 - 00;05;06;04
Speaker 2
Are you thinking of using a specific image generation model like Stable Diffusion or Dall-E?
00;05;06;06 - 00;05;31;17
Speaker 1
Okay, so we had capture audio and transcribe, which makes sense. And then probably make a good prompt out of a summary of the, one minute. So summarize, feed it into the model, which is actually, like breaking this down makes it way easier because I don't need to make a model. I can feed it into an existing model.
00;05;31;20 - 00;05;50;19
Speaker 2
Okay, so you've got the basic steps down. Capture audio, transcribe it, summarize it, and feed it to the model. That's a solid plan. Now for the image generation model you could use something like stable diffusion or deli. Both are powerful and can create amazing images from text prompts. Do you have any experience working with either of these models?
00;05;50;21 - 00;05;53;27
Speaker 1
No.
00;05;53;29 - 00;06;10;20
Speaker 2
Okay. No problem. Since you're new to image generation models, I'd recommend starting with stable diffusion. It's a bit more user friendly and has a great community around it. There are lots of tutorials and resources available to help you get started. Do you have any experience with coding or machine learning?
00;06;10;23 - 00;06;34;09
Speaker 1
No, but I enjoy learning it. Like the VS code chat bot and bolt new and stuff like that. Before it was never really practical, but now with with an AI that I can kind of walk through and like ask questions and like it shows me things about the code and stuff like that. It's a really convenient teacher.
00;06;34;09 - 00;06;50;06
Speaker 1
It's like having an expert coding person. I mean, obviously not as good as it, but like, like 80% there or like 70% there. And that's really all I needed.
00;06;50;09 - 00;07;08;11
Speaker 2
Okay. So you're looking for something that's not quite as advanced as stable diffusion, but still pretty good. That's totally doable. There are a few options out there that might fit the bill. One option is to use a pre-trained model from Huggingface. They have a huge library of models, and you can probably find something that's been trained on a specific data set that's relevant to your podcast.
00;07;08;14 - 00;07;21;12
Speaker 2
Another option is to use a cloud based image generation API. There are a few companies that offer this service, and they often have different pricing tiers depending on your needs.
00;07;21;15 - 00;07;36;27
Speaker 1
Know the same thing that I said before was kind of unrelated to that, but thank you for telling me about those easier ones, I think. I think I'm probably up to the challenge of working with stable diffusion.
00;07;37;00 - 00;07;55;09
Speaker 2
Okay. You're ready to dive into stable diffusion. That's awesome. It's a powerful tool, and I'm sure you'll create some amazing things with it. To get started, you'll need to install the stable diffusion software on your computer. There are a few different ways to do this, but the easiest is probably to use the web UI. It's a graphical interface that makes it easy to generate images.
00;07;55;14 - 00;08;16;08
Speaker 2
Once you have stable diffusion installed, you can start experimenting with different prompts. Try describing the scene you want to generate in detail and see what stable diffusion comes up with. You can also use negative prompts to tell stable diffusion what not to include in the image. Do you have any specific ideas in mind for the first image you want to generate?
00;08;16;10 - 00;08;20;26
Speaker 1
How quickly can it generate images?
00;08;20;29 - 00;08;42;11
Speaker 2
Okay, let's get you generating images quickly. The fastest way is probably using an online stable diffusion demo. There are a few websites that offer free demos, and they're a great way to experiment without having to install anything. One popular option is Dream Studio. It's a website created by stability AI, the company behind Stable Diffusion. Dream studio offers a free trial and it's super easy to use.
00;08;42;14 - 00;08;49;09
Speaker 2
You just type in your prompt and it generates an image for you. Would you like me to help you find some other online stable diffusion demos? No.
00;08;49;09 - 00;09;14;06
Speaker 1
Well, I think that we're having a little bit of misunderstandings between translations and stuff like that, but you're being very helpful and actually telling me about some things that I wouldn't have asked about. But I but I said something along the lines of like, how quickly can stable diffusion generate a single image? Is it like three seconds?
00;09;14;06 - 00;09;20;10
Speaker 1
Is it like a minute?
00;09;20;12 - 00;09;39;00
Speaker 2
Gotcha. You're wondering about the actual speed of image generation with stable diffusion. It really depends on a few things, like your computer's hardware. The image size, and the complexity of the prompt. Generally, it can take anywhere from a few seconds to a couple of minutes to generate a single image. Does that answer your question?
00;09;39;02 - 00;09;45;04
Speaker 1
How long? Again, without the whole description?
00;09;45;06 - 00;09;56;25
Speaker 2
Okay, without getting too technical. Stable diffusion can generate a single image in a matter of seconds. However, the exact time will depend on your computer's hardware.
00;09;56;27 - 00;10;12;24
Speaker 1
Interesting. I think I have, like, a decent, desktop computer. At least it's got, like a rising, five series. I want to say with, like, the integrated GPU.
00;10;12;26 - 00;10;25;01
Speaker 2
Okay, so with an integrated GPU, it might take a bit longer, maybe around 30s to a minute per image. It really depends on the specific Ryzen five model and how much Ram you have.
00;10;25;03 - 00;10;32;29
Speaker 1
Got. But if I wanted to like bump up the GPU, I could generate images real fast.
00;10;33;01 - 00;10;43;12
Speaker 2
Definitely a dedicated GPU will give you a huge speed boost. You could be looking at generating images in just a few seconds, depending on the GPU.
00;10;43;14 - 00;10;47;24
Speaker 1
How much Ram would I need?
00;10;47;26 - 00;11;03;18
Speaker 2
For stable diffusion? 16GB of Ram is generally recommended, but 32GB will give you a much smoother experience, especially if you're working with larger images or running other programs at the same time. How much do you currently have?
00;11;03;20 - 00;11;21;10
Speaker 1
Not that much. I think I might have 16, actually. Actually, I might have, yeah, I'm not sure. I gotta check my computer. Could be a 70. Not for.
00;11;21;13 - 00;11;31;21
Speaker 2
Okay. Eight gigabytes should be enough to at least get stable diffusion running. But you might run into some performance issues. You could try closing any unnecessary programs while you're generating images to free up some Ram.
00;11;31;23 - 00;11;52;17
Speaker 1
No, I probably just get more Ram if I didn't have enough. If I, if I have, like, two sticks of ram these days, I bought them like a year or two ago. Would those be, like, two eights? Probably, or two sixteens?
00;11;52;20 - 00;12;07;01
Speaker 2
Okay. If you bought the ram a year or two ago, it's likely either two eight gigabyte sticks or two 16 gigabyte sticks. You can easily check in your computer system information to see the exact amount. Would you like me to walk you through how to do that?
00;12;07;03 - 00;12;25;28
Speaker 1
No. We're getting a little bit off track. But I did not know that, you can generate images really fast if you are doing it locally, I guess. I don't know why I didn't think about that.
00;12;26;01 - 00;12;39;19
Speaker 2
Yeah. Running stable diffusion locally gives you way more control and speed. You can fine tune things to your liking and generate images much faster than relying on an online service.
00;12;39;21 - 00;12;59;03
Speaker 1
Cool. What's been what's happened in the field over the last, like 5 to 6 years? Have things gotten really a lot faster? Or if they've always been pretty quick? If this if you had the Ram.
00;12;59;05 - 00;13;14;18
Speaker 2
Stable diffusion has gotten way faster in the last few years when it first came out, generating an image could take several minutes, even with a decent setup. Now, with the latest updates and better hardware, you can get images in seconds. It's pretty amazing how far it's come.
00;13;14;20 - 00;13;28;08
Speaker 1
Okay, so what is an increase of speed? Not including like local hardware. What? What is caused by that?
00;13;28;10 - 00;13;42;12
Speaker 2
Okay, there are a few things that have made stable diffusion so much faster. Better algorithms definitely play a part. The software itself is more efficient, but also graphics cards have gotten way more powerful in recent years, and that really helps stable diffusion work. Its magic.
00;13;42;14 - 00;13;57;02
Speaker 1
Okay. That might be the way to go if you want to generate, like real time images of like, conversations.
00;13;57;05 - 00;14;10;16
Speaker 2
You could try using a powerful GPU and optimizing stable diffusion settings for speed. There are also some specialized software tools designed for real time image generation, but they can be a bit more complex to set up.
00;14;10;19 - 00;14;41;28
Speaker 1
Okay. And so I would love to have like real time capabilities, like this. But I think probably the way to start off would be to take the transcripts of our conversations and do it in a post like every minute chunk. But I'd like to I like it to upgrade it to life eventually.
00;14;42;00 - 00;15;02;04
Speaker 2
Okay. To get started with real time image generation from your conversations, you'll need a good speech to text tool to transcribe what's being said. Then you can feed those transcripts into stable diffusion to generate images. There might be a slight delay, but it'll be close to real time. Do you want to explore some speech to text options?
00;15;02;06 - 00;15;05;14
Speaker 1
Yes, please.
00;15;05;16 - 00;15;22;23
Speaker 2
Okay. For speech to text, you could use Google Cloud Speech to text or Amazon Transcribe. Both are pretty accurate and can handle real time audio. They have free tiers to get you started and we can explore their setup later.
00;15;22;25 - 00;15;28;19
Speaker 1
What are some open source options?
00;15;28;21 - 00;15;41;14
Speaker 2
For open source? You could try Mozilla, Deep Speech or CMU. Think they might need a bit more technical know how to set up, but they're powerful options. Do you have a preference for cloud based or open source solutions?
00;15;41;17 - 00;15;47;02
Speaker 1
What was the Mozilla one called in?
00;15;47;04 - 00;15;52;11
Speaker 2
It's called Mozilla Deep Speech. It's a solid choice for accurate transcriptions.
00;15;52;13 - 00;16;02;10
Speaker 1
Okay, I've heard of that before. Tell me about tell me about the top three open source options right now.
00;16;02;13 - 00;16;16;20
Speaker 2
Okay. Besides Mozilla deep speech, you could look into CMU Sphinx, which is known for being highly customizable. There's also VOC, which is lightweight and can even run on devices like Raspberry Pi. Do any of these sound like they might fit your needs?
00;16;16;23 - 00;16;36;07
Speaker 1
Mozilla deep speech probably because if I'm doing this, is probably going to be hooked in through my computer. Maybe I'll run like some lines back into the house. Like, like some high speed cables and HDMI or something like that.
00;16;36;10 - 00;16;50;02
Speaker 2
Okay. For a local setup with Mozilla Deep Speech, you'll need to install it on your computer and configure it to receive audio input. It can be a bit technical, but there are tutorials available to guide you through the process. Are you comfortable working with the command line?
00;16;50;04 - 00;16;54;11
Speaker 1
No, not at all.
00;16;54;14 - 00;17;06;16
Speaker 2
Okay. If you're not comfortable with the command line, we might want to explore some cloud based options that have easier interfaces. Would you like me to find some resources for setting up Google Cloud Speech to Text or Amazon Transcribe?
00;17;06;18 - 00;17;19;03
Speaker 1
Tell me more about, Mozilla, the speech and working in the command line.
00;17;20;05 - 00;17;34;10
Speaker 2
Sure. Besides Mozilla deep speech, you could try C, a new sphinx, which is known for being highly customizable. There's also Vostok, which is lightweight and can even run on devices like Raspberry Pi. Do any of these sound like they might fit your needs?
00;17;34;12 - 00;17;44;02
Speaker 1
I think of I'm going to learn more about this stuff. I'm going to have to get comfortable with the command line, which just makes sense.
00;17;44;04 - 00;17;55;15
Speaker 2
Okay, I understand. Working with the command line can be a bit daunting at first, but with a little practice, you'll get the hang of it. Would you like me to find some beginner friendly tutorials to help you get started?
00;17;55;18 - 00;18;05;05
Speaker 1
No, I just to I want to explore how you control, deep speech using the command line.
00;18;05;08 - 00;18;14;09
Speaker 2
Okay, let's explore speech to text options for cloud based solutions. Google Cloud Speech to Text and Amazon Transcribe are good choices for Gemini. They're accurate and can handle.
00;18;14;09 - 00;18;29;15
Speaker 1
With Gemini stuff. I would like to explore how to manipulate Mozilla deep speech using the command line.
00;18;29;18 - 00;18;47;14
Speaker 2
Okay, to manipulate Mozilla deep speech using the command line. You'll need to install it first. Then you can use commands to load audio files, transcribe them, and even fine tune the model. Is your audio in a specific format like WAV or MP3?
00;18;47;16 - 00;18;53;21
Speaker 1
Yeah. Probably lav.
00;18;53;23 - 00;19;10;04
Speaker 2
Okay. The WAV files are perfect. You can use the audio flag followed by the file path to transcribe them with deep speech. There are also flags for controlling the output format and other options. Would you like me to give you some example commands?
00;19;10;06 - 00;19;17;13
Speaker 1
What is the, Yeah, actually. Go. Please do that.
00;19;17;16 - 00;19;37;01
Speaker 2
Okay. Here's an example. Deep speech model output. Underscore graph PBM audio. My underscore audio link to dot wave. This will transcribe the audio in my underscore audio link to that WAV using your deep speech model. You can find more details about other flags in the Deep Speech documentation.
00;19;37;04 - 00;19;45;12
Speaker 1
Okay. Shit. I forgot where we were going to go with the conversation. Loading audio.
00;19;45;13 - 00;19;49;27
Speaker 2
Okay, so you started with transcribing your Gemini stuff.
00;19;49;29 - 00;20;11;10
Speaker 1
Tell me, I would like you to. I would like you to, kind of compare and contrast Mozilla deep speech with something like Google Cloud speech and why somebody would pick one and somebody would pick the other.
00;20;11;12 - 00;20;33;09
Speaker 2
Okay. Mozilla deep speech is great for local processing and privacy, but it might require more technical setup. Google Cloud Speech to text is a powerful cloud based option with high accuracy and scalability, but it relies on an internet connection. Which one you choose depends on your needs and technical skills.
00;20;33;11 - 00;20;42;21
Speaker 1
Okay. Tell me more about, speech to text on Google Cloud speech.
00;20;42;23 - 00;20;59;06
Speaker 2
Google cloud speech to text is a powerful service with high accuracy, especially for noisy environments. It supports many languages and dialects, and it can handle real time audio. However, it does require an internet connection and might have some latency. Would you like to know more about its features?
00;20;59;09 - 00;21;32;11
Speaker 1
Okay, I don't really have to worry about not having internet. That's not really. It's not really something that I'm worried about. It is cool that you could have, like, an offline thing on, like, your phone or something like that. Like, if you if you lived in a place where you didn't have Wi-Fi or reception or whatever, but that's really not, like a point to win on in this situation.
