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游雁
2023-09-01 22:46:46 +08:00
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docs/model_zoo/modelscope_models.md
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@@ -54,6 +54,7 @@ Here we provided several pretrained models on different datasets. The details of
#### Conformer
| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |
|:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary) | CN | AISHELL (178hours) | 4234 | 44M | Offline | Duration of input wav <= 20s |
docs/reference/papers.md
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@@ -6,7 +6,9 @@ FunASR have implemented the following paper code
- [FunASR: A Fundamental End-to-End Speech Recognition Toolkit](https://arxiv.org/abs/2305.11013), INTERSPEECH 2023
- [BAT: Boundary aware transducer for memory-efficient and low-latency ASR](https://arxiv.org/abs/2305.11571), INTERSPEECH 2023
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317), INTERSPEECH 2022
- [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020.
- [E-branchformer: Branchformer with enhanced merging for speech recognition](https://arxiv.org/abs/2210.00077), SLT 2022
- [Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding](https://proceedings.mlr.press/v162/peng22a.html?ref=https://githubhelp.com), ICML 2022
- [Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model](https://arxiv.org/abs/2010.14099), arXiv preprint arXiv:2010.14099, 2020
- [San-m: Memory equipped self-attention for end-to-end speech recognition](https://arxiv.org/pdf/2006.01713), INTERSPEECH 2020
- [Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition](https://arxiv.org/abs/2006.01712), INTERSPEECH 2020
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100), INTERSPEECH 2020