Talking Face Generation with Multilingual TTS
π
563
Generate multilingual talking-face videos from your text
AI & ML interests
CVPR Demo Track @ CVPR 2022
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deepkyuΒ
updated a
Space 6 days ago
Post
2158
Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you 
Anthropic for supporting open source.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap β issues and PRs welcome.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap β issues and PRs welcome.
Post
5891
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
- 6 replies
AanishaΒ
authored a
paper 2 months ago
ggxxiiΒ
submitted a
paper to Daily Papers 2 months ago
Post
1204
Uhh did Opus 4.8 cheat on PostTrainBench??
it found an API key in the PostTrainBench environment that allowed it to generate synthetic training data without using GPU hours, boosting the base model by 0.4913
Source: https://posttrainbench.com/traces/run.html?id=claude_non_api_max_claude-opus-4-8_10h_run1__healthbench_Qwen_Qwen3-4B-Base_17315102#tab=trace
it found an API key in the PostTrainBench environment that allowed it to generate synthetic training data without using GPU hours, boosting the base model by 0.4913
Source: https://posttrainbench.com/traces/run.html?id=claude_non_api_max_claude-opus-4-8_10h_run1__healthbench_Qwen_Qwen3-4B-Base_17315102#tab=trace
- 1 reply
Post
420
Shipped v0.1.2 of vtx β a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono β same principles, pure Python, no transpiled runtime.
What ships out of the box:
β Textual TUI + headless CLI (vtx -p "fix the failing test")
β 49 LLM provider gateways, all declared in a single provider.yaml
β 5 core tools (read / edit / write / bash / find) plus web search and fetch
β Session tree with compaction, handoff, and resume
β AGENTS.md / CLAUDE.md auto-discovery
β Skills system β drop SKILL.md files in .agents/skills/ and they become slash commands
β Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
β Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono β same principles, pure Python, no transpiled runtime.
What ships out of the box:
β Textual TUI + headless CLI (vtx -p "fix the failing test")
β 49 LLM provider gateways, all declared in a single provider.yaml
β 5 core tools (read / edit / write / bash / find) plus web search and fetch
β Session tree with compaction, handoff, and resume
β AGENTS.md / CLAUDE.md auto-discovery
β Skills system β drop SKILL.md files in .agents/skills/ and they become slash commands
β Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
β Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Post
5065
Sharing how I built the LongCat-Video-Avatar 1.5 Space (+500k views on X) in one agent session. Gave a coding agent its own AI lab on ZeroGPU, framed the goal, walked away. It designed, deployed, tested against the live API, fixed, shipped.
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
- 1 reply
Post
6288
Want to share my enthusiasm for zai-org/GLM-5.1 here too π₯
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
- 5 replies
abidlabsΒ
updated a
Space 5 months ago
1aurentΒ
authored a
paper 5 months ago
elischwartzΒ
authored 9
papers 6 months ago
FETA: Towards Specializing Foundation Models for Expert Task Applications
Paper β’ 2209.03648 β’ Published
Teaching Structured Vision&Language Concepts to Vision&Language Models
Paper β’ 2211.11733 β’ Published
Teaching VLMs to Localize Specific Objects from In-context Examples
Paper β’ 2411.13317 β’ Published
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Paper β’ 2502.09927 β’ Published β’ 1
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark
Paper β’ 2502.12342 β’ Published β’ 7
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers
Paper β’ 2505.22584 β’ Published
Advancing Speech Understanding in Speech-Aware Language Models with GRPO
Paper β’ 2509.16990 β’ Published β’ 22
ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation
Paper β’ 2507.19492 β’ Published β’ 1
CLIMP: Contrastive Language-Image Mamba Pretraining
Paper β’ 2601.06891 β’ Published β’ 3