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# Conv-TasNet
This is a reference implementation of Conv-TasNet.
> Luo, Yi, and Nima Mesgarani. "Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27.8 (2019): 1256-1266. Crossref. Web.
This implementation is based on [arXiv:1809.07454v3](https://arxiv.org/abs/1809.07454v3) and [the reference implementation](https://github.com/naplab/Conv-TasNet) provided by the authors.
For the usage, please checkout the [source separation README](../README.md).
## (Default) Training Configurations
The default training/model configurations follow the non-causal implementation from [Asteroid](https://github.com/asteroid-team/asteroid/tree/master/egs/librimix/ConvTasNet). (causal configuration is not implemented.)
- Sample rate: 8000 Hz
- Batch size: total 12 over distributed training workers
- Epochs: 200
- Initial learning rate: 1e-3
- Gradient clipping: maximum L2 norm of 5.0
- Optimizer: Adam
- Learning rate scheduling: Halved after 5 epochs of no improvement in validation accuracy.
- Objective function: SI-SNR
- Reported metrics: SI-SNRi, SDRi
- Sample audio length: 3 seconds (randomized position)
- Encoder/Decoder feature dimension (N): 512
- Encoder/Decoder convolution kernel size (L): 16
- TCN bottleneck/output feature dimension (B): 128
- TCN hidden feature dimension (H): 512
- TCN skip connection feature dimension (Sc): 128
- TCN convolution kernel size (P): 3
- The number of TCN convolution block layers (X): 8
- The number of TCN convolution blocks (R): 3
- The mask activation function: ReLU
## Evaluation
The following is the evaluation result of training the model on Libri2Mix dataset.
### LibirMix 2speakers
| | Si-SNRi (dB) | SDRi (dB) | Epoch |
|:-------------------:|-------------:|----------:|------:|
| Reference (Asteroid)| 14.7 | 15.1 | 200 |
| torchaudio | 15.3 | 15.6 | 200 |
|
