Post
716
Esoteric practices: inference models in PHP!
Repository: https://github.com/egorsmkv/speech-to-text-using-php
Repository: https://github.com/egorsmkv/speech-to-text-using-php
Karaoke POC
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karaokepoc's activity
Post
2431
Made a workable program that uses IREE runtime using Rust to inference wav2vec2-bert model for Automatic Speech Recognition.
- 1 reply
Post
2693
I have made a Rust project with integration of the latest state-of-the-art model for object detection, it outperforms YOLO!
Check it out: https://github.com/egorsmkv/rf-detr-usls
Check it out: https://github.com/egorsmkv/rf-detr-usls
- 2 replies
Post
2104
Convert your audio data to Parquet/DuckDB files with blazingly fast speeds!
Repository with pre-built binaries: https://github.com/crs-org/audios-to-dataset
Repository with pre-built binaries: https://github.com/crs-org/audios-to-dataset
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- 2 replies
Post
2257
Create spectrogram using Rust!
Slightly improved nice project that creates spectrogram and built binaries for different platform using cross-rs I've mentioned earlier in my channel.
Repo: https://github.com/crs-org/sonogram
Slightly improved nice project that creates spectrogram and built binaries for different platform using cross-rs I've mentioned earlier in my channel.
Repo: https://github.com/crs-org/sonogram
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- 1 reply
Post
668
Added more built executables to extract-audio I've released recently.
See my previous post - https://huggingface.co/posts/Yehor/654118712490771
Repository: https://github.com/crs-org/extract-audio
See my previous post - https://huggingface.co/posts/Yehor/654118712490771
Repository: https://github.com/crs-org/extract-audio
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- 1 reply
Post
1946
Made a simple Python script to generate Argilla project for audio annotation from a dataset:
https://github.com/egorsmkv/argilla-audio-annotation
https://github.com/egorsmkv/argilla-audio-annotation
- 1 reply
Post
2052
Are you interesting in different runtimes for AI models?
Check out IREE (iree.dev), it convert models to MLIR and then execute on different platforms.
I have tested it in Rust on CPU and CUDA: https://github.com/egorsmkv/eerie-yolo11
Check out IREE (iree.dev), it convert models to MLIR and then execute on different platforms.
I have tested it in Rust on CPU and CUDA: https://github.com/egorsmkv/eerie-yolo11
Post
2235
Extract audio datasets with Rust on blazingly fast speeds!
With this tool you can extract audio files from a parquet or arrow file generated by Hugging Face datasets library.
Repository: https://github.com/egorsmkv/extract-audio
With this tool you can extract audio files from a parquet or arrow file generated by Hugging Face datasets library.
Repository: https://github.com/egorsmkv/extract-audio
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- 1 reply
Post
622
If you spent a lot of time in Telegram, then use this bot to monitor state of your ML lab:
https://github.com/egorsmkv/gpu-state-tgbot
https://github.com/egorsmkv/gpu-state-tgbot
Post
1515
Published some datasets for researchers in Ukrainian NLP from my project https://ua-lawyer.com (Q&A platform in Ukraine):
Datasets:
- ua-l/topics
- ua-l/topics-train-test
- ua-l/topics-text-label
Model:
- https://huggingface.co/ua-l/topics-classifier
Space:
- https://huggingface.co/spaces/ua-l/topics-classifier-demo
Datasets:
- ua-l/topics
- ua-l/topics-train-test
- ua-l/topics-text-label
Model:
- https://huggingface.co/ua-l/topics-classifier
Space:
- https://huggingface.co/spaces/ua-l/topics-classifier-demo
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- 1 reply
Post
2884
Published a stable version of Ukrainian Text-to-Speech library on GitHub and PyPI.
Features:
- Multi-speaker model: 2 female (Tetiana, Lada) + 1 male (Mykyta) voices;
- Fine-grained control over speech parameters, including duration, fundamental frequency (F0), and energy;
- High-fidelity speech generation using the RAD-TTS++ acoustic model;
- Fast vocoding using Vocos;
- Synthesizes long sentences effectively;
- Supports a sampling rate of 44.1 kHz;
- Tested on Linux environments and Windows/WSL;
- Python API (requires Python 3.9 or later);
- CUDA-enabled for GPU acceleration.
Repository: https://github.com/egorsmkv/tts_uk
Features:
- Multi-speaker model: 2 female (Tetiana, Lada) + 1 male (Mykyta) voices;
- Fine-grained control over speech parameters, including duration, fundamental frequency (F0), and energy;
- High-fidelity speech generation using the RAD-TTS++ acoustic model;
- Fast vocoding using Vocos;
- Synthesizes long sentences effectively;
- Supports a sampling rate of 44.1 kHz;
- Tested on Linux environments and Windows/WSL;
- Python API (requires Python 3.9 or later);
- CUDA-enabled for GPU acceleration.
Repository: https://github.com/egorsmkv/tts_uk
Post
634
Added Advanced options to RAD-TTS++ space, so you can synthesize Ukrainian voices precisely.
Space: https://huggingface.co/spaces/Yehor/radtts-uk-vocos-demo
Space: https://huggingface.co/spaces/Yehor/radtts-uk-vocos-demo
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- 5 replies