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README.md

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+# Introduction
+
+This repo contains pre-trained model using
+<https://github.com/k2-fsa/icefall/pull/213>.
+
+It is trained on full LibriSpeech dataset.
+Also, it uses the `L` subset from [GigaSpeech](https://github.com/SpeechColab/GigaSpeech)
+as extra training data.
+
+## How to clone this repo
+```
+sudo apt-get install git-lfs
+git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01
+
+
+cd icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01
+git lfs pull
+```
+
+**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
+
+The model in this repo is trained using the commit `TODO`.
+
+You can use
+
+```
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+git checkout TODO
+```
+to download `icefall`.
+
+You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/TODO/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py#L198>.
+
+In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
+the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
+
+The decoder architecture is modified from
+[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
+A Conv1d layer is placed right after the input embedding layer.
+
+-----
+
+## Description
+
+This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
+using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
+and contains only an embedding layer and a Conv1d.
+
+The commands for training are:
+
+```
+cd egs/librispeech/ASR/
+./prepare.sh
+./prepare_giga_speech.sh
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./transducer_stateless_multi_datasets/train.py \
+  --world-size 4 \
+  --num-epochs 40 \
+  --start-epoch 0 \
+  --exp-dir transducer_stateless_multi_datasets/exp-full-2 \
+  --full-libri 1 \
+  --max-duration 300 \
+  --lr-factor 5 \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --modified-transducer-prob 0.25 \
+  --giga-prob 0.2
+```
+
+The tensorboard training log can be found at
+<https://tensorboard.dev/experiment/xmo5oCgrRVelH9dCeOkYBg/>
+
+The command for decoding is:
+
+```bash
+epoch=39
+avg=15
+sym=1
+
+# greedy search
+./transducer_stateless_multi_datasets/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir transducer_stateless_multi_datasets/exp-full-2 \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --max-duration 100 \
+  --context-size 2 \
+  --max-sym-per-frame $sym
+
+# modified beam search
+./transducer_stateless_multi_datasets/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir transducer_stateless_multi_datasets/exp-full-2 \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --max-duration 100 \
+  --context-size 2 \
+  --decoding-method modified_beam_search \
+  --beam-size 4
+```
+
+You can find the decoding log for the above command in this
+repo (in the folder `log`).
+
+The WERs for the test datasets are
+
+|                                     | test-clean | test-other | comment                                  |
+|-------------------------------------|------------|------------|------------------------------------------|
+| greedy search (max sym per frame 1) | 2.64       | 6.55       | --epoch 39, --avg 15, --max-duration 100 |
+| modified beam search (beam size 4)  | 2.61       | 6.46       | --epoch 39, --avg 15, --max-duration 100 |
+
+
+# File description
+
+- [log][log], this directory contains the decoding log and decoding results
+- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
+- [data][data], this directory contains files generated by [prepare.sh][prepare]
+- [exp][exp], this directory contains only one file: `preprained.pt`
+
+`exp/pretrained.pt` is generated by the following command:
+
+```bash
+./transducer_stateless_multi_datasets/export.py \
+  --epoch 39 \
+  --avg 15 \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --exp-dir transducer_stateless_multi_datasets/exp-full-2
+```
+
+**HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
+just do the following:
+```
+cp icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/exp/pretrained.pt \
+  /path/to/icefall/egs/librispeech/ASR/transducer_stateless_multi_datasets/exp/epoch-999.pt
+```
+and pass `--epoch 999 --avg 1` to `transducer_stateless_multi_datasets/decode.py`.
+
+
+[icefall]: https://github.com/k2-fsa/icefall
+[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
+[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/exp
+[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/data
+[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/test_wavs
+[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/log
+[icefall]: https://github.com/k2-fsa/icefall