00;21;32;13 - 00;21;46;04
Speaker 2
Okay. If offline capability isn't a priority. Google Cloud speech to text might be a good fit. It's known for its accuracy and ability to handle noisy environments. Would you like me to find some resources to help you get started with it?
00;21;46;06 - 00;21;56;18
Speaker 1
Why does I want you to explain to me why someone chooses Mozilla deep speech, and give me some examples.
00;21;56;21 - 00;22;13;21
Speaker 2
Okay, Mozilla deep speech is a good option if you want to keep your data private and process it locally. It's also free and open source so you have more control over the software. Would you like me to help you find some resources for setting it up?
00;22;13;24 - 00;22;20;22
Speaker 1
Is Google Cloud speech free?
00;22;20;24 - 00;22;35;04
Speaker 2
Google Cloud's speech to text isn't completely free, but they do have a free tier that gives you 60 minutes of audio processing per month. After that, you'll be charged based on usage. Would you like me to find the pricing details for you?
00;22;35;06 - 00;22;43;28
Speaker 1
Yeah. What if I did, like, 50 hours a month? How much would that cost?
00;22;44;01 - 00;22;59;16
Speaker 2
Okay. Google cloud speech to text charges per minute of audio processed the first 60 minutes each month are free. After that, it's $0.016 per minute. So 50 hours would cost you around $48.
00;22;59;18 - 00;23;23;18
Speaker 1
I mean, it's something, but it's, it's not that much at all, really. I'm not really worried about privacy because everything the I'm doing here, I'm. I'm posting anyways, so I don't need to, like, make an app that keeps user data private.
00;23;23;20 - 00;23;36;03
Speaker 2
Okay. In that case, Google Cloud speech to text might be a good option since it's quite accurate and can handle noisy environments. Would you like me to help you find some resources for setting it up?
00;23;36;05 - 00;23;47;16
Speaker 1
No, not right now. I'm just kind of, talking through the concepts and stuff like that. I'll follow up on all this shit.
00;23;47;18 - 00;23;56;04
Speaker 2
Okay. Sounds good. Feel free to reach out whenever you're ready to dive deeper into the setup process.
00;23;56;06 - 00;24;31;24
Speaker 1
Okay, so, that was the capture audio and transcribe, Google Cloud speech is probably a decent, at least starting point to keep it more simple. And then I'd like to take the speech to text. And I would like to take, a bot that takes chunks of the conversation and contextualizes it, eventually contextualizes it. Probably what right now is it will take a chunk every minute.
00;24;31;26 - 00;24;48;26
Speaker 1
Probably based on word count. It doesn't need to specifically be every minute. It will take a chunk per minute or whatever for now, and it will summarize that and put it into an image generator. It will it will prompt, from.
00;24;48;26 - 00;25;09;11
Speaker 2
What you use Google Cloud speech to text. Okay. So you want to use Google Cloud speech to text to transcribe the audio and then use a bot to create prompts for stable diffusion. You could use a language model like GPT three or Lamda for that. Does that sound like what you had in mind?
00;25;09;14 - 00;25;38;26
Speaker 1
Yeah. So this is something where I could potentially, like, grab that. I'm thinking about live right now, but this would all be actually, like, really easy to do in post. But if we ever did it live, it would, it would probably have to be something where it grabs that and, prompts through an API or.
00;25;38;26 - 00;25;53;05
Speaker 2
Something, Google cloud speech to text to transcribe the audio and then feeding that into a language model to generate prompts for stable diffusion is definitely doable for post-production for a live setup. You might need to fine tune the language model to work with shorter chunks of text. Does that make sense?
00;25;53;10 - 00;26;29;07
Speaker 1
Yeah, I think the the part that I'm kind of like struggling to wrap my head around is like moving it from like taking that transcription and then putting it into ChatGPT in a way that it will understand it, I guess using the API, maybe we'll like, kind of prime the API to like, let it know what we're doing and be like, hey, we're we're doing a recording.
00;26;29;07 - 00;26;30;24
Speaker 1
And,
00;26;30;27 - 00;26;42;04
Speaker 2
Yeah, you could use the OpenAI API to send the transcript to ChatGPT and get a summary back. You might need to experiment with different prompts to get the best results. Does that sound like a good approach?
00;26;42;07 - 00;27;01;14
Speaker 1
Okay, once again, if we're doing this in real time, I need a way to get it from the cloud speech API or whatever that it probably is to the GPT five API, whether it be something like Python.
00;27;01;17 - 00;27;20;12
Speaker 2
Yeah, Python would be perfect for that. You could use it to grab the transcript from the Cloud Speech API and then send it to GPT four. You probably want to use libraries like request and Json to handle the API calls. Does that sound like a good plan?
00;27;20;14 - 00;27;43;05
Speaker 1
Okay. And then and then we'll take the summary from the GPT five bot. And this will all happen like really quickly. And we will send that using Python into the next API, which is like going to be either stable diffusion or something similar.
00;27;43;07 - 00;27;54;10
Speaker 2
Yeah. You could use Python to send the summary from ChatGPT to the stable diffusion API. There are a few libraries that can help with that.
00;27;54;12 - 00;27;58;25
Speaker 1
Okay, can you tell me about those?
00;27;58;28 - 00;28;11;20
Speaker 2
Okay, so diffusers is a library from Huggingface that makes it really easy to work with stable diffusion in Python. The stable diffusion API library is a bit more general purpose and can be used with other diffusion models as well. Does that help?
00;28;11;23 - 00;28;20;07
Speaker 1
Yeah. Can you tell me more about Hugging Face? I've heard a lot about it, but I just haven't asked about it.
00;28;20;09 - 00;28;35;28
Speaker 2
Hugging face is a great resource for working with AI models. They have a huge library of pre-trained models, including stable diffusion, and they make it really easy to use them in your own projects. They also have a great community and lots of tutorials to help you get started. Does that sound like it might be helpful for your project?
00;28;36;00 - 00;28;55;26
Speaker 1
Yeah. So what is hugging face? Is hugging face like, like an amalgamation of apps made by a certain company. That's like living on stable diffusion or not living on, but like, partially using stable diffusion and stuff.
00;28;55;28 - 00;29;07;19
Speaker 2
Hugging face is like a big hub for AI models and tools. It's not tied to any one company or model, but it's a really popular place for developers working with things like stable diffusion.
00;29;07;22 - 00;29;20;02
Speaker 1
Okay, tell me more about hugging face. Like, when did it start? Tell me its origin. Like, what is it on the cloud?
00;29;20;05 - 00;29;38;06
Speaker 2
Hugging face started in 2016, in New York City. It's a platform where you can find and share machine learning models and datasets. They're pretty big in the natural language processing world, and they have a library called Transformers that's really popular. Hugging face also has something called the Hugging Face Hub, which is kind of like a central place for all things machine learning.
00;29;38;09 - 00;29;44;03
Speaker 2
They're a cloud based platform, so you can access everything online. Do you want to know more about the Hugging Face Hub?
00;29;44;06 - 00;29;47;19
Speaker 1
Yes, please.
00;29;47;21 - 00;30;02;18
Speaker 2
The Hugging Face Hub is like a giant library for AI models and data sets. You can find all sorts of pre-trained models there, including different versions of stable diffusion. It's a great place to discover new tools and resources for your projects. Do you want to explore some of the models available on the hub?
