Name | Source | Hours |
---|---|---|
Dataset by University of Edinburgh | https://datashare.ed.ac.uk | 39h(141165.816338) |
VCTK(2009) | https://datashare.ed.ac.uk | 82h(297552.223038) |
LibriSpeech | http://www.openslr.org | 983h(3539995.721648) |
Common Voice | https://commonvoice.mozilla.org | 1837h(6615925.423006) |
The VoxCeleb1 Dataset | https://www.robots.ox.ac.uk | x |
The VoxCeleb2 Dataset | https://www.robots.ox.ac.uk | x |
Name | Source | Hours |
---|---|---|
Common Voice | https://commonvoice.mozilla.org/ | 832h (2995609.871861) |
Name | Source | Hours |
---|---|---|
DEMAND | https://zenodo.org | 8h(28800.384000) |
100 Noise | http://web.cse.ohio-state.edu | 293s(293.299375) |
RIRS_NOISES | https://www.openslr.org | 27h(97661.178407) |
QUT-NOISE | https://research.qut.edu.au | 27h (98262.946746) |
MUSAN | https://www.openslr.org | 48h (175827.483386) |
Deep Noise Suppression (DNS) Challenge - Interspeech 2020 | https://github.com/breizhn/DNS-Challenge | |
Deep Noise Suppression (DNS) Challenge - Interspeech 2022 | https://github.com/microsoft/DNS-Challenge |
Link | Language | Description |
---|---|---|
Data simulation | Python | Add reverberation, noise or mix speaker. |
audio-SNR | Python | Mixing an audio file with a noise file at any Signal-to-Noise Ratio. |
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Transformer
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Speech Enhancement
- Improving GANs for Speech Enhancement 논문
- Improving GANs for Speech Enhancement (페이퍼 관련 소스)
- Self Attention GAN for Speech Enhancement 논문
- Self Attention GAN for Speech Enhancement in Tensorflow 2 (페이퍼 관련 소스)
- Spectral Normalization 논문
- Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification 논문
- To prune, or not to prune: exploring the efficacy of pruning for model compression 논문
- CMGAN: Conformer-based Metric GAN for Speech Enhancement
- MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
- MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
- Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression 논문
- Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression (페이퍼 관련 소스)
- A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement
- SDR – HALF-BAKED OR WELL DONE?
- TasNet: time-domain audio separation network for real-time, single-channel speech separation
- conv1d
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Data Augmentation
-
End to End
- Speech Processing for Machine Learning: Filter backs, Mel-Frequency Cepstral Coefficients(MFCCs) and What's In-Between
- RNN transducer
- What is translation equivariance, and why do we use convolutions to get it?
- Tensorflow-lite signiture 분석
- TFLite Quantization
- TensorFlow Custom Op으로 데이터 변환 최적화하기
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Tensorflow and Tensorflow Lite code in the context of audio processing (MFCC, RNN)
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Audio recognition using Tensorflow Lite in Flutter applications
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Udacity - TensorFlow Lite e-Learning과정
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Coursera - Device-based Models with TensorFlow Lite
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A Definitive Guide for Audio Processing in Android with TensorFlow Lite Models
-
Tensorflow lite
- Quantization
- Inside TensorFlow: TF Model Optimization Toolkit (Quantization and Pruning)
- TensorFlow Model Optimization Toolkit — Pruning API
Bitmap bitmap = Bitmap.createScaledBitmap(yourInputImage, 224, 224, true);
ByteBuffer input = ByteBuffer.allocateDirect(224 * 224 * 3 * 4).order(ByteOrder.nativeOrder());
for (int y = 0; y < 224; y++) {
for (int x = 0; x < 224; x++) {
int px = bitmap.getPixel(x, y);
// Get channel values from the pixel value.
int r = Color.red(px);
int g = Color.green(px);
int b = Color.blue(px);
// Normalize channel values to [-1.0, 1.0]. This requirement depends
// on the model. For example, some models might require values to be
// normalized to the range [0.0, 1.0] instead.
float rf = (r - 127) / 255.0f;
float gf = (g - 127) / 255.0f;
float bf = (b - 127) / 255.0f;
input.putFloat(rf);
input.putFloat(gf);
input.putFloat(bf);
}
}
int bufferSize = 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE;
ByteBuffer modelOutput = ByteBuffer.allocateDirect(bufferSize).order(ByteOrder.nativeOrder());
interpreter.run(input, modelOutput);
...