Qwen TTS Clone Demo
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Clone and synthesize voice from a sample
optimalprime
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Python, OCR, ASR, video processing
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Smol2Operator: Post-Training GUI Agents for Computer Use
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tomaarsen's
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3 months ago
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2246
โผ๏ธSentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on.
1๏ธโฃ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers
2๏ธโฃ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
3๏ธโฃ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4๏ธโฃ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5๏ธโฃ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
1๏ธโฃ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers
2๏ธโฃ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
model.similarity(embeddings1, embeddings2) and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!3๏ธโฃ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4๏ธโฃ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5๏ธโฃ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
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freddyaboulton's
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Getting WebRTC and Websockets right in python is very tricky. If you've tried to wrap an LLM in a real-time audio layer then you know what I'm talking about.
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc
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as a few of you know - i am working on a rather more elaborate-tts that can produce more interesting sounds in context of rp
early sneak peak is here -
MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000
its based on orpheus - but really the model is irrelevant as i focus mostly on data augmentation / prep / pipelineing - its just the way to show progress
should be able to express fine even in a sfw context
probably the last release for a few weeks as i go back to the data pipeline and improve there ..
in the mean time, please do test and report problems or enjoyable generations you found - we have a growing discord community and i love to see what you get out of that early release !
(small colab is provided on the model page if you dont have the gpu to run that your self)
early sneak peak is here -
MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000
its based on orpheus - but really the model is irrelevant as i focus mostly on data augmentation / prep / pipelineing - its just the way to show progress
should be able to express fine even in a sfw context
probably the last release for a few weeks as i go back to the data pipeline and improve there ..
in the mean time, please do test and report problems or enjoyable generations you found - we have a growing discord community and i love to see what you get out of that early release !
(small colab is provided on the model page if you dont have the gpu to run that your self)
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orasul's
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8 months ago
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hi, it is deki, and now I am open sourced.
An Android AI agent powered by open-source ML model, ๐ฑ๐ฒ๐ธ๐ถ, was fully open-sourced.
It understands whatโs on your screen and can perform tasks based on your voice or text commands.
Some examples:
* "Write my friend "some_name" in WhatsApp that I'll be 15 minutes late"
* "Open Twitter in the browser and write a post about something"
* "Read my latest notifications"
* "Write a linkedin post about something"
Currently, it works only on Android โ but support for other OS is planned.
The ML and backend codes were also fully open-sourced.
Video prompt example:
"Open linkedin, tap post and write: hi, it is deki, and now I am open sourced. But don't send, just return"
License: GPLv3
You can find other AI agent demos or usage examples, like, code generation or object detection in github.
Github: https://github.com/RasulOs/deki
An Android AI agent powered by open-source ML model, ๐ฑ๐ฒ๐ธ๐ถ, was fully open-sourced.
It understands whatโs on your screen and can perform tasks based on your voice or text commands.
Some examples:
* "Write my friend "some_name" in WhatsApp that I'll be 15 minutes late"
* "Open Twitter in the browser and write a post about something"
* "Read my latest notifications"
* "Write a linkedin post about something"
Currently, it works only on Android โ but support for other OS is planned.
The ML and backend codes were also fully open-sourced.
Video prompt example:
"Open linkedin, tap post and write: hi, it is deki, and now I am open sourced. But don't send, just return"
License: GPLv3
You can find other AI agent demos or usage examples, like, code generation or object detection in github.