00;30;02;20 - 00;30;07;02
Speaker 1
Yes, please.
00;30;07;04 - 00;30;27;01
Speaker 2
Okay. There are tons of models on the hub. You can find different versions of stable diffusion, fine tuned for specific styles or subjects. There are also models for image to image translation, like turning sketches into realistic images. Do you have any particular styles or subjects in mind?
00;30;27;03 - 00;30;41;20
Speaker 1
I just want to. I just want to like. Right now that you you think about it. We could almost, like, set a theme, but there's a lot of things we can do with this.
00;30;41;23 - 00;30;56;25
Speaker 2
Yeah, you could definitely set a theme. Like, you could have the bot generate prompts based on the conversations, mood or topic, and then use a stable diffusion model fine tuned for that specific theme. That could lead to some really interesting and unique visuals.
00;30;56;27 - 00;31;06;12
Speaker 1
Okay, tell me more about Transformers with respect to hugging face.
00;31;06;15 - 00;31;24;05
Speaker 2
Okay. Hugging faces. Transformers library is a really powerful tool for working with language models. It makes it super easy to download and use pre-trained models for all sorts of tasks. You could use it to generate different kinds of text, translate languages, or even summarize long articles. Does that sound like something that could be useful for your project?
00;31;24;07 - 00;31;36;04
Speaker 1
So tell me the difference between the Transformers library and the hugging Face hub.
00;31;36;07 - 00;31;52;21
Speaker 2
The Transformers Library is a Python library that gives you tools to work with pre-trained language models. The Hugging Face Hub, on the other hand, is a platform where you can discover and share those models. Think of the library as the toolbox and the hub as the place to find the tools. Does that make sense?
00;31;52;23 - 00;31;58;27
Speaker 1
Yeah. Tell me more about this Python library.
00;31;58;29 - 00;32;15;01
Speaker 2
The Transformers library is great because it supports tons of different language models. So you can find the perfect one for your project. It also has a really simple API, so you don't have to be a coding expert to use it. Plus, Hugging Face has a huge community, so there are tons of resources and examples to help you get started.
00;32;15;03 - 00;32;18;18
Speaker 2
Do you want to explore some of the models available in the library?
00;32;18;20 - 00;32;22;10
Speaker 1
Yes, please.
00;32;22;12 - 00;32;36;23
Speaker 2
Okay, you could use the Transformers library to generate summaries of the conversation. Would you could then feed into stable diffusion to create visuals. There are also models for question answering. So you could have the bot answer questions about the conversation in real time. Does that sound like a good starting point?
00;32;36;25 - 00;32;44;22
Speaker 1
Wait, can you repeat that and, go into a little bit more detail?
00;32;44;24 - 00;33;04;15
Speaker 2
Sure. So you could use the Transformers library to generate summaries of the conversation. These summaries could then be used as prompts for stable diffusion, creating visuals that reflect the conversations content. Additionally, there are models within the library for question answering. This means the bot could potentially answer questions about the conversation in real time, providing an interactive element to the visuals.
00;33;04;18 - 00;33;07;12
Speaker 2
Does that give you a clearer picture?
00;33;07;14 - 00;33;18;12
Speaker 1
Yeah, that sounds, really cool. I'd like you to just kind of keep talking on this subject.
00;33;18;15 - 00;33;41;04
Speaker 2
Okay, so we've covered using Google Cloud speech to text for transcription. Feeding that into a language model like GPT four to generate prompts and then using stable diffusion to create visuals. We've also discussed how hugging faces, Transformers library and hub can be valuable resources for this project. Does that all make sense? Or would you like me to clarify anything?
00;33;41;06 - 00;34;07;05
Speaker 1
It seems like taking the audio and putting it in the Transformers library and then going straight to the stable diffusion, seems quicker and better and more customizable than going to cloud speech. Speech to text to GPT T5 and then stable diffusion.
00;34;07;07 - 00;34;20;06
Speaker 2
You're right. Using the Transformers library directly with stable diffusion could be faster and more customizable. Google cloud speech to text in GPT four add extra steps that might not be necessary. Do you want to explore how to set that up?
00;34;20;08 - 00;34;24;25
Speaker 1
Yes, please.
00;34;24;28 - 00;34;40;10
Speaker 2
Okay. To use the Transformers library directly with stable diffusion, you'll need to install the library and its dependencies. Then you can use the pipeline function to load a pre-trained stable diffusion model and generate images from your audio input. Would you like me to walk you through the installation process?
00;34;40;12 - 00;34;59;04
Speaker 1
No. I want to keep it kind of high level and conceptual. Let's say that I am, I have audio and I want to feed it into the transformer library. How do I do that?
00;34;59;06 - 00;35;19;10
Speaker 2
Okay. To feed audio into the Transformers library, you'll need to convert it into a format that the model can understand. Typically, this means converting it into a numerical representation of the audio waveform. The library provides tools to help with this conversion. Would you like me to find some examples of how to do this?
00;35;19;12 - 00;35;23;17
Speaker 1
What?
00;35;23;19 - 00;35;41;02
Speaker 2
Okay, so you can use the library or the library in Python to load your audio file and convert it into a numerical array. Then you can feed that array into the transformers library for processing. Does that make sense? Or would you like me to find some code examples?
00;35;41;04 - 00;36;01;04
Speaker 1
Well, I didn't really think about that. That's how it works. So it takes the audio and instead of it, it takes the audio and it turns it into numerical form.
00;36;01;07 - 00;36;15;20
Speaker 2
Yeah. That's right. The library. The library helps you turn your audio into numbers, which the Transformers library can then use to understand the sound. It's kind of like translating the audio into a language the computer understands. Does that make sense?
00;36;15;23 - 00;36;20;21
Speaker 1
Is this how it's always done?
00;36;20;23 - 00;36;34;06
Speaker 2
Yeah. Converting audio to numerical form is pretty standard practice for most audio processing tasks. The numerical representation captures the essence of the sound like it's pitch and loudness in a way the computers can understand. Does that make sense?
00;36;34;08 - 00;36;42;03
Speaker 1
Is this how Google, cloud speech? Does it?
00;36;42;06 - 00;36;56;16
Speaker 2
Yes. Google cloud speech to text also converts audio into a numerical representation. However, it uses a slightly different process that's optimized for speech recognition. Would you like me to explain how Google Cloud speech to text does this?
00;36;56;18 - 00;37;09;00
Speaker 1
Okay, I have a question. If I use Google speech to text, is it going to be way more accurate than if I use the transformer library?
00;37;09;02 - 00;37;24;09
Speaker 2
Generally, yes. Google cloud speech to text is specifically designed and optimized for speech recognition, so it's likely to be more accurate than a general purpose model from the Transformers library.
00;37;24;11 - 00;37;30;22
Speaker 1
How how like how much less accurate?
00;37;30;24 - 00;37;48;29
Speaker 2
It's tough to give an exact number, as accuracy can vary a lot depending on the audio quality and what's being said. Google's API is trained on a massive data set of speech. So it's generally more accurate, especially in noisy environments or with varied accents. But the transformer library is more flexible for different kinds of audio tasks.
00;37;49;02 - 00;37;52;16
Speaker 1
Go into that.
