[hackathon7] Add timit/asr1 readme.md (#3930)

* add timit md file

* fix

* fix

examples/timit/README.md

@@ -1,7 +1,3 @@
 # TIMIT
 
-asr model with phone unit
-
-* asr0 - deepspeech2 Streaming/Non-Streaming
-* asr1 - transformer/conformer Streaming/Non-Streaming
-* asr2 - transformer/conformer Streaming/Non-Streaming with Kaldi feature
+* asr1 - transformer Streaming/Non-Streaming

examples/timit/asr1/README.md

@@ -0,0 +1,195 @@
+# Transformer ASR with Timit
+The phoneme-based continuous speech corpus is a collaboration between Texas Instruments, MIT, and SRI International. The [Timit](https://catalog.ldc.upenn.edu/docs/LDC93S1/) dataset has a voice sampling frequency of 16 khz and contains a total of 6,300 sentences, with 630 people from 8 major U.S. dialects speaking a given 10 sentences each, all sentences are manually segmented