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* copy files from existing branch * add rule in .flake8 * monir style fix * fix typos * add tail padding * refactor, use fixed-length cache for batch decoding * copy from streaming branch * copy from streaming branch * modify emformer states stack and unstack, streaming decoding, to be continued * refactor Stream class * remane streaming_feature_extractor.py * refactor streaming decoding * test states stack and unstack * fix bugs, no grad, and num_proccessed_frames * add modify_beam_search, fast_beam_search * support torch.jit.export * use torch.div * copy from pruned_transducer_stateless4 * modify export.py * add author info * delete other test functions * minor fix * modify doc * fix style * minor fix doc * minor fix * minor fix doc * update RESULTS.md * fix typo * add info * fix typo * fix doc * add test function for conv module, and minor fix. * add copyright info * minor change of test_emformer.py * fix doc of stack and unstack, test case with batch_size=1 * update README.md
32 lines
2.7 KiB
Markdown
32 lines
2.7 KiB
Markdown
# Introduction
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Please refer to <https://icefall.readthedocs.io/en/latest/recipes/librispeech/index.html> for how to run models in this recipe.
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[./RESULTS.md](./RESULTS.md) contains the latest results.
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# Transducers
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There are various folders containing the name `transducer` in this folder.
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The following table lists the differences among them.
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| | Encoder | Decoder | Comment |
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|---------------------------------------|---------------------|--------------------|---------------------------------------------------|
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| `transducer` | Conformer | LSTM | |
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| `transducer_stateless` | Conformer | Embedding + Conv1d | Using optimized_transducer from computing RNN-T loss |
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| `transducer_stateless2` | Conformer | Embedding + Conv1d | Using torchaudio for computing RNN-T loss |
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| `transducer_lstm` | LSTM | LSTM | |
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| `transducer_stateless_multi_datasets` | Conformer | Embedding + Conv1d | Using data from GigaSpeech as extra training data |
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| `pruned_transducer_stateless` | Conformer | Embedding + Conv1d | Using k2 pruned RNN-T loss |
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| `pruned_transducer_stateless2` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss |
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| `pruned_transducer_stateless3` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss + using GigaSpeech as extra training data |
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| `pruned_transducer_stateless4` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless2 + save averaged models periodically during training |
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| `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner|
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| `pruned_transducer_stateless6` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + distillation with hubert|
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| `pruned_stateless_emformer_rnnt2` | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR|
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| `conv_emformer_transducer_stateless` | Emformer | Embedding + Conv1d | Using Emformer augmented with convolution for streaming ASR + mechanisms in reworked model |
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The decoder in `transducer_stateless` is modified from the paper
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[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
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We place an additional Conv1d layer right after the input embedding layer.
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