00;37;52;19 - 00;38;15;06
Speaker 2
Okay, Google cloud speech to text is trained on a massive data set of real world conversations, which helps it handle things like background noise and different accents. Much better. The Transformers library, on the other hand, is trained on a wider variety of audio, so it might not be as finely tuned for everyday speech. Does that help explain the difference?
00;38;15;09 - 00;38;23;14
Speaker 1
I would like you to go into the specifics of the positive and negatives of both.
00;38;23;16 - 00;38;44;00
Speaker 2
Okay, let's break down the pros and cons. Google cloud speech to text is great for accuracy, especially with varied speakers and noisy audio, but it can get pricey for large volumes of audio. The Transformers library is more flexible and cost effective, but might not be as accurate for general speech. Does that help you weigh your options?
00;38;44;03 - 00;38;55;23
Speaker 1
What if I wanted to? So the Transformers library, is that something that is open source?
00;38;55;25 - 00;39;14;14
Speaker 2
Yes, the Transformers library is open source. It's a really great resource because it's constantly being updated and improved by a huge community of developers. This means you get access to the latest and greatest models and techniques.
00;39;14;17 - 00;39;26;21
Speaker 1
Okay. So if I were to do something live, how would I use Google speech to do it?
00;39;26;23 - 00;39;44;09
Speaker 2
If you want to use Google Cloud speech to text for live transcription, you'll need to use their streaming API. It's designed to handle real time audio input. You typically use a programing language like Python to send the audio data to the API, and receive the transcript as it's generated. Does that make sense?
00;39;44;11 - 00;39;51;26
Speaker 1
Yeah. I would like you to step me through that process beginning to end.
00;39;51;29 - 00;40;11;03
Speaker 2
Okay. First, you'd need to set up a Google Cloud project and enable the Speech to text API. Then you'd use a client library like the one for Python to send your audio stream to Google servers. The API would then return the transcript in real time. Do you have a Google Cloud account already?
00;40;11;05 - 00;40;27;26
Speaker 1
Hold on. Servers. So, no, I don't want to go into like the specifics of, like, setting up accounts and stuff like that. Or just talking on a conceptual level right now.
00;40;27;28 - 00;40;42;02
Speaker 2
Okay. Conceptually, you'd use a programing language like Python to send chunks of your live audio to Google servers. Google would then send back the transcribed text in near real time. This text could then be fed into your stable diffusion setup. Does that make sense?
00;40;42;05 - 00;40;52;14
Speaker 1
Okay, so how would you use Python to send audio chunks to Google? Speech to text.
00;40;52;17 - 00;41;14;11
Speaker 2
Okay. You'd use the Google Cloud Speech library in Python. It lets you create a stream that sends your audio data to Google servers in real time. You break your audio into small chunks and send them through the stream and the app. I would send back the transcript as it's processed. Would you like me to find some code examples of how to do this?
00;41;14;13 - 00;41;37;24
Speaker 1
So, yeah, it feels like it's starting to get more complicated. Can you can you walk me through how I would do the logistics of taking chunks of audio and feeding them into Google speech effects?
00;41;37;26 - 00;41;57;27
Speaker 2
Okay, so you'd use a Python library called Pi Audio to capture audio from your microphone in real time. Then you break that audio into small chunks and send them to Google's API using the Google Cloud Speech Library. Google would then send back the transcript for each chunk, and you could display it as it comes in. Does that make sense?
00;41;58;00 - 00;42;09;00
Speaker 1
Yeah. Would you recommend this? I guess who I would like you to give us.
00;42;09;01 - 00;42;11;14
Speaker 2
This is the common approach for live transcription.
00;42;11;16 - 00;42;18;29
Speaker 1
I would like you to give us some real world examples of anybody who's doing this.
00;42;19;02 - 00;42;35;07
Speaker 2
Okay. One example is how live captions are generated for YouTube videos. They use a similar set up to transcribe speech in real time. Another example is linked to Otter AI, which provides live transcription for meetings and conferences. Would you like me to find more examples?
00;42;35;09 - 00;42;40;12
Speaker 1
Tell me about auto AI.
00;42;40;15 - 00;42;57;15
Speaker 2
Links to others I have a really cool tool that uses AI to transcribe speech in real time. It's great for meetings, interviews, and even lectures. It can also identify different speakers and create summaries of the conversation. Have you ever used it before?
00;42;57;17 - 00;43;04;27
Speaker 1
No. What is their, what are their models built on?
00;43;04;29 - 00;43;23;20
Speaker 2
Link to auto. I use the combination of machine learning models to achieve accurate transcription. They've trained their models on a huge data set of audio recordings to handle different accents, background noise, and speaking styles. Would you like to know more about the specific techniques they use?
00;43;23;22 - 00;43;46;17
Speaker 1
Do you think that, if I'm like a Californian Texan, like a American, I mumble a little bit and stuff like that, but how how much do you think an open source model would misunderstand me?
00;43;46;19 - 00;44;01;26
Speaker 2
Well, open source models are getting better all the time, but they might struggle a bit more with accents and mumbling compared to something like Google's API. It really depends on the specific model and how much training data it has for those kinds of speech patterns.
00;44;01;29 - 00;44;13;07
Speaker 1
Okay, tell me about using Pi audio. With Google Live speech to text.
00;44;13;10 - 00;44;33;03
Speaker 2
Okay. So you can use the Pi audio library in Python to capture live audio from your microphone. Then you need to Google Cloud speech to text client library to send that audio to Google servers for transcription. It's a bit more involved than just using a prerecorded file, but it's definitely doable. Have you worked with audio in Python before?
00;44;33;06 - 00;44;36;21
Speaker 1
No.
00;44;36;24 - 00;44;56;20
Speaker 2
Okay. No problem. We can take it step by step. First, you need to install the Pi audio library. It's a bit more involved than just a simple pip install, but there are good instructions on the library's website. Once you have it installed, we can move on to the next step.
00;44;56;22 - 00;45;09;01
Speaker 1
Okay. I want to jump back to the Transformers library for the audio to Transformers library to stable diffusion approach.
00;45;09;03 - 00;45;27;28
Speaker 2
Okay, to use the Transformers library with stable diffusion, you first load your audio file and convert it into a spectrogram. This is a visual representation of the audio frequencies over time. Then you'd feed that spectrogram into a pre-trained model from the Transformers library to generate a text prompt. Finally, you'd use that prompt with stable diffusion to create your image.
00;45;28;00 - 00;45;30;20
Speaker 2
Does that make sense?
00;45;30;22 - 00;45;55;02
Speaker 1
And so what I'm kind of thinking about is like, if I use an open source model to do this and run it on my computer, then I can like fine tune it and stuff like that and not really have to update it and change it because like, I'm not trying to make an app, I'm trying. I'm not trying to like, do anything like that and like keep it up to date with the best models and like blah, blah, blah.
00;45;55;03 - 00;46;00;18
Speaker 1
I just want to I want to make something that is like a fine tuned machine. And then I can just.
00;46;00;19 - 00;46;17;21
Speaker 2
Using an open source model gives you a lot of. Exactly. Once you fine tune the model, you can keep using it without needing to chase the latest updates. It'll be customized to your voice and work just the way you want it to.
00;46;17;23 - 00;46;32;20
Speaker 1
And if I do it using something like the Google Speech to Text API, I guess it's pretty easy. Like, I put it into the API and the API spits it back out.
