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* init files * add ctc as auxiliary loss and ctc_decode.py * tuning the scalar of HLG score for 1best, nbest and nbest-oracle * rename to pruned_transducer_stateless7_ctc * fix doc * fix bug, recover the hlg scores * modify ctc_decode.py, move out the hlg scale * fix hlg_scale * add export.py and pretrained.py, and so on * upload files, update README.md and RESULTS.md * add CI test
37 lines
3.5 KiB
Markdown
37 lines
3.5 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_transducer_stateless7` | Zipformer | Embedding + Conv1d | First experiment with Zipformer from Dan|
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| `pruned_transducer_stateless7_ctc` | Zipformer | Embedding + Conv1d | Same as pruned_transducer_stateless7, but with extra CTC head|
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| `pruned_transducer_stateless8` | Zipformer | Embedding + Conv1d | Same as pruned_transducer_stateless7, but using extra data from GigaSpeech|
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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` | ConvEmformer | Embedding + Conv1d | Using ConvEmformer for streaming ASR + mechanisms in reworked model |
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| `conv_emformer_transducer_stateless2` | ConvEmformer | Embedding + Conv1d | Using ConvEmformer with simplified memory for streaming ASR + mechanisms in reworked model |
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| `lstm_transducer_stateless` | LSTM | Embedding + Conv1d | Using LSTM with mechanisms in reworked model |
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| `lstm_transducer_stateless2` | LSTM | Embedding + Conv1d | Using LSTM with mechanisms in reworked model + gigaspeech (multi-dataset setup) |
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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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