Scenema Audio: Zero-shot expressive voice cloning and speech generation [N]
Our take
Introducing Scenema Audio: a groundbreaking tool in our video production platform that enables zero-shot expressive voice cloning and speech generation. This innovative model allows you to dictate emotional performance—be it rage, grief, or wonder—while providing a reference audio for voice identity. Even voices unrecorded in specific emotions can deliver authentic performances. While the diffusion model may require post-editing for optimal results, it consistently outshines traditional TTS systems in naturalness.
The release of Scenema Audio, which features zero-shot expressive voice cloning and speech generation, marks a significant step forward in the realm of audio-first video production. By separating emotional performance from voice identity, Scenema Audio enables the generation of nuanced vocal expressions that can resonate with various emotional contexts—be it rage, grief, or excitement. This independence allows creators to tap into a broader spectrum of performances without the constraints typically imposed by traditional voice synthesis methods. The implications of this technology are vast, inviting exploration not only for video producers but also for anyone interested in the evolving landscape of AI-driven content creation. As we see advancements in tools like Build AI Financial Models in Sourcetable and innovations in AI like trained transformer-based chess models, the integration of such expressive technologies can redefine how narratives are constructed and communicated.
A noteworthy feature of Scenema Audio is its reliance on diffusion models, which, despite some limitations, produce a more natural and emotionally resonant output than many autoregressive Text-to-Speech (TTS) systems. While users are cautioned about potential gibberish or repetition, the model encourages an iterative process of generation and selection—similar to traditional post-editing workflows. This not only fosters creativity but also emphasizes the importance of user input in shaping the final product. For creative professionals, this means that rather than merely relying on pre-recorded audio, they can generate voice performances that are tailored to their specific needs, effectively transforming their approach to storytelling. As highlighted in related discussions on audio and video generation, the ability to create compelling audio tracks before video production can streamline workflows and enhance the overall quality of the final output.
Moreover, the accessibility of Scenema Audio through a Docker REST API serves to simplify the integration process for developers and content creators alike. The auto-detection of GPU configurations demonstrates a forward-thinking approach to ensuring that users can maximize the potential of their hardware without grappling with technical intricacies. By adopting a user-centered design philosophy, Scenema Audio not only empowers creators to manipulate audio in innovative ways but also reduces the barriers to entry for those unfamiliar with complex audio processing. This aligns with the broader trend in technology towards democratizing advanced tools, evident in emerging platforms that allow users to build sophisticated models without extensive technical knowledge, as seen in articles like Have the "on-hold" durations been getting longer for arXiv submissions?.
Looking forward, the evolution of voice synthesis technology such as Scenema Audio prompts several critical questions about the future of digital storytelling. How will these advancements influence the authenticity of voice in media? Will they lead to a new set of ethical considerations regarding voice cloning and performance? As users embrace these transformative tools, the conversation surrounding the implications of AI in creative spaces will become increasingly vital. The potential for voice generation to enhance narratives is immense, but it also invites scrutiny on how technology shapes our understanding of identity and expression in the digital age. The journey ahead is not just about improving technology but also about navigating the complex landscape of human emotion and connection that these innovations will undoubtedly impact.
We've been building Scenema Audio as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code.
The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state.
Limitations (and why we still use it)
This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model.
That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery.
Audio-first video generation
As this video points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. Here's an example of that workflow in action.
On distillation and speed
A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds.
Prompting matters
This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a pace parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss.
Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you.
Docker REST API with automatic VRAM management
We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration:
| VRAM | Audio Model | Gemma | Notes |
|---|---|---|---|
| 16 GB | INT8 (4.9 GB) | CPU streaming | Needs 32 GB system RAM |
| 24 GB | INT8 (4.9 GB) | NF4 on GPU | Default config |
| 48 GB | bf16 (9.8 GB) | bf16 on GPU | Best quality |
We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then docker compose up.
ComfyUI
Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service.
Links
- All demos + article: scenema.ai/audio
- Model weights: huggingface.co/ScenemaAI/scenema-audio
- Code + setup: github.com/ScenemaAI/scenema-audio
- YouTube demo: youtu.be/VnEQ_ImOaAc
This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT.
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