mrfakename PRO
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The refreshed UI for the leaderboard is smoother and (hopefully) more intuitive. You can now view models based on a simpler win-rate percentage and exclude closed models.
In addition, the TTS Arena now supports keyboard shortcuts. This should make voting much more efficient as you can now vote without clicking anything!
In both the normal Arena and Battle Mode, press "r" to select a random text, Cmd/Ctrl + Enter to synthesize, and "a"/"b" to vote! View more details about keyboard shortcuts by pressing "?" (Shift + /) on the Arena.
Check out all the new updates on the TTS Arena:
TTS-AGI/TTS-Arena
Hi, do you see a limit in the number of voices I have 416 and it fails to load all of them. (scroll menu limit?)
I'm not sure if there's a set limit for the dropdown, but with that many voices, it might make sense to not use the dropdown but instead have a textbox to specify the path to the reference speaker.
I don't think that's supported by the model, but you could fine-tune it or clone a voice with emotions. (I am not the author of the model itself, just of the web demo)
Hi,
You can upload a WAV file to the voices
folder. Then, in the app.py
file, add the filename of the voice (without .wav
) to the voicelist
list. It should show up in the Gradio demo.
Hi,
I added:
import nltk
nltk.download('punkt_tab')
and it seems to resolve the issue for me. Have you changed any code from the original Space?
Thanks!
Hi,
Sorry about the issues! Please try adding:
nltk.download('punkt_tab')
below the nltk.download()
line – let me know if it works!
Moonshine is a fast, efficient, & accurate ASR model released by Useful Sensors. It's designed for on-device inference and licensed under the MIT license!
HF Space (unofficial demo): mrfakename/Moonshine
GitHub repo for Moonshine: https://github.com/usefulsensors/moonshine
Training itself would be pretty easy, but the main issue would be data. AFAIK there's not much data out there for other TTS models. I synthetically generated the StyleTTS 2 dataset as it's quite efficient but other models would require much more compute.
It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.
Come test it on the space :
Jofthomas/Everchanging-Quest
I was inspired by the TTS-AGI/TTS-Arena (definitely check it out if you haven't), which compares recent TTS system using crowdsourced A/B testing.
I wanted to see if we can also do a similar evaluation with objective metrics and it's now available here:
ttsds/benchmark
Anyone can submit a new TTS model, and I hope this can provide a way to get some information on which areas models perform well or poorly in.
The paper with all the details is available here: https://arxiv.org/abs/2407.12707
Congratulations!
Dual-licensed under MIT/Apache 2.0.
Model Weights: mrfakename/styletts2-detector
Spaces: mrfakename/styletts2-detector
@mahiatlinux is correct. But it can also be used if you have a classification filter and need an explanation on why a message is blocked.
I don’t think so, it’s the same model just without image generation
Hi,
I think image generation is only available to Plus subscribers. I'm on the Free plan, so I'm experiencing similar issues. It will generate links unless you're a subscriber.
Hi, thanks for your interest in the dataset. Actually the dataset is not designed for guardrailing and the prompts it refuses are completely innocuous. I took the Capybara dataset and generated refusals to all questions. The model is trained to provide explanations on why it can’t do things, not act as a filter. Thanks!