Github: https://github.com/RasulOs/deki
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8 months ago
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Kimi-Audio ๐๐ง an OPEN audio foundation model released by Moonshot AI
moonshotai/Kimi-Audio-7B-Instruct
โจ 7B
โจ 13M+ hours of pretraining data
โจ Novel hybrid input architecture
โจ Universal audio capabilities (ASR, AQA, AAC, SER, SEC/ASC, end-to-end conversation)
moonshotai/Kimi-Audio-7B-Instruct
โจ 7B
โจ 13M+ hours of pretraining data
โจ Novel hybrid input architecture
โจ Universal audio capabilities (ASR, AQA, AAC, SER, SEC/ASC, end-to-end conversation)
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as-cle-bert's
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8 months ago
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One of the biggest challenges I've been facing since I started developing [๐๐๐๐๐ญ๐๐จ๐ฐ๐ง](https://github.com/AstraBert/PdfItDown) was handling correctly the conversion of files like Excel sheets and CSVs: table conversion was bad and messy, almost unusable for downstream tasks๐ซฃ
That's why today I'm excited to introduce ๐ซ๐๐๐๐๐ซ๐ฌ, the new feature of PdfItDown v1.4.0!๐
With ๐ณ๐ฆ๐ข๐ฅ๐ฆ๐ณ๐ด, you can choose among three (for now๐) flavors of text extraction and conversion to PDF:
- ๐๐ผ๐ฐ๐น๐ถ๐ป๐ด, which does a fantastic work with presentations, spreadsheets and word documents๐ฆ
- ๐๐น๐ฎ๐บ๐ฎ๐ฃ๐ฎ๐ฟ๐๐ฒ by LlamaIndex, suitable for more complex and articulated documents, with mixture of texts, images and tables๐ฆ
- ๐ ๐ฎ๐ฟ๐ธ๐๐๐๐ผ๐๐ป by Microsoft, not the best at handling highly structured documents, by extremly flexible in terms of input file format (it can even convert XML, JSON and ZIP files!)โ๏ธ
You can use this new feature in your python scripts (check the attached code snippet!๐) and in the command line interface as well!๐
Have fun and don't forget to star the repo on GitHub โก๏ธ https://github.com/AstraBert/PdfItDown
That's why today I'm excited to introduce ๐ซ๐๐๐๐๐ซ๐ฌ, the new feature of PdfItDown v1.4.0!๐
With ๐ณ๐ฆ๐ข๐ฅ๐ฆ๐ณ๐ด, you can choose among three (for now๐) flavors of text extraction and conversion to PDF:
- ๐๐ผ๐ฐ๐น๐ถ๐ป๐ด, which does a fantastic work with presentations, spreadsheets and word documents๐ฆ
- ๐๐น๐ฎ๐บ๐ฎ๐ฃ๐ฎ๐ฟ๐๐ฒ by LlamaIndex, suitable for more complex and articulated documents, with mixture of texts, images and tables๐ฆ
- ๐ ๐ฎ๐ฟ๐ธ๐๐๐๐ผ๐๐ป by Microsoft, not the best at handling highly structured documents, by extremly flexible in terms of input file format (it can even convert XML, JSON and ZIP files!)โ๏ธ
You can use this new feature in your python scripts (check the attached code snippet!๐) and in the command line interface as well!๐
Have fun and don't forget to star the repo on GitHub โก๏ธ https://github.com/AstraBert/PdfItDown
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fdaudens's
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8 months ago
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Forget everything you know about transcription models - NVIDIA's parakeet-tdt-0.6b-v2 changed the game for me!
Just tested it with Steve Jobs' Stanford speech and was speechless (pun intended). The video isnโt sped up.
3 things that floored me:
- Transcription took just 10 seconds for a 15-min file
- Got a CSV with perfect timestamps, punctuation & capitalization
- Stunning accuracy (correctly captured "Reed College" and other specifics)
NVIDIA also released a demo where you can click any transcribed segment to play it instantly.
The improvement is significant: number 1 on the ASR Leaderboard, 6% error rate (best in class) with complete commercial freedom (cc-by-4.0 license).
Time to update those Whisper pipelines! H/t @Steveeeeeeen for the finding!
Model: nvidia/parakeet-tdt-0.6b-v2
Demo: nvidia/parakeet-tdt-0.6b-v2
ASR Leaderboard: hf-audio/open_asr_leaderboard
Just tested it with Steve Jobs' Stanford speech and was speechless (pun intended). The video isnโt sped up.
3 things that floored me:
- Transcription took just 10 seconds for a 15-min file
- Got a CSV with perfect timestamps, punctuation & capitalization
- Stunning accuracy (correctly captured "Reed College" and other specifics)
NVIDIA also released a demo where you can click any transcribed segment to play it instantly.
The improvement is significant: number 1 on the ASR Leaderboard, 6% error rate (best in class) with complete commercial freedom (cc-by-4.0 license).
Time to update those Whisper pipelines! H/t @Steveeeeeeen for the finding!
Model: nvidia/parakeet-tdt-0.6b-v2
Demo: nvidia/parakeet-tdt-0.6b-v2
ASR Leaderboard: hf-audio/open_asr_leaderboard
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