Croissant: A Metadata Format for ML-Ready Datasets
Paper
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2403.19546
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Published
•
1
huggingPartyParis
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lhoestq
authored
a
paper
30 days ago
Post
1722
Made a HF Dataset editor a la gg sheets here:
lhoestq/dataset-spreadsheets
With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python
Available for the 100,000+ parquet datasets on HF :)
With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python
Available for the 100,000+ parquet datasets on HF :)
leonsick
authored
3
papers
about 2 months ago
Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design
Paper
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2309.01408
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Published
Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics
Paper
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2403.11821
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Published
•
3
CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation
Paper
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2411.16319
•
Published
glample
authored
a
paper
3 months ago
devendrachaplot
authored
a
paper
3 months ago
ymxlzgy
authored
2
papers
4 months ago
imthanhlv
authored
a
paper
5 months ago
Post
3366
Everchanging Quest is out !
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
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
- 2 replies
Post
4124
Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here:
infinite-dataset-hub/infinite-dataset-hub
Question for the Community:
Which models should I use to generate images and audio samples for those datasets ? 🤗
Question for the Community:
Which models should I use to generate images and audio samples for those datasets ? 🤗
-
- 4 replies
Parm1990
authored
4
papers
7 months ago
Structure and Content-Guided Video Synthesis with Diffusion Models
Paper
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2302.03011
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Published
Bayesian active learning for production, a systematic study and a reusable library
Paper
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2006.09916
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Published
•
1
Can Active Learning Preemptively Mitigate Fairness Issues?
Paper
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2104.06879
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Published
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
Paper
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2106.12059
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Published
martin-r-oswald
authored
a
paper
7 months ago
patricklabatut
authored
a
paper
8 months ago
helboukkouri
authored
a
paper
9 months ago
Post
6735
Yesterday, we released Parler-TTS and Data-Speech, fully open-source reproduction of work from the paper:
Natural language guidance of high-fidelity text-to-speech with synthetic
annotations (2402.01912)
Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc).
https://huggingface.co/collections/parler-tts/parler-tts-fully-open-source-high-quality-tts-models-66164ad285ba03e8ffde214c
Parler-TTS Mini v0.1, is the first iteration Parler-TTS model trained using 10k hours of narrated audiobooks. It generates high-quality speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech. The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention and torch compile.
parler-tts/parler_tts_mini_v0.1
Data-Speech can be used for annotating speech characteristics in a large-scale setting.
parler-tts/open-source-speech-datasets-annotated-using-data-speech-661648ffa0d3d76bfa23d534
This work is both scalable and easily modifiable and will hopefully help the TTS research community explore new ways of conditionning speech synthesis.
All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.
Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc).
https://huggingface.co/collections/parler-tts/parler-tts-fully-open-source-high-quality-tts-models-66164ad285ba03e8ffde214c
Parler-TTS Mini v0.1, is the first iteration Parler-TTS model trained using 10k hours of narrated audiobooks. It generates high-quality speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech. The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention and torch compile.
parler-tts/parler_tts_mini_v0.1
Data-Speech can be used for annotating speech characteristics in a large-scale setting.
parler-tts/open-source-speech-datasets-annotated-using-data-speech-661648ffa0d3d76bfa23d534
This work is both scalable and easily modifiable and will hopefully help the TTS research community explore new ways of conditionning speech synthesis.
All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.