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SincNet is a neural architecture for efficiently processing raw audio samples.
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# SincNet SincNet is a neural architecture for processing **raw audio samples**. It is a novel Convolutional Neural Network (CNN) that encourages the first convolutional layer to discover more **meaningful filters**. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a **customized filter bank** specifically tuned for the desired application. This project releases a collection of codes and utilities to perform speaker identification with SincNet. An example of speaker identification with the TIMIT database is provided. If you are interested in **SincNet applied to speech recognition you can take a look into the PyTorch-Kaldi github repository (https://github.com/mravanelli/pytorch-kaldi).** <img src="https://github.com/mravanelli/SincNet/blob/master/SincNet.png" width="400" img align="right"> [Take a look into our video introduction to SincNet](https://www.youtube.com/watch?v=mXQBObRGUgk&feature=youtu.be) ## Cite us If you use this code or part of it, please cite us! *Mirco Ravanelli, Yoshua Bengio, “Speaker Recognition from raw waveform with SincNet”* [Arxiv](http://arxiv.org/abs/1808.00158) ## Prerequisites - Linux - Python 3.6/2.7 - pytorch 1.0 - pysoundfile (``` conda install -c conda-forge pysoundfile```) - We also suggest using the anaconda environment. ## SpeechBrain SincNet is implemented in the SpeechBrain (https://speechbrain.github.io/) project as well. We encourage you to take a look into it as well! It is an all-in-one pytorch-based speech processing toolkit that currently supports speech recognition, speaker recognition, SLU, speech enhancement, speech separation, multi-microphone signal processing. It is designed to be flexible, easy-to-use, modular, and well documented. [Check it out](https://speechbrain.github.io/). ## Updates Feb, 16 2019: - We replaced the old "sinc_conv" with "SincConv_fast". The latter is 50% faster. - In the near future, we plan to support SincNet based speaker-id within the [PyTorch-Kaldi project](https://github.com/mravanelli/pytorch-kaldi) (the current version of the project only supports SincNEt for speech recognition experiments). This will allow users to perform speaker recognition experiments in a faster and much more flexible environment. The current repository will anyway remain as a showcase. ## How to run a TIMIT experiment Even though the code can be easily adapted to any speech dataset, in the following part of the documentation we provide an example based on the popular TIMIT dataset. **1. Run TIMIT data preparation.** This step is necessary to store a version of TIMIT in which start and end silences are removed and the amplitude of each speech utterance is normalized. To do it, run the following code: `` python TIMIT_preparation.py $TIMIT_FOLDER $OUTPUT_FOLDER data_lists/TIMIT_all.scp `` where: - *$TIMIT_FOLDER* is the folder of the original TIMIT corpus - *$OUTPUT_FOLDER* is the folder in which the normalized TIMIT will be stored - *data_lists/TIMIT_all.scp* is the list of the TIMIT files used for training/test the speaker id system. **2. Run the speaker id experiment.** - Modify the *[data]* section of *cfg/SincNet_TIMIT.cfg* file according to your paths. In particular, modify the *data_folder* with the *$OUTPUT_FOLDER* specified during the TIMIT preparation. The other parameters of the config file belong to the following sections: 1. *[windowing]*, that defines how each sentence is split into smaller chunks. 2. *[cnn]*, that specifies the characteristics of the CNN architecture. 3. *[dnn]*, that specifies the characteristics of the fully-connected DNN architecture following the CNN layers. 4. *[class]*, that specify the softmax classification part. 5. *[optimization]*, that reports the main hype
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Mirco Ravanelli · Concordia University/Mila · Canada
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Hervé BREDIN · @pyannoteai and CNRS · France
1
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Seung-won Park · @moloco · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:83ae9b2588393379, topic:speech-recognition, topic:asr, topic:audio-processing
matched fp:83ae9b2588393379, topic:deep-learning, topic:pytorch