mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
* init files * use average value as memory vector for each chunk * change tail padding length from right_context_length to chunk_length * correct the files, ln -> cp * fix bug in conv_emformer_transducer_stateless2/emformer.py * fix doc in conv_emformer_transducer_stateless/emformer.py * refactor init states for stream * modify .flake8 * fix bug about memory mask when memory_size==0 * add @torch.jit.export for init_states function * update RESULTS.md * minor change * update README.md * modify doc * replace torch.div() with << * fix bug, >> -> << * use i&i-1 to judge if it is a power of 2 * minor fix * fix error in RESULTS.md
32 lines
2.9 KiB
Markdown
32 lines
2.9 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 |
|
|
|
|
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.
|