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solve the conflicts
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@ -20,11 +20,8 @@ We provide 6 recipes at present:
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- [TIMIT][timit]
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- [TED-LIUM3][tedlium3]
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- [GigaSpeech][gigaspeech]
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<<<<<<< HEAD
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- [Aidatatang_200zh][aidatatang_200zh]
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- [WenetSpeech][wenetspeech]
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=======
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>>>>>>> master
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### yesno
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@ -222,7 +219,6 @@ and [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned R
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| fast beam search | 10.50 | 10.69 |
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| modified beam search | 10.40 | 10.51 |
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<<<<<<< HEAD
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### Aidatatang_200zh
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We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aidatatang_200zh_pruned_transducer_stateless2].
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@ -250,8 +246,6 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder
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| modified beam search | 7.76 | 8.71 | 13.41 |
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
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=======
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>>>>>>> master
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## Deployment with C++
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@ -286,9 +280,6 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
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[timit]: egs/timit/ASR
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[tedlium3]: egs/tedlium3/ASR
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[gigaspeech]: egs/gigaspeech/ASR
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<<<<<<< HEAD
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[aidatatang_200zh]: egs/aidatatang_200zh/ASR
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[wenetspeech]: egs/wenetspeech/ASR
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=======
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>>>>>>> master
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[k2]: https://github.com/k2-fsa/k2
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