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+# Introduction
+
+
+
+**ReazonSpeech** is an open-source dataset that contains a diverse set of natural Japanese speech, collected from terrestrial television streams. It contains more than 35,000 hours of audio.
+
+
+
+The dataset is available on Hugging Face. For more details, please visit:
+
+- Dataset: https://huggingface.co/datasets/reazon-research/reazonspeech
+- Paper: https://research.reazon.jp/_static/reazonspeech_nlp2023.pdf
+
+
+
+[./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                                           |
+| ---------------------------------------- | -------------------- | ------------------ | ------------------------------------------------- |
+| `pruned_transducer_stateless2`           | Conformer (modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss                        |
+| `pruned_transducer_stateless7_streaming` | Streaming Zipformer  | Embedding + Conv1d | streaming version of pruned_transducer_stateless7 |
+| `zipformer`                              | Upgraded Zipformer   | Embedding + Conv1d | The latest recipe                                 |
+
+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.
+
diff --git a/egs/reazonspeech/ASR/RESULTS.md b/egs/reazonspeech/ASR/RESULTS.md
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+## Results
+
+### Zipformer
+
+#### Non-streaming
+
+##### large-scaled model, number of model parameters: 159337842, i.e., 159.34 M
+
+|   decoding method    | In-Distribution CER | JSUT | CommonVoice | TEDx  |      comment       |
+| :------------------: | :-----------------: | :--: | :---------: | :---: | :----------------: |
+|    greedy search     |         4.2         | 6.7  |    7.84     | 17.9  | --epoch 39 --avg 7 |
+| modified beam search |        4.13         | 6.77 |    7.69     | 17.82 | --epoch 39 --avg 7 |
+
+The training command is:
+
+```shell
+./zipformer/train.py \
+  --world-size 8 \
+  --num-epochs 40 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-large \
+  --causal 0 \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --lang data/lang_char \
+  --max-duration 1600 
+```
+
+The decoding command is:
+
+```shell
+./zipformer/decode.py \
+    --epoch 40 \
+    --avg 16 \
+    --exp-dir zipformer/exp-large \
+    --max-duration 600 \
+    --causal 0 \
+    --decoding-method greedy_search \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 512,768,1536,2048,1536,768 \
+    --encoder-dim 192,256,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --lang data/lang_char \
+    --blank-penalty 0
+```
+