segment-anything
TTS
segment-anything | TTS | |
---|---|---|
56 | 232 | |
44,293 | 29,631 | |
2.1% | 4.7% | |
0.0 | 9.4 | |
24 days ago | 3 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Mozilla Public License 2.0 |
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segment-anything
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What things are happening in ML that we can't hear oer the din of LLMs?
- segment anything: https://github.com/facebookresearch/segment-anything
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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Generate new version of a living-room with specific furniture
Render a new living room using a controlnet model of your choice to keep the basic structure. Load the original living room image and look for the furniture you want to change with a Segment Anything Model to create a mask. Use that mask on the new living room to inpaint new furniture.
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How Do I read Github Pages? It is so exhausting, I always struggle, oh and I am on windows
Hello,So I am trying to run some programs, python scripts from this page: https://github.com/facebookresearch/segment-anything, and found myself spending hours without succeeding in even understanding what's is written on that page. And I think this is ultimately related to programming.
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Autodistill: A new way to create CV models
Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO
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How to Fine-Tune Foundation Models to Auto-Label Training Data
Webinar from last week on how to fine-tune VFMs, specifically Meta's Segment Anything Model (SAM).
What you'll need to follow along the fine-tuning walkthrough:
Images, ground-truth masks, and optionally, prompts from the Stamp Verification (StaVer) Dataset on Kaggle (https://www.kaggle.com/datasets/rtatman/stamp-verification-s...)
Download the model weights for SAM the official GitHub repo (https://github.com/facebookresearch/segment-anything)
Good understanding of the model architecture Segment Anything paper (https://ai.meta.com/research/publications/segment-anything/)
GPU infra the NVIDIA A100 should do for this fine-tuning.
Data curation and model evaluation tool Encord Active (https://github.com/encord-team/encord-active)
Colab walkthrough for fine-tuning: https://colab.research.google.com/github/encord-team/encord-...
I'd love to get your thoughts and feedback. Thank you.
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Deploying a ML model (segment-anything) to GCP - how would you do it?
I now want users to be able to use the segment-anything model (https://github.com/facebookresearch/segment-anything) in my app. It's in pytorch if that matters. How it should work is that
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The Mathematics of Training LLMs
Yeah, they are great and some of the reason (up the causal chain) for some of the work I've done! Seems really fun! <3 :))))
Facebook's Segment Anything Model I think has a lot of potentially really fun usecases. Plaintext description -> Network segmentation (https://github.com/facebookresearch/segment-anything/blob/ma...) Not sure if that's what you're looking for or not, but I love that impressing your kids is where your heart is. That kind of parenting makes me very, very, very, happy. :') <3
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How hard is it to "code" a tool based on segment-anything and Stable diffusion ?
There are some snippets of Python code on the segment-anything github readme that show how to do this. Once you have it installed you can import functions from the segment-anything module, load a segmentation model, and generate masks for input images that match the prompt of your choice. You don't need Stable Diffusion for this, but you could load it through diffusers to do things like inpaint your images using the masks.
- The less i know the better
TTS
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Ask HN: Open-source, local Text-to-Speech (TTS) generators
I just noticed that https://coqui.ai/ is "Shutting down".
I'm building a web app (React / Django) which takes a list of affirmations & goals (in Markdown files), puts them into a database (SQlite), and uses voice synthesis to create voice audio files of the phrases. These are combined with a relaxed backing track (ffmpeg), made into playlists of 10-20 phrases (randomly sampled, or according to a theme: "mind" "body" "soul") and then play automatically in the morning & evening (cron). This allows you to persistently hear & vocalize your own goals & good vibes over time.
I had been planning to use Coqui TTS as the local text-to-speech engine, but with this cancellation, I'd love to hear from the community what is a great open-source, local text-to-speech engine?
Generally, I learn both the highest quality commercially available technology (example: ElevenLabs), and also the best open-source equivalent. Would love to hear suggestions & perspectives on this. What voice synth tools are you investing your time into learning & building with?
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OpenAI deems its voice cloning tool too risky for general release
lol this marketing technique is getting very old. https://github.com/coqui-ai/TTS is already amazing and open source.
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What things are happening in ML that we can't hear oer the din of LLMs?
Not sure how relevant this is but note that Coqui TTS (the realistic TTS) has already shut down
https://coqui.ai
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Base TTS (Amazon): The largest text-to-speech model to-date
I've used coqui.ai's TTS models[0] and library[1] to great success. I was able to get cloned voice to be rendered in about 80% of the audio clip length, and I believe you can also stream the response. Do note the model license for XTTS, it is one they wrote themselves that has some restrictions.
[0] https://huggingface.co/coqui/XTTS-v2
[1] https://github.com/coqui-ai/TTS
- FLaNK Stack Weekly 12 February 2024
- Coqui Is Shutting Down
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Coqui.ai Is Shutting Down
My only exposure to Coqui was their text to speech software. If I remember correctly the website was a commercialized service with TTS and probably some other related things. I hope the software work continues in the open.
https://github.com/coqui-ai/TTS
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Hello guys, any selfhosted alternative to eleven labs?
Coqui.ai TTS (https://github.com/coqui-ai/TTS)
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Demo of Anagnorisis - completely local recommendation system powered by Llama 2. Radio mode. Work in progress.
"tts_models/multilingual/multi-dataset/xtts_v2" model from https://github.com/coqui-ai/TTS. It gives pretty good results and works with references, so it's pretty easy to change the voice. By the way the source code of the project is open: https://github.com/volotat/Anagnorisis but be ready, the code is pretty raw for now.
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XTTS voice cloning with only a seconds of audio
A recent update to their GitHub also has a no-code gradio ui to facilitate fine-tuning and inferencing locally. https://github.com/coqui-ai/TTS/releases/tag/v0.21.3
What are some alternatives?
Segment-Everything-Everywhere-All-At-Once - [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
backgroundremover - Background Remover lets you Remove Background from images and video using AI with a simple command line interface that is free and open source.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
ComfyUI-extension-tutorials
silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple
stable-diffusion-webui-Layer-Divider - Layer-Divider, an extension for stable-diffusion-webui using the segment-anything model (SAM)
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
Grounded-Segment-Anything - Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
piper - A fast, local neural text to speech system