speechbrain VS SincNet

Compare speechbrain vs SincNet and see what are their differences.

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speechbrain SincNet
26 3
7,694 1,075
6.6% -
9.8 0.0
4 days ago almost 3 years ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

speechbrain

Posts with mentions or reviews of speechbrain. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-22.

SincNet

Posts with mentions or reviews of SincNet. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-25.

What are some alternatives?

When comparing speechbrain and SincNet you can also consider the following projects:

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

speech-to-text-benchmark - speech to text benchmark framework

NeMo - NeMo: a framework for generative AI

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.

cnnimageretrieval-pytorch - CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch