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MARS5 TTS: Open Source Text to Speech with insane prosodic control! 🔥 > Voice cloning with less than 5 seconds of audio > Two stage Auto-Regressive (750M) + Non-Auto Regressive (450M) model architecture > Used BPE tokenizer to enable control over punctuations, pauses, stops etc. > AR model predicts... show more

Vaibhav (VB) Srivastav

55,546 subscribers

162,281 views • 2 years ago •via X (Twitter)

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10 Comments

Vaibhav (VB) Srivastav2 years ago

Check out the model here:

Vaibhav (VB) Srivastav2 years ago

GitHub for more deets:

Carlos DP2 years ago

Wow, these outputs are incredible. Like, is this the new SOTA? The samples sound better than the 11labs ones, at least, but idk what params were used

Vaibhav (VB) Srivastav2 years ago

750M + 450M -> pretty lightweight overall, in the GitHub README they promise more updates coming soon :D

Furkan Gözükara2 years ago

5 seconds to clone is always a lie but i can't say for sure without testing i asked them for gradio demo app to be shared

marko.2 years ago

Released under GNU AGPL 3.0, a very curious choice for a model but I'll take it 🎉

Marouane Belkouri2 years ago

Finnetunning code ?

adivina_soy32 years ago

@huggingface Impresionante. Crees que seria posible combinarlo con Hallo?

Thomas Hill2 years ago

Nice share 🔥

STEVE blowJOBS2 years ago

This is racist ask me why

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23,958 views • 2 years ago

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Akshay 🚀

45,256 views • 4 months ago

Tencent presents GameGen-O Open-world Video Game Generation We introduce GameGen-O, the first diffusion transformer model tailored for the generation of open-world video games. This model facilitates high-quality, open-domain generation by simulating a wide array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, thus allowing for the gameplay simulation. The development of GameGen-O involves a comprehensive data collection and processing effort from scratch. We collect and build the first Open-World Video Game Dataset (OGameData), amassed extensive data from over a hundred of next-generation open-world games, employing a proprietary data pipeline for efficient sorting, scoring, filtering, and decoupled captioning. This robust and extensive OGameData forms the foundation of our model's training process. GameGen-O undergoes a two-stage training process, consisting of foundation model pretraining and instruction tuning. In the first phase, the model is pre-trained on the OGameData via the text-to-video and video continuation, endowing GameGen-O with the capability for open-domain video game generation. In the second phase, the pre-trained model is frozen, and we fine-tuned using a trainable InstructNet, which enables the production of subsequent frames based on multimodal structural instructions. This whole training process imparts the model with the ability to generate and interactively control content. In summary, GameGen-O represents a notable initial step forward in the realm of open-world video game generation via generative models. It underscores the potential of generative models to serve as an alternative to rendering techniques, which can efficiently combine creative generation with interactive capabilities.

Tencent presents GameGen-O Open-world Video Game Generation We introduce GameGen-O, the first diffusion transformer model tailored for the generation of open-world video games. This model facilitates high-quality, open-domain generation by simulating a wide array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, thus allowing for the gameplay simulation. The development of GameGen-O involves a comprehensive data collection and processing effort from scratch. We collect and build the first Open-World Video Game Dataset (OGameData), amassed extensive data from over a hundred of next-generation open-world games, employing a proprietary data pipeline for efficient sorting, scoring, filtering, and decoupled captioning. This robust and extensive OGameData forms the foundation of our model's training process. GameGen-O undergoes a two-stage training process, consisting of foundation model pretraining and instruction tuning. In the first phase, the model is pre-trained on the OGameData via the text-to-video and video continuation, endowing GameGen-O with the capability for open-domain video game generation. In the second phase, the pre-trained model is frozen, and we fine-tuned using a trainable InstructNet, which enables the production of subsequent frames based on multimodal structural instructions. This whole training process imparts the model with the ability to generate and interactively control content. In summary, GameGen-O represents a notable initial step forward in the realm of open-world video game generation via generative models. It underscores the potential of generative models to serve as an alternative to rendering techniques, which can efficiently combine creative generation with interactive capabilities.

AK

367,110 views • 1 year ago

VoxCPM 2 just dropped by OpenBMB Only 2B-param open-source TTS (Text-to-Speech) model built for production-grade multilingual voice work. Apache-2.0 license, Can run on only 8GB VRAM. • Eliminates the "robotic" feel of traditional TTS, delivering prosody and emotional depth suitable for high-stakes professional environments like filmmaking, gaming, animation, and audiobooks. • 30-language multilingual: no language tag needed, just type in a supported language and generate directly. • Voice design: create a brand-new voice from a text description alone, like age, tone, pace, or emotion. No reference audio required. Describe the desired voice characteristics (gender, age, tone, emotion, pace …) in Control Instruction, and VoxCPM2 will craft a unique voice from your description alone. • Controllable cloning: clone from a short clip, then steer delivery style without losing the speaker’s core voice. • Ultimate cloning: use reference audio + transcript for continuation-style cloning that keeps the tiny vocal details. • 48kHz output: takes 16kHz reference audio and produces studio-quality speech without an external upsampler. • Real-time ready: around 0.3 RTF on RTX 4090, even lower with Nano-VLLM. • Commercial use: Apache-2.0 licensed. Developer-Friendly Infrastructure: - Native Torch Inference: Direct support for PyTorch-based workflows. - Training Flexibility: Supports both full-parameter and LoRA fine-tuning for specific domain adaptation. - Production Readiness: Compatible with voxcpm-nanovllm for large-scale, high-concurrency deployment.

