Webhooks Explorers (BETA)

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https://huggingface.co/docs/hub/webhooks

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AI & ML interests

Webhooks are now publicly available on Hugging Face!

Recent Activity

taesiri authored a paper about 2 months ago

A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality

lidiya authored a paper 3 months ago

MixQG: Neural Question Generation with Mixed Answer Types

Zaid authored a paper 3 months ago

Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

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taesiri

authored a paper about 2 months ago

A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality

Paper • 2507.07202 • Published Jul 9 • 22

victor

posted an update 3 months ago

Post

5228

Open Source Avengers, Assemble! Ask an expert AI agent team to solve complex problems together 🔥

Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.

Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp

2 replies

lidiya

authored a paper 3 months ago

MixQG: Neural Question Generation with Mixed Answer Types

Paper • 2110.08175 • Published Oct 15, 2021

Zaid

authored 5 papers 3 months ago

Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

Paper • 2110.06744 • Published Oct 13, 2021

Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic

Paper • 2412.04277 • Published Dec 5, 2024

Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training

Paper • 2410.20796 • Published Oct 28, 2024

Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches

Paper • 2307.06218 • Published Jul 12, 2023

MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs

Paper • 2505.19800 • Published May 26 • 2

mrfakename

posted an update 4 months ago

Post

6979

Hi everyone,

I just launched TTS Arena V2 - a platform for benchmarking TTS models by blind A/B testing. The goal is to make it easy to compare quality between open-source and commercial models, including conversational ones.

What's new in V2:

- **Conversational Arena**: Evaluate models like CSM-1B, Dia 1.6B, and PlayDialog in multi-turn settings
- **Personal Leaderboard**: Optional login to see which models you tend to prefer
- **Multi-speaker TTS**: Random voices per generation to reduce speaker bias
- **Performance Upgrade**: Rebuilt from Gradio → Flask. Much faster with fewer failed generations.
- **Keyboard Shortcuts**: Vote entirely via keyboard

Also added models like MegaTTS 3, Cartesia Sonic, and ElevenLabs' full lineup.

I'd love any feedback, feature suggestions, or ideas for models to include.

TTS-AGI/TTS-Arena-V2

7 replies

victor

posted an update 4 months ago

Post

5009

DIA TTS is just amazing - please share your funniest gens (here is mine) 😂
nari-labs/Dia-1.6B

nouamanetazi

authored a paper 5 months ago

SmolVLM: Redefining small and efficient multimodal models

Paper • 2504.05299 • Published Apr 7 • 199

mrfakename

posted an update 5 months ago

Post

3027

Papla P1 from Papla Media is now available on the TTS Arena!

Try out Papla's new ultra-realistic TTS model + compare it with other leading models on the TTS Arena: TTS-AGI/TTS-Arena

awacke1

posted an update 5 months ago

Post

2232

AI Vision & SFT Titans 🌟 Turns PDFs into text, snaps pics, and births AI art.

https://huggingface.co/spaces/awacke1/TorchTransformers-Diffusion-CV-SFT

1. OCR a grocery list or train a titan while sipping coffee? ☕
2. Camera Snap 📷: Capture life’s chaos—your cat’s face or that weird receipt. Proof you’re a spy!
3. OCR 🔍: PDFs beg for mercy as GPT-4o extracts text.
4. Image Gen 🎨: Prompt “neon superhero me”
5. PDF 📄: Double-page OCR Single-page sniping

Build Titans 🌱: Train tiny AI models. 💪Characters🧑‍🎨: Craft quirky heroes.
🎥

emre

authored a paper 5 months ago

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Paper • 2211.05100 • Published Nov 9, 2022 • 33

emre

posted an update 5 months ago

Post

3625

having trouble with auto train
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?