00;46;32;23 - 00;46;48;18
Speaker 2
Yes that's right. The Google Speech to text API designed to be pretty straightforward. You send it the audio and it sends back the transcription. It handled a lot of the heavy lifting for you.
00;46;48;20 - 00;47;08;14
Speaker 1
Would it be easier? Yeah, I guess so. So how would I create a situation where I'm transcribing this conversation live using that.
00;47;08;16 - 00;47;26;00
Speaker 2
Okay. To transcribe this conversation live. You need to use a programing language like Python to capture the audio from your microphone. Then you'd send that audio to the Google Speech to Text API in real time. The API would then send back the transcription, which you could display on your screen. Does that make sense?
00;47;26;02 - 00;47;32;03
Speaker 1
Yeah. What kind of intervals would you do for this on?
00;47;32;06 - 00;47;48;12
Speaker 2
You'd typically send audio chunks in intervals of a few hundred milliseconds, like 100 to 300 milliseconds. This keeps the latency low and the transcription feeling real time. Does that make sense?
00;47;48;15 - 00;47;54;10
Speaker 1
Dang. What about, like, internet connection?
00;47;54;12 - 00;48;07;19
Speaker 2
You'll need a stable internet connection for live transcription. The faster and more consistent your connection, the better the experience will be. Google recommends at least 100kbps for optimal performance.
00;48;07;21 - 00;48;24;22
Speaker 1
So I can have a mic in here with Python sending audio chunks to Google API, and it will send me back live transcription.
00;48;24;25 - 00;48;40;04
Speaker 2
Yeah, that's exactly right. You can use Python to capture audio from your microphone, send it to Google's API and receive the transcript in real time. It's a pretty powerful setup for live transcription.
00;48;40;06 - 00;49;01;01
Speaker 1
So the code needed to do that is Google. Does Google change its API in a way that, like, I would need to update, and keep keep like bug hunting and stuff like that.
00;49;01;04 - 00;49;14;14
Speaker 2
Yes, Google does update its speech to text API, so you need to keep an eye on their documentation for any changes. They usually provide plenty of notice for major updates, but there might be smaller tweaks along the way.
00;49;14;17 - 00;49;41;28
Speaker 1
So let's say I had this Python, this Python py audio situation, and I was sending this audio, every however many milliseconds to Google speech to text and getting it back and creating a live feed that was, back on my computer. I forget my question.
00;49;42;00 - 00;49;59;27
Speaker 2
Okay, so you'd have a Python script running that captures audio from your microphone using Pi audio. That audio gets sent to Google's API in chunks, and then the API sends back the text transcript in real time. You could then display that text on your screen as it's generated. Does that make sense?
00;50;00;00 - 00;50;29;24
Speaker 1
Yeah. And then let's say that I want to kind of record this, this transcription and feed that every, every minute or every 30s into something that will give me a really fast prompt.
00;50;29;26 - 00;50;45;16
Speaker 2
Okay. So you can use the transcript from the Google Speech to Text API and feed it into a language model like GPT. For every 30s or so. That would give you a good balance between real time updates and manageable processing. Does that sound like what you had in mind?
00;50;48;00 - 00;51;07;12
Speaker 1
So if instead of feeding that audio into, into the Google speech to text and then into GPT five, if I use the Transformers library, can I skip the whole transcription process?
00;51;07;15 - 00;51;21;25
Speaker 2
That's right. You can skip the transcription step by using the Transformers library to process the audio directly. It can analyze the sound and generate text descriptions without needing to convert the entire conversation to words. First.
00;51;21;27 - 00;51;26;03
Speaker 1
Would you recommend this?
00;51;26;06 - 00;51;41;06
Speaker 2
If you're aiming for a more abstract or creative interpretation of the sound, then yes, using the Transformers library directly could be a great approach. It might give you some really interesting and unique prompts compared to just transcribing the words.
00;51;41;09 - 00;51;50;22
Speaker 1
Interesting. It's just gonna like, give it weird and noetic things and not related to the English language.
00;51;50;25 - 00;52;05;22
Speaker 2
It might pick up on sounds that aren't words, but it'll try to relate them to English if it can. It's trained on a lot of English, so that's its default. But you might get some interesting interpretations of non-verbal sounds.
00;52;05;24 - 00;52;24;10
Speaker 1
Okay, so, tell me about how it can just, skip the transcription process and go straight to prompting based on the audio it heard?
00;52;24;12 - 00;52;46;18
Speaker 2
Okay. So instead of using the Google Speech to text API, you'd use a pre-trained model from the Transformers library that's designed for audio analysis. This model can take your voice recording and generate a text prompt directly without ever creating a full transcript. It's a bit like having a really smart AI that can understand the vibe of your voice and turn it into a creative description.
00;52;46;21 - 00;53;06;03
Speaker 1
Yeah, but if I said, like, hey, make me an image of a dog sitting on a bench in 1951, would it give me an accurate image?
00;53;06;05 - 00;53;16;22
Speaker 2
Yes. If you use a clear and specific prompt like that, stable diffusion should be able to generate a pretty accurate image of a dog sitting on a bench in a 1950s style.
00;53;16;24 - 00;53;29;25
Speaker 1
Know what I'm talking about? Is the transformer library going to, convert that into a good prompt to give to stable diffusion?
00;53;29;28 - 00;53;43;04
Speaker 2
Yes. The transformer library is designed to create effective prompts for stable diffusion. It can analyze the audio and generate a text description that captures the key elements for image generation.
00;53;43;07 - 00;53;58;25
Speaker 1
So is this, is this purpose? Is this thing's purpose to be able to skip audio transcription for the purpose of image generation?
00;53;58;27 - 00;54;16;08
Speaker 2
Yeah, that's exactly right. The main goal of using Transformers in this way is to skip the traditional text transcription step and go straight to generating prompts that stable diffusion can use to create images from sound. It's all about making the process faster and more focused on the visual output.
00;54;16;10 - 00;54;32;14
Speaker 1
Got it. I think that that is what we're going to do. Even though it sounds like way a bit harder and it seems way more direct and customizable.
00;54;32;17 - 00;54;48;06
Speaker 2
That's right. Using the Transformers library to go straight from audio to image prompt is more direct and customizable. It might be a bit more complex to set up, but it gives you a lot of control over the final result.
00;54;48;08 - 00;54;51;12
Speaker 1
Okay. I want you.
00;54;51;13 - 00;54;54;19
Speaker 2
I want to explore how to set that up with the transformer.
00;54;54;21 - 00;54;55;25
Speaker 1
Yes. Let's do that.
00;54;55;25 - 00;55;05;29
Speaker 2
Okay. I can help you with that. First, you'll need. Great. You can install the Transformers library using pip. Just run pip. Install transformers.
00;55;06;01 - 00;55;30;23
Speaker 1
You know what I don't want you to start looking at. I don't want specifics of how to how to do it. Like, actually the steps and stuff like that. I want you to take me now that we've kind of, like, nailed down a method that we want to use. I want to take me. I want you to take me from audio to, to.
00;55;30;25 - 00;55;46;14
Speaker 2
Okay. So you start by recording audio from your microphone and then use the Transformers library to analyze the sound and generate a text prompt. Finally, you'd feed that prompt into stable diffusion to create your image.
00;55;46;16 - 00;55;53;20
Speaker 1
Okay, so how would I get the audio into the Transformers library?
