mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-10 02:22:17 +00:00
f2f5baf687
* add ScaledLSTM * add RNNEncoderLayer and RNNEncoder classes in lstm.py * add RNN and Conv2dSubsampling classes in lstm.py * hardcode bidirectional=False * link from pruned_transducer_stateless2 * link scaling.py pruned_transducer_stateless2 * copy from pruned_transducer_stateless2 * modify decode.py pretrained.py test_model.py train.py * copy streaming decoding files from pruned_transducer_stateless2 * modify streaming decoding files * simplified code in ScaledLSTM * flat weights after scaling * pruned2 -> pruned4 * link __init__.py * fix style * remove add_model_arguments * modify .flake8 * fix style * fix scale value in scaling.py * add random combiner for training deeper model * add using proj_size * add scaling converter for ScaledLSTM * support jit trace * add using averaged model in export.py * modify test_model.py, test if the model can be successfully exported by jit.trace * modify pretrained.py * support streaming decoding * fix model.py * Add cut_id to recognition results * Add cut_id to recognition results * do not pad in Conv subsampling module; add tail padding during decoding. * update RESULTS.md * minor fix * fix doc * update README.md * minor change, filter infinite loss * remove the condition of raise error * modify type hint for the return value in model.py * minor change * modify RESULTS.md Co-authored-by: pkufool <wkang.pku@gmail.com>
33 lines
3.0 KiB
Markdown
33 lines
3.0 KiB
Markdown
# Introduction
|
|
|
|
Please refer to <https://icefall.readthedocs.io/en/latest/recipes/librispeech/index.html> for how to run models in this recipe.
|
|
|
|
[./RESULTS.md](./RESULTS.md) contains the latest results.
|
|
|
|
# Transducers
|
|
|
|
There are various folders containing the name `transducer` in this folder.
|
|
The following table lists the differences among them.
|
|
|
|
| | Encoder | Decoder | Comment |
|
|
|---------------------------------------|---------------------|--------------------|---------------------------------------------------|
|
|
| `transducer` | Conformer | LSTM | |
|
|
| `transducer_stateless` | Conformer | Embedding + Conv1d | Using optimized_transducer from computing RNN-T loss |
|
|
| `transducer_stateless2` | Conformer | Embedding + Conv1d | Using torchaudio for computing RNN-T loss |
|
|
| `transducer_lstm` | LSTM | LSTM | |
|
|
| `transducer_stateless_multi_datasets` | Conformer | Embedding + Conv1d | Using data from GigaSpeech as extra training data |
|
|
| `pruned_transducer_stateless` | Conformer | Embedding + Conv1d | Using k2 pruned RNN-T loss |
|
|
| `pruned_transducer_stateless2` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss |
|
|
| `pruned_transducer_stateless3` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss + using GigaSpeech as extra training data |
|
|
| `pruned_transducer_stateless4` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless2 + save averaged models periodically during training |
|
|
| `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner|
|
|
| `pruned_transducer_stateless6` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + distillation with hubert|
|
|
| `pruned_stateless_emformer_rnnt2` | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR|
|
|
| `conv_emformer_transducer_stateless` | ConvEmformer | Embedding + Conv1d | Using ConvEmformer for streaming ASR + mechanisms in reworked model |
|
|
| `conv_emformer_transducer_stateless2` | ConvEmformer | Embedding + Conv1d | Using ConvEmformer with simplified memory for streaming ASR + mechanisms in reworked model |
|
|
| `lstm_transducer_stateless` | LSTM | Embedding + Conv1d | Using LSTM with mechanisms in reworked model |
|
|
|
|
The decoder in `transducer_stateless` is modified from the paper
|
|
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
|
|
We place an additional Conv1d layer right after the input embedding layer.
|