Rohan Paul

13,541 views • 3 months ago

AI transcribing 1 hour of audio in less than 30 seconds! Think about that! That's lightning-fast, especially when it can do that with very few mistakes. The team Deepgram released their new Nova 2 model. It's another leap forward: • 36% more accurate than OpenAI Whisper • 5-40x faster than every other alternative • 30% fewer errors than the competitors in real-time transcriptions I like to travel and can't wait for headphones with integrated live transcription and translation. We are getting close! By the way, this is the cheapest transcription service out in the market now (if you discount Whisper, which is open-source.) It costs $0.0043 to transcribe a minute of audio. When you sign up to access their API, you get $200 of credit for free, which will let you transcribe around 45,000 minutes of audio! Here is the link: I wanted to record a quick video showing their live transcription in Spanish and English. You can see it attached here. This post was sponsored by Deepgram.

AI transcribing 1 hour of audio in less than 30 seconds! Think about that! That's lightning-fast, especially when it can do that with very few mistakes. The team Deepgram released their new Nova 2 model. It's another leap forward: • 36% more accurate than OpenAI Whisper • 5-40x faster than every other alternative • 30% fewer errors than the competitors in real-time transcriptions I like to travel and can't wait for headphones with integrated live transcription and translation. We are getting close! By the way, this is the cheapest transcription service out in the market now (if you discount Whisper, which is open-source.) It costs $0.0043 to transcribe a minute of audio. When you sign up to access their API, you get $200 of credit for free, which will let you transcribe around 45,000 minutes of audio! Here is the link: I wanted to record a quick video showing their live transcription in Spanish and English. You can see it attached here. This post was sponsored by Deepgram.

Santiago

354,288 views • 2 years ago

Here are 10 AI video editor GitHub repos worth bookmarking: 1. Shotcut Most actively maintained open source video editor in 2026. 14K stars. Cross-platform with AI-assisted features. Just shipped a new release April 30, 2026. 2. Kdenlive The closest open source alternative to Adobe Premiere Pro. Multi-track editing, proxy editing, VST audio, and customizable workspace. Best for professional workflows. 3. OpenShot The easiest entry point for beginners. Drag and drop, 400+ transitions, 3D titles, and AI-assisted trimming. 5,700 stars. 4. Blender Not just 3D. Blender's video sequence editor and compositing pipeline is used in professional film production. 18,300 stars. Unmatched for VFX. 5. Recordly Screen recorder with auto-zoom, cursor polish, webcam overlays, and styled frames built in. Built for demo videos and walkthroughs. 6. Wan2.1 Alibaba's open source text-to-video model. Cinema-grade 1080p generation. Apache 2.0. The gold standard for open source video generation in 2026. 7. HunyuanVideo Tencent's 13B parameter open source video model. 11.9K stars. Handles 720p and 1080p with high temporal coherence. 8. CogVideoX Apache 2.0 licensed. Loads natively via Hugging Face Diffusers. Strong prompt following and smooth frame transitions. Needs 16GB VRAM minimum. 12.5K stars. 9. Open-Sora Most starred open source video generation project at 24K stars. Full training pipeline for $200K. Production-level output quality. 10. Mochi 1 Focused entirely on motion quality. The most natural-looking physics of any open source video model. Water, fabric, and human gestures without AI jitter. Apache 2.0.

Here are 10 AI video editor GitHub repos worth bookmarking: 1. Shotcut Most actively maintained open source video editor in 2026. 14K stars. Cross-platform with AI-assisted features. Just shipped a new release April 30, 2026. 2. Kdenlive The closest open source alternative to Adobe Premiere Pro. Multi-track editing, proxy editing, VST audio, and customizable workspace. Best for professional workflows. 3. OpenShot The easiest entry point for beginners. Drag and drop, 400+ transitions, 3D titles, and AI-assisted trimming. 5,700 stars. 4. Blender Not just 3D. Blender's video sequence editor and compositing pipeline is used in professional film production. 18,300 stars. Unmatched for VFX. 5. Recordly Screen recorder with auto-zoom, cursor polish, webcam overlays, and styled frames built in. Built for demo videos and walkthroughs. 6. Wan2.1 Alibaba's open source text-to-video model. Cinema-grade 1080p generation. Apache 2.0. The gold standard for open source video generation in 2026. 7. HunyuanVideo Tencent's 13B parameter open source video model. 11.9K stars. Handles 720p and 1080p with high temporal coherence. 8. CogVideoX Apache 2.0 licensed. Loads natively via Hugging Face Diffusers. Strong prompt following and smooth frame transitions. Needs 16GB VRAM minimum. 12.5K stars. 9. Open-Sora Most starred open source video generation project at 24K stars. Full training pipeline for $200K. Production-level output quality. 10. Mochi 1 Focused entirely on motion quality. The most natural-looking physics of any open source video model. Water, fabric, and human gestures without AI jitter. Apache 2.0.

Kanika

17,726 views • 1 month ago