| # Module Introduction |
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| Here is a brief introduction of each module(directory). |
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| * `bin`: training and recognition binaries |
| * `dataset`: IO design |
| * `utils`: common utils |
| * `transformer`: the core of `WeNet`, in which the standard transformer/conformer is implemented. It contains the common blocks(backbone) of speech transformers. |
| * transformer/attention.py: Standard multi head attention |
| * transformer/embedding.py: Standard position encoding |
| * transformer/positionwise_feed_forward.py: Standard feed forward in transformer |
| * transformer/convolution.py: ConvolutionModule in Conformer model |
| * transformer/subsampling.py: Subsampling implementation for speech task |
| * `transducer`: transducer implementation |
| * `squeezeformer`: squeezeformer implementation, please refer [paper](https://arxiv.org/pdf/2206.00888.pdf) |
| * `efficient_conformer`: efficient conformer implementation, please refer [paper](https://arxiv.org/pdf/2109.01163.pdf) |
| * `paraformer`: paraformer implementation, please refer [paper](https://arxiv.org/pdf/1905.11235.pdf) |
| * `paraformer/cif.py`: Continuous Integrate-and-Fire implemented, please refer [paper](https://arxiv.org/pdf/1905.11235.pdf) |
| * `branchformer`: branchformer implementation, please refer [paper](https://arxiv.org/abs/2207.02971) |
| * `whisper`: whisper implementation, please refer [paper](https://arxiv.org/abs/2212.04356) |
| * `ssl`: Self-supervised speech model implementation. e.g. wav2vec2, bestrq, w2vbert. |
| * `ctl_model`: Enhancing the Unified Streaming and Non-streaming Model with with Contrastive Learning implementation [paper](https://arxiv.org/abs/2306.00755) |
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| `transducer`, `squeezeformer`, `efficient_conformer`, `branchformer` and `cif` are all based on `transformer`, |
| they resue a lot of the common blocks of `tranformer`. |
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| **If you want to contribute your own x-former, please reuse the current code as much as possible**. |
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