Gemma 4 Technical Report
Paper • 2607.02770 • Published • 62
AI & ML interests
Decentralized yet unified efforts to accelerate research for Open Text to Speech (TTS) systems!
Recent Activity
osanseviero
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osanseviero
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sanchit-gandhi
authored a
paper 3 months ago
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jpc
authored a
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Post
3187
Just open sourced LavaSR v2: a model that can enhance 5000 seconds of audio in 1 second while being higher quality than giant and slow 6gb diffusion models!
It works with any sampling rate from 8-48khz and is nearly 5000x faster than competition while being superior in objective benchmarks.
LavaSR v2 is Perfect for
- Enhancing TTS models.
- Fixing old audio datasets.
- Restoring low quality recordings.
You can check out the examples and run it locally or online:
Repo: https://github.com/ysharma3501/LavaSR.git
Demo: YatharthS/LavaSR
Model: YatharthS/LavaSR
It works with any sampling rate from 8-48khz and is nearly 5000x faster than competition while being superior in objective benchmarks.
LavaSR v2 is Perfect for
- Enhancing TTS models.
- Fixing old audio datasets.
- Restoring low quality recordings.
You can check out the examples and run it locally or online:
Repo: https://github.com/ysharma3501/LavaSR.git
Demo: YatharthS/LavaSR
Model: YatharthS/LavaSR
sanchit-gandhi
authored a
paper 5 months ago
Post
4575
I just released NovaSR, a tiny 52kb audio upsampler that can enhance 3600 seconds of muffled 16khz audio in to clearer 48khz audio in just 1 second!
NovaSR can
- Enhance TTS model quality.
- Restore poor quality datasets.
- Work on any device(just 52kb which is smaller than a 3 second audio file!)
Model: YatharthS/NovaSR
Space to try it: YatharthS/NovaSR
Github repo: https://github.com/ysharma3501/NovaSR
NovaSR can
- Enhance TTS model quality.
- Restore poor quality datasets.
- Work on any device(just 52kb which is smaller than a 3 second audio file!)
Model: YatharthS/NovaSR
Space to try it: YatharthS/NovaSR
Github repo: https://github.com/ysharma3501/NovaSR
- 5 replies
Post
5484
👉 What happened in AI in 2025? 👈
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:
1️⃣ Q1 — 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)
2️⃣ Q2 — 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
3️⃣ Q3 — "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
4️⃣ Q4 — 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 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
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:
1️⃣ Q1 — 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)
2️⃣ Q2 — 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
3️⃣ Q3 — "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
4️⃣ Q4 — 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 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
- 3 replies
Post
3702
🤯 🤯 Released a high quality finetuned LLM based TTS model that can generate realistic and clear 48khz audio at over 100x realtime speed! 🤯 🤯
Github link: https://github.com/ysharma3501/MiraTTS
Model link: https://github.com/ysharma3501/MiraTTS
Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models
Github link: https://github.com/ysharma3501/MiraTTS
Model link: https://github.com/ysharma3501/MiraTTS
Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models
- 4 replies
Post
2838
I just released LayaCodec, a highly efficient neural audio tokenizer/codec for TTS models, far better than most previous audio tokenizers.
🤯 Next-gen TTS models that use this could achieve several 100s of times real-time speed while producing clearer audio!! 🤯
GitHub repo: https://github.com/ysharma3501/LayaCodec
Model: YatharthS/LayaCodec
🤯 Next-gen TTS models that use this could achieve several 100s of times real-time speed while producing clearer audio!! 🤯
GitHub repo: https://github.com/ysharma3501/LayaCodec
Model: YatharthS/LayaCodec
mrfakename
posted an update 7 months ago
Post
28020
Excited to share that I've joined the Hugging Face Fellows program! 🤗
Looking forward to contributing to & working more closely with the open-source ecosystem - huge thanks to everyone who's supported me on this journey! 🚀
Looking forward to contributing to & working more closely with the open-source ecosystem - huge thanks to everyone who's supported me on this journey! 🚀
reach-vb
authored a
paper 8 months ago
Post
1688
Just uploaded a detailed blog about my findings in optimizing NeuTTS to generate 200 seconds of audio in a single second. Also went in depth in NeuTTS’s architecture. Will be happy to answer any questions.
https://huggingface.co/blog/YatharthS/making-neutts-200x-realtime
https://huggingface.co/blog/YatharthS/making-neutts-200x-realtime
Steveeeeeeen
authored a
paper 8 months ago