00;55;53;23 - 00;56;10;05
Speaker 2
You can load the audio file using the library, a library which is great for working with audio data in Python. Once the audio is loaded, you can preprocess it as needed. Like resampling or normalizing the volume. Then you can feed the audio data into the transformer model for analysis.
00;56;10;07 - 00;56;19;18
Speaker 1
Is this something that could be fine tuned into being able to do in real time?
00;56;19;21 - 00;56;38;17
Speaker 2
Yes, you can definitely fine tune Transformers for real time performance. You'd likely want to focus on optimizing the model for speed, perhaps by reducing its size or complexity. You could also explore techniques like streaming inference to process the audio in smaller chunks.
00;56;38;20 - 00;56;46;10
Speaker 1
Okay. Tell me about, tell me about what you just said. And streaming inference.
00;56;46;13 - 00;57;04;01
Speaker 2
Okay. Streaming inference is like breaking down a big task into smaller pieces and tackling them one by one. So instead of waiting for the whole audio clip to finish, you process it in short segments. This way you get results much faster, almost in real time.
00;57;04;03 - 00;57;13;01
Speaker 1
Okay. Can you go through the specifics of, like, doing that?
00;57;13;03 - 00;57;31;13
Speaker 2
Sure. So you'd start by breaking your audio into smaller chunks, say a few seconds each. Then you'd feed each chunk into your transformer model as soon as it's recorded. The model would process it and give you a prompt, and you'd use that prompt to generate an image with stable diffusion.
00;57;31;15 - 00;57;55;23
Speaker 1
Okay. An end to end. How quick could I do this? Like, like like how from recorded being, recording of the audio being sent to, the transformer through the completed image.
00;57;55;25 - 00;58;08;00
Speaker 2
If you're using a pre-trained model, it could be pretty fast. Maybe just a few seconds from capturing the audio to seeing the image. But if you're fine tuning the model or doing some heavy post-processing, it might take a bit longer.
00;58;10;03 - 00;58;36;19
Speaker 1
Okay. I think that's probably a really good idea that we're going to kind of. Can you can you give me, can you give me some tips on how to use this method? As someone who doesn't have a ton of, like, command line experience or experience with Python.
00;58;36;19 - 00;58;56;21
Speaker 1
Okay. If you're not super comfortable with the command line or Python, don't worry. There are some user friendly tools that can help. You could try using a graphical interface for stable diffusion, and there are even some online platforms that handle the audio processing and image generation for you.
00;58;56;23 - 00;59;11;26
Speaker 2
And what if I wanted to, kind of jump in both the, into doing it to where it can be closer and closer to real time?
00;59;11;29 - 00;59;27;15
Speaker 1
Okay. To get closer to real time, you'd want to focus on optimizing your code in hardware. Using a GPU can significantly speed up the image generation process and you could also look into techniques like model quantization to make the transformer model run faster.
00;59;27;17 - 00;59;32;05
Speaker 2
Okay, tell me more about that.
00;59;32;07 - 00;59;51;03
Speaker 1
Okay, so model quantization is like compressing a file to make it smaller and faster. It reduces the precision of the numbers the model uses, which means it takes up less memory and runs calculations more quickly. It's a bit more advanced, but it can make a big difference for real time performance.
00;59;51;06 - 01;00;24;23
Speaker 2
Okay. I think that's a good stopping point. This is a core idea. And actually, before we go, if I, if I didn't do this real time, what is the easiest way to do something like this? Is it like to take an audio transcription and cut it up into one minute chunks? Have ChatGPT summarize that and then put it on a green screen?
01;00;24;23 - 01;00;30;07
Speaker 2
Or just like get a picture in picture and post.
01;00;30;10 - 01;00;48;15
Speaker 1
Yes, that's definitely doable. You could use a Python library like Pi dub to split the audio into one minute chunks, then use Google Cloud speech to text or similar service to transcribe each segment. After that, you could feed the transcriptions into ChatGPT for summarization and display the results however you'd like.
01;00;48;17 - 01;01;09;24
Speaker 2
Okay, so I already get a transcription from, premiere on the final output, so I could probably take those, how long? How many words are in a minute of speech?
01;01;09;26 - 01;01;18;24
Speaker 1
On average, there are about 150 words in a minute of speech, but it can vary quite a bit depending on how fast someone is talking.
01;01;18;27 - 01;01;53;20
Speaker 2
Okay, so I could probably have it take maybe like 50 or 80 word chunks, using maybe like a python and like, API to kind of automate it and then just have the image generated that way through stable diffusion and it wouldn't be alive, but that it could be the kind of thing where it takes 5 to 10 minutes to generate the images.
01;01;53;20 - 01;02;06;29
Speaker 1
Yeah, that's a great approach for a non-real time workflow. Great. That sounds like a fun project. Do you want to explore the Python code for automating this process?
01;02;07;01 - 01;02;41;14
Speaker 2
Yeah, take me from the easiest method to have maybe 200 images after an hour long recording. Based on chunks of the conversation from the point of taking the transcription off of premiere and putting it through like, a program that pops out, a bunch of images in order.
01;02;41;15 - 01;02;55;26
Speaker 1
Okay, so you take the transcript from premiere and use Python to split it into chunks. Then you'd feed each chunk into stable diffusion to generate an image. Finally, you could create a slideshow or video with all the images.
01;02;56;07 - 01;03;07;14
Speaker 2
Okay, so I think the, the, this is a good starter Python project. Maybe.
01;03;07;16 - 01;03;20;23
Speaker 1
Absolutely. This would be a great starter project. It combines text processing and image generation, and you can get creative with how you display the final results. Do you want to start exploring the Python code for this?
01;03;20;26 - 01;03;57;11
Speaker 2
Okay, so this is something that I definitely would not want to do by like taking one minute chunks by hand and putting them in the chat, GPT three or an image generator or whatever, getting a prompt out. And that sounds really, really miserable. So and it's just like prohibitively not possible. But if we wrote a little bit of code to like, take this transcript and like, maybe even clean it up a little bit, like pull the numbers out and stuff like that.
01;03;57;14 - 01;03;58;17
Speaker 2
And then.
01;03;58;20 - 01;04;20;17
Speaker 1
Absolutely manually processing each chunk would be very tedious. We can definitely write some Python code to automate that. We can use libraries like re for text cleaning and OS for file handling to make the process smoother.
01;04;20;20 - 01;05;00;21
Speaker 2
Okay. So I think that the hard part is going to be, context. Right. So if we like had Gemini or like ChatGPT generate an image based on our entire conversation that we've had over the last like hour ten, it would just be like some garbled nonsense, probably. But then on the other hand, if we just take the last 30s, it's probably more accurate, but it doesn't have the full context of our conversation.
01;05;00;24 - 01;05;19;23
Speaker 1
That's a great point. Context is key. We could try a few things. One idea is to give Gemini the whole transcript and after to pick out the most visually interesting parts. Another option is to use a sliding window. So each image is based on the last few minutes of conversation, giving some context without being overwhelming.
01;05;19;26 - 01;06;00;04
Speaker 2
Okay. I like that. Idea. I also was thinking about like, maybe if we could use some kind of custom GPT that kind of knew what we were doing, and we kind of like trained it on like, hey, we are we are having a conversation and I want you to take the context of the conversation and, and like, really prime it on to like what it's getting into and then use that in an API, fashion.
01;06;00;06 - 01;06;18;01
Speaker 1
Okay. So you'd want to create a custom GPT that's specifically trained on your conversation and then use it through an API. This way you could automate the process of generating image prompts based on the context of your discussion. We'd have to figure out the best way to feed the conversation history into the GPT. But it's definitely doable.
01;06;18;04 - 01;06;49;04
Speaker 2
Right. I think it I think it needs to be like, it needs to be smart to what we're doing. Right? Because it doesn't know, it doesn't have the covers. It doesn't have the context of this being a conversation in a workshop with an AI. It's just going to like, take whatever we've said last and like, vomit it out.
01;06;49;07 - 01;07;07;05
Speaker 1
Okay, I understand. So to make this work, we'll need to give the custom GPT some context about our conversation. We could start by feeding it a summary of what we've just got. So far, and then add each new chunk of conversation as it comes in. This way, it'll have a better understanding of what's going on and generate more relevant image prompts.
01;07;08;08 - 01;07;28;28
Speaker 2
Yeah, I think that's probably the best way to go about it. Or just like take that Python code or take that Python code to grab, like, I mean, where the audio and just dump it into stable diffusion, see what you get out.
01;07;29;01 - 01;07;48;04
Speaker 1
Okay. I think I've got a good plan. We can use Python to grab those minute long audio chunks and feed them into stable diffusion. It might take a bit of tweaking to get the prompts just right, but it'll be fun to see what kind of images we get.
01;07;48;06 - 01;08;23;22
Speaker 2
Now, it's probably going to be smarter to, like, take that with the custom GPT. The custom GPT makes the prompts and then, because we're not having to deal with, like, real time anything. So let's say that you take the Python code and you put it into this GPT on like some kind of sliding window or like cumulative audio segment and, no, it wouldn't be an audio segment.
01;08;23;22 - 01;08;29;25
Speaker 2
Sorry. Can we start over?
01;08;29;27 - 01;08;36;09
Speaker 1
No problem. Take your time.
01;08;36;11 - 01;09;02;16
Speaker 2
Okay. So what this would do is we would have the transcription from premiere in my hand. Right. I've already cut the audio together and cleaned it up and stuff like that. And I get a transcription at the end. What I do is I take this and I put it into the Python folder, and the python grabs it and takes the transcription and then cuts it into certain level chunks.
01;09;02;18 - 01;09;08;03
Speaker 2
So that's probably like let's say 100 chunks and okay, take.
01;09;08;03 - 01;09;18;14
Speaker 1
The transcript from premiere and Python would split it into chunks. Then you'd feed those chunks into a custom GPT which would generate prompts for stable diffusion. Does that sound right?
01;09;18;16 - 01;09;23;09
Speaker 2
Yeah.
01;09;23;11 - 01;09;34;21
Speaker 1
Okay. So Python would grab the transcript, split it into chunks, and feed them into a custom GPT. The GPT would then generate prompts for stable diffusion.
01;09;34;24 - 01;09;43;19
Speaker 2
Yeah. And can I do that? With a custom GPT through an API. So it's real quick.
01;09;43;22 - 01;09;51;08
Speaker 1
Okay. So you'd use a custom GPT through an API to generate those prompts quickly. That sounds like a good plan.
01;09;51;10 - 01;10;19;11
Speaker 2
Okay. And then we take, those, like 50 prompts or whatever and send them separately into stable diffusion and stable diffusion takes that, that we've done with Python as well. Like we, we take the, we take the chunks, and we put them in the GPT with Python, and then we take the prompts with Python and we put them into stable diffusion.
01;10;19;13 - 01;10;37;12
Speaker 2
And then, this process that started with us just dumping a transcript into a folder or something like that pops out a hundred images the next day or something like that.
01;10;37;15 - 01;10;48;03
Speaker 1
Okay. So you dump the transcript into a folder, Python would do its thing, and you'd have 100 images ready to go the next day. It's a fully automated pipeline. Do you want to start exploring the Python code for this?
01;10;48;06 - 01;10;52;19
Speaker 2
No. Does that sound plausible?
01;10;52;21 - 01;11;01;17
Speaker 1
Yeah, that sounds totally feasible. We can definitely write a Python script to automate that whole process for you.
01;11;01;19 - 01;11;10;25
Speaker 2
What could the potential turn over on those hundred images be?
01;11;10;27 - 01;11;26;22
Speaker 1
That's a bit tricky to say for sure, as it depends on a few things the speed of the API for your custom GPT, and how fast stable diffusion runs on your setup are the main factors. But generally, generating 100 images could take anywhere from a few hours to half a day. Does that fit your time frame?
01;11;26;25 - 01;11;31;04
Speaker 2
Yeah.
01;11;31;06 - 01;11;51;02
Speaker 1
Great. So we've got a plan to automate the image generation from your podcast transcript. We can use Python to grab the transcript, split it into chunks, and feed those into a custom GPT via API. The GPT will generate prompts for stable diffusion, and Python will handle sending those prompts to stable diffusion. This way, you could have 100 images ready in a few hours.
01;11;51;05 - 01;11;57;26
Speaker 2
Is the bulk of this time on the image generation side?
01;11;57;28 - 01;12;14;09
Speaker 1
Yes, the image generation part with stable diffusion will likely take the most time. Generating high quality images is computationally intensive. The text processing and prompt generation should be relatively quick in comparison.
01;12;14;12 - 01;12;22;19
Speaker 2
If you had to give it a percentage of total, what would you give it? Time wise?
01;12;22;22 - 01;12;34;07
Speaker 1
I'd estimate that image generation with stable diffusion would take up about 80 to 90% of the total processing time. Text processing and prompt generation are much faster.
01;12;34;10 - 01;12;43;05
Speaker 2
Okay, cool. I think that's, it's probably a good place to start.
01;12;43;08 - 01;13;02;19
Speaker 1
Great. We've got a solid plan to automate the image generation from your podcast. Transcript. We can use Python to grab the transcript, split it into chunks, and feed those into a custom GPT via API. The GPT will generate prompts for stable diffusion, and Python will handle sending those prompts to stable diffusion. This way, you could have 100 images ready in a few hours.
01;13;02;21 - 01;13;06;08
Speaker 1
Do you want to start exploring the Python code for this?
01;13;06;10 - 01;13;16;13
Speaker 2
No. And this is something that I can do by just honestly dropping a transcript into a folder right?
01;13;16;16 - 01;13;37;29
Speaker 1
Yes. That's right. You can just drop the transcript into a folder, and the Python script will take care of the rest. It'll split the transcript into chunks. Feed them into the custom GPT via API to generate prompts, and then send those prompts to stable Diffusion. You'll have 100 images ready in no time.
01;13;38;01 - 01;13;49;05
Speaker 2
That's pretty cool. Okay. Thank you. Gemini. This is a really cool conversation.
01;13;49;08 - 01;13;56;18
Speaker 1
You're very welcome. I'm glad you found it helpful. Do you have any other questions about the process, or are you ready to start exploring the Python code?
01;13;56;21 - 01;14;03;17
Speaker 2
No, I think we're going to start from there because the shed's pretty hot now.
01;14;03;19 - 01;14;13;03
Speaker 1
Okay, I understand it's important to stay comfortable. Let me know when you're ready to continue. And we can explore the Python code for your image generation project.
01;14;13;05 - 01;14;14;26
Speaker 2
Thanks. Be.