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c059ef3169
* keep model_avg on cpu * explicitly convert model_avg to cpu * minor fix * remove device convertion for model_avg * modify usage of the model device in train.py * change model.device to next(model.parameters()).device for decoding * assert params.start_epoch>0 * assert params.start_epoch>0, params.start_epoch
Introduction
Please refer to https://icefall.readthedocs.io/en/latest/recipes/librispeech/index.html for how to run models in this recipe.
./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 |
The decoder in transducer_stateless is modified from the paper
Rnn-Transducer with Stateless Prediction Network.
We place an additional Conv1d layer right after the input embedding layer.