narrationbox

Joined 6 years ago

Karma
193
Submissions
108
Member since
Jan 30
We do generative ML, specializing in speech technology.

https://narrationbox.com

Cost effective voiceovers and narrations at scale. Voices for agentic AI and audiobooks.

Check out our blog here: https://narrationbox.com/blog

Follow us at: https://twitter.com/narrationbox

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Recent submissions

Comment · 27 days ago
Could be interesting, though spectrogram voice gen is a step back in terms of advancement. Vocoder models that take in Mel Specs suffer from a lot of issues like hissing due to Griffin Lim issues. Would be curious to see if diffusion models can work on neural codecs directly.

I think Google had one called riffusion (the first version was designed for specs)

Comment · 27 days ago
Plenty of models take in text + audio and spits out audio. It's the format of most newer generation accent conversion/voice cloning models.

What's your exact use case?

Comment · 2 months ago
Are you still working on it?
Comment · 2 months ago
Haven't read the full report yet, just a quick question. Are your numbers for cold start without pre fill or is it after warmed cache?
Comment · 2 months ago
> Typical production voice agent is an ensemble of three models: STT, an LLM, and TTS.

To use a claudism, I would like to push back on this. The industry is very much moving towards one-model-does-all end to end trained similar to LLMs and VLMs. Mostly for latency reasons and partially because the results for the end to end trained models are just so much better than those using three pieces architectures.

I think most of the value prop is in automatic evals, not routing specifically. A better pitch for you would be "the LM Arena of voice models" rather than comparing yourself to openrouter because the value add is rather questionable. For TTS specifically, the current SOTA for production systems are all using prompt based voice gen i.e. instead of having 10 different Tacotron models trained on 10 different models, these days it's all a single large model and the "style" is a prompt in the system prompt. The input is usually something like

  <System prompt>
  Speak in a deep smooth voice similar to a documentary narrator
  </System prompt>
  <Text to Narrate>
  Speko is the ultimate evaluation platform for voice agents. We do automatic  evals.
  </Text to Narrate>
It's the same for voice cloning too, you just pass the reference speech as an input file for all generations. A lot of systems don't have any separate style vector extraction step or model-specific fine-tuning anymore.

So something like OpenRouter for voices offer questionable value given that stakeholders usually make this sort of decisions once at the start of the project. On the other hand if you can offer automatic evals and figure out which prompts give the most similar results across different voice providers, that would offer a lot more value. It would be nice to be able to switch from e.g. Grok voice agents to ChatGPT voice agents knowing that the output style won't change too much. There are many companies now with evals as a core business model: LM Arena, Artificial Analysis, Prompt foo (before they got acquired and pivoted to security only) so many take a look at them.

Source: we have been building TTS systems for over a decade too https://narrationbox.com

Comment · 6 months ago
Yes, the SOTA is currently much more advanced.
Comment · 1 year ago
Comment · 2 years ago
Comment · 3 years ago
Yeah, neural codecs are pretty amazing. The most incredible part is that they can do compression well across the temporal domain, something which has been non-trivial.
Comment · 3 years ago
Their sentence segmentation heuristics were not configured correctly. It's not an inherent limitation of the technology itself.

The newer transformer based generators are a bit better in this regard (since they can maintain a longer context window, not just in short tiny snippets).

Comment · 3 years ago
Mel + multispeaker vocoder is very much a classic (tacotron era) TTS approach
Comment · 3 years ago
Comment · 3 years ago
> Recognizes "human" and recognizes "desk". I sit on desk. Does AI mark it as a desk or as a chair?

Not an issue if the image segmentation is advanced enough. You can train the model to understand "human sitting". It may not generalize to other animals sitting but human action recognition is perfectly possible right now.

Comment · 4 years ago
Your average mobile processor doesn't have anywhere near enough processing power to run a state of the art text to speech network in real-time. Most text to speech on mobile hardware are stream from the cloud.
Comment · 5 years ago
For the high end stuff no, but many of the lower tier jobs are under threat.

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