eq_harry
speechbrain
| eq_harry | speechbrain | |
|---|---|---|
| 5 | 26 | |
| 0 | 7,892 | |
| - | 2.5% | |
| 0.0 | 9.8 | |
| over 2 years ago | 8 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
eq_harry
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Cooler Master MH752 Review!
Ya boi just got his dekoni nuggets and slapped it on the mh 752; this in combination with a custom eq sets me up good for a Friday night.
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Sennheiser HD560s thoughts from a beginner. Do I just not like 'analytical'?
If you'd like to try some of the fruits of my work (560s are my main after eq), the doors are open.
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Short review for almost every „standard“ headphone up to 350 Euros
Some time went by and I gave it another chance, and with a custom made eq.
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Need help 🥲 please!
Sidenote, since we are on the headphones subreddit, the headset I use is voiced and tuned to accentuate intelligibility and vocal clarity, so do not expect it to have booming bass or a flat response. With that being said, I find the lack of bass to be a huge plus, since some plantronics headsets I have used in the past have had enough bass to give me headaches during longer calls. Additionally, with the right eq, the Sennheisers can sound relatively impressive as an "open back" on ear kind of headset. HD 800 killer confirmed /s!
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How to eq headphones?
If you are feeling adventurous and would like to eq headphones according to the HRTF instead of a standard like diffuse or harman, feel free to poke around in this repo.
speechbrain
- SpeechBrain 1.0: A free and open-source AI toolkit for all things speech
- FLaNK Stack Weekly 22 January 2024
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[D] Training ASR model using SpeechBrain
You likely have a very broken sample in one of your batches. It looks like your training actually went through a few batches before it horked the error at you. A quick google shows a similar issue in the github repo: https://github.com/speechbrain/speechbrain/issues/649 .
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Whisper.cpp
https://github.com/ggerganov/whisper.cpp https://speechbrain.github.io/
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[D] What is the best open source text to speech model?
I don't know if it's the best, but Speechbrain is supposed to be state of the art.
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[D] What's stopping you from working on speech and voice?
- https://github.com/speechbrain/speechbrain
- Specific Voice recognition
- How to get high-quality, low-cost Speech-to-Text transcription?
- [D] Speech Enhancement SOTA
- Speaker diarization
What are some alternatives?
espnet - End-to-End Speech Processing Toolkit
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
Resemblyzer - A python package to analyze and compare voices with deep learning
ukrainian-onnx-model - An ONNX model for speech recognition of the Ukrainian language
SincNet - SincNet is a neural architecture for efficiently processing raw audio samples.
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
speech-to-text-benchmark - speech to text benchmark framework
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
imgaug - Image augmentation for machine learning experiments.
denoiser - Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
AugLy - A data augmentations library for audio, image, text, and video.