refactor egs

examples/aishell/README.md

@@ -1,7 +1 @@
-# Aishell-1
+* s0 for deepspeech2
-
-## CTC
-| Model | Config | Test set |  CER |
-| --- | --- | --- | --- |
-| DeepSpeech2 | conf/deepspeech2.yaml | test | 0.078977 |
-| DeepSpeech2 | release 1.8.5 | test | 0.080447 |

examples/aishell/s0/README.md

@@ -0,0 +1,7 @@
+# Aishell-1
+
+## CTC
+| Model | Config | Test set |  CER |
+| --- | --- | --- | --- |
+| DeepSpeech2 | conf/deepspeech2.yaml | test | 0.078977 |
+| DeepSpeech2 | release 1.8.5 | test | 0.080447 |

examples/aishell/s0/path.sh

@@ -1,4 +1,4 @@
-export MAIN_ROOT=${PWD}/../../
+export MAIN_ROOT=${PWD}/../../../
 
 export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
 export LC_ALL=C

examples/librispeech/README.md

@@ -1,7 +1 @@
-# LibriSpeech
+* s0 for deepspeech2
-
-## CTC
-| Model | Config | Test set |  WER |
-| --- | --- | --- | --- |
-| DeepSpeech2 | conf/deepspeech2.yaml | test-clean | 0.073973 |
-| DeepSpeech2 | release 1.8.5 | test-clean | 0.074939 |

examples/librispeech/s0/README.md

@@ -0,0 +1,7 @@
+# LibriSpeech
+
+## CTC
+| Model | Config | Test set |  WER |
+| --- | --- | --- | --- |
+| DeepSpeech2 | conf/deepspeech2.yaml | test-clean | 0.073973 |
+| DeepSpeech2 | release 1.8.5 | test-clean | 0.074939 |

examples/librispeech/s0/path.sh

@@ -1,4 +1,4 @@
-export MAIN_ROOT=${PWD}/../../
+export MAIN_ROOT=${PWD}/../../../
 
 export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
 export LC_ALL=C

examples/tiny/README.md

@@ -1,51 +1 @@
-# Tiny Example
+* s0 for deepspeech2
-
-1. `source path.sh`
-3. set `CUDA_VISIBLE_DEVICES` as you need.
-2. demo scrpt is `bash run.sh`. You can run commond separately as needed.
-
-## Steps
-- Prepare the data
-
-    ```bash
-    bash local/data.sh
-    ```
-
-    `data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
-
-- Train your own ASR model
-
-    ```bash
-    bash local/train.sh
-    ```
-
-    `train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
-
-- Case inference with an existing model
-
-    ```bash
-    bash local/infer.sh
-    ```
-
-    `infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference.
-
-- Evaluate an existing model
-
-    ```bash
-    bash local/test.sh
-    ```
-
-    `test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
-
-
-- Export jit model
-
-    ```bash
-    bash local/export.sh ckpt_path saved_jit_model_path
-    ```
-
-- Tune hyper paerameter
-
-    ```bash
-    bash local/tune.sh
-    ```

examples/tiny/s0/README.md

@@ -0,0 +1,51 @@
+# Tiny Example
+
+1. `source path.sh`
+3. set `CUDA_VISIBLE_DEVICES` as you need.
+2. demo scrpt is `bash run.sh`. You can run commond separately as needed.
+
+## Steps
+- Prepare the data
+
+    ```bash
+    bash local/data.sh
+    ```
+
+    `data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
+
+- Train your own ASR model
+
+    ```bash
+    bash local/train.sh
+    ```
+
+    `train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
+
+- Case inference with an existing model
+
+    ```bash
+    bash local/infer.sh
+    ```
+
+    `infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference.
+
+- Evaluate an existing model
+
+    ```bash
+    bash local/test.sh
+    ```
+
+    `test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
+
+
+- Export jit model
+
+    ```bash
+    bash local/export.sh ckpt_path saved_jit_model_path
+    ```
+
+- Tune hyper paerameter
+
+    ```bash
+    bash local/tune.sh
+    ```

examples/tiny/s0/path.sh

@@ -1,4 +1,4 @@
-export MAIN_ROOT=${PWD}/../../
+export MAIN_ROOT=${PWD}/../../../
 
 export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
 export LC_ALL=C