TTS Arena V2
π
928
Vote on the latest TTS models!
AI & ML interests
Decentralized yet unified efforts to accelerate research for Open Text to Speech (TTS) systems!
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mrfakenameΒ
updated
a
Space
6 days ago
Request to add EpistemeAI/LexiVox
3
#109 opened 9 days ago
by
legolasyiu
Post
3101
π What happened in AI in 2025? π
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1οΈβ£ Q1 β Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2οΈβ£ Q2 β Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3οΈβ£ Q3 β "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4οΈβ£ Q4 β Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 π€―
Credits
π NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
π«‘ @reach-vb for the original idea, design and recipe
π @ariG23498 and yours truly for compiling and verifying the 2025 edition
π₯³ Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! π₯
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1οΈβ£ Q1 β Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2οΈβ£ Q2 β Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3οΈβ£ Q3 β "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4οΈβ£ Q4 β Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 π€―
Credits
π NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
π«‘ @reach-vb for the original idea, design and recipe
π @ariG23498 and yours truly for compiling and verifying the 2025 edition
π₯³ Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! π₯
- 1 reply
mrfakenameΒ
posted
an
update
about 2 months ago
Post
12205
Excited to share that I've joined the Hugging Face Fellows program! π€
Looking forward to contributing to & working more closely with the open-source ecosystem - huge thanks to everyone who's supported me on this journey! π
Looking forward to contributing to & working more closely with the open-source ecosystem - huge thanks to everyone who's supported me on this journey! π
reach-vbΒ
authored
a
paper
2 months ago
SteveeeeeeenΒ
authored
2
papers
2 months ago
mrfakenameΒ
posted
an
update
3 months ago
Post
6208
Trained a model for emotion-controllable TTS based on MiMo audio on LAION's dataset.
Still very early and does have an issue with hallucinating but results seem pretty good so far, given that it is very early into the training run.
Will probably kick off a new run later with some settings tweaked.
Put up a demo here: https://huggingface.co/spaces/mrfakename/EmoAct-MiMo
(Turn π on to hear audio samples)
Still very early and does have an issue with hallucinating but results seem pretty good so far, given that it is very early into the training run.
Will probably kick off a new run later with some settings tweaked.
Put up a demo here: https://huggingface.co/spaces/mrfakename/EmoAct-MiMo
(Turn π on to hear audio samples)
-
- 5 replies
multimodalartΒ
posted
an
update
4 months ago
Post
15852
Want to iterate on a Hugging Face Space with an LLM?
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
thomwolfΒ
authored
a
paper
4 months ago
osansevieroΒ
authored
a
paper
4 months ago
sanchit-gandhiΒ
authored
2
papers
7 months ago
thomwolfΒ
authored
a
paper
7 months ago
multimodalartΒ
posted
an
update
8 months ago
Post
18116
Self-Forcing - a real-time video distilled model from Wan 2.1 by
@adobe
is out, and they open sourced it π
I've built a live real time demo on Spaces πΉπ¨
multimodalart/self-forcing
I've built a live real time demo on Spaces πΉπ¨
multimodalart/self-forcing
-
- 6 replies
Post
6428
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub π€―
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! π₯
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! π₯
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
- 1 reply
Post
4302
π ZeroGPU now supports PyTorch native quantization via
While it hasnβt been battle-tested yet,
Let us know if you run into any issues β and weβre excited to see what the community will build!
torchaoWhile it hasnβt been battle-tested yet,
Int8WeightOnlyConfig is already working flawlessly in our tests.Let us know if you run into any issues β and weβre excited to see what the community will build!
import spaces
from diffusers import FluxPipeline
from torchao.quantization.quant_api import Int8WeightOnlyConfig, quantize_
pipeline = FluxPipeline.from_pretrained(...).to('cuda')
quantize_(pipeline.transformer, Int8WeightOnlyConfig()) # Or any other component(s)
@spaces.GPU
def generate(prompt: str):
return pipeline(prompt).images[0]
- 5 replies
thomwolfΒ
authored
a
paper
8 months ago
pyp1Β
authored
a
paper
9 months ago
Post
4702
hey hey
@mradermacher
- VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! π₯
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! π€
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! π€
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- 16 replies