LavaSR
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Ultra fast universal speech enhancement model.
MeyerAliba
Meyer54
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Update README.md
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#3 opened 25 days ago
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Meyer54
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YatharthS's
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about 1 month ago
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4401
I just released NovaSR, a tiny 52kb audio upsampler that can enhance 3600 seconds of muffled 16khz audio in to clearer 48khz audio in just 1 second!
NovaSR can
- Enhance TTS model quality.
- Restore poor quality datasets.
- Work on any device(just 52kb which is smaller than a 3 second audio file!)
Model: YatharthS/NovaSR
Space to try it: YatharthS/NovaSR
Github repo: https://github.com/ysharma3501/NovaSR
NovaSR can
- Enhance TTS model quality.
- Restore poor quality datasets.
- Work on any device(just 52kb which is smaller than a 3 second audio file!)
Model: YatharthS/NovaSR
Space to try it: YatharthS/NovaSR
Github repo: https://github.com/ysharma3501/NovaSR
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LLM based Audio models
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YatharthS's
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about 2 months ago
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3692
🤯 🤯 Released a high quality finetuned LLM based TTS model that can generate realistic and clear 48khz audio at over 100x realtime speed! 🤯 🤯
Github link: https://github.com/ysharma3501/MiraTTS
Model link: https://github.com/ysharma3501/MiraTTS
Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models
Github link: https://github.com/ysharma3501/MiraTTS
Model link: https://github.com/ysharma3501/MiraTTS
Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models
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etemiz's
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2 months ago
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1728
Today's winner is Ling 1T with a score of 38!
Btw AHA2 is in the works, with more domains, better comparison LLMs and questions, overall better signal.
Btw AHA2 is in the works, with more domains, better comparison LLMs and questions, overall better signal.
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martinsu's
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2 months ago
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3320
https://huggingface.co/blog/martinsu/potus-broke-my-pipeline
How POTUS Completely Broke My Flash 2.5-Based Guardrail
Did quite a bit of deep research on this one, since it IMHO matters. At first I used this story to amuse fellow MLOps guys, but then I went deeper and was surprised.
To those who don't want to read too much, in plain English: when you give the model a high-stakes statement that clashes with what it "knows" about the world, it gets more brittle. Sometimes to a point of being unusable.
Or an even shorter version: do not clash with the model's given worldview—it will degrade to some extent.
And in practice, it means that in lower-resource languages like Latvian and Finnish (and probably others), Flash 2.5 is an unreliable guardrail model when something clashes with the model's general "worldview".
However, I'm sure this degradation applies to other languages and models as well to varying extents.
In one totally normal week of MLOps, my news summarization pipeline started failing intermittently. Nothing was changed. No deploys. No prompt edits. No model version bump (as far as I could tell). Yet the guardrail would suddenly turn into a grumpy judge and reject outputs for reasons that felt random, sometimes even contradicting itself between runs. It was the worst kind of failure: silent, flaky, and impossible to reproduce on demand.
Then I noticed the pattern: it started when one specific named entity appeared in the text — Donald Trump ** (**and later in tests — Bernie Sanders too ).
And then down the rabbit hole I went.
How POTUS Completely Broke My Flash 2.5-Based Guardrail
Did quite a bit of deep research on this one, since it IMHO matters. At first I used this story to amuse fellow MLOps guys, but then I went deeper and was surprised.
To those who don't want to read too much, in plain English: when you give the model a high-stakes statement that clashes with what it "knows" about the world, it gets more brittle. Sometimes to a point of being unusable.
Or an even shorter version: do not clash with the model's given worldview—it will degrade to some extent.
And in practice, it means that in lower-resource languages like Latvian and Finnish (and probably others), Flash 2.5 is an unreliable guardrail model when something clashes with the model's general "worldview".
However, I'm sure this degradation applies to other languages and models as well to varying extents.
In one totally normal week of MLOps, my news summarization pipeline started failing intermittently. Nothing was changed. No deploys. No prompt edits. No model version bump (as far as I could tell). Yet the guardrail would suddenly turn into a grumpy judge and reject outputs for reasons that felt random, sometimes even contradicting itself between runs. It was the worst kind of failure: silent, flaky, and impossible to reproduce on demand.
Then I noticed the pattern: it started when one specific named entity appeared in the text — Donald Trump ** (**and later in tests — Bernie Sanders too ).
And then down the rabbit hole I went.
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YatharthS's
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2 months ago
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2837
I just released LayaCodec, a highly efficient neural audio tokenizer/codec for TTS models, far better than most previous audio tokenizers.
🤯 Next-gen TTS models that use this could achieve several 100s of times real-time speed while producing clearer audio!! 🤯
GitHub repo: https://github.com/ysharma3501/LayaCodec
Model: YatharthS/LayaCodec
🤯 Next-gen TTS models that use this could achieve several 100s of times real-time speed while producing clearer audio!! 🤯
GitHub repo: https://github.com/ysharma3501/LayaCodec
Model: YatharthS/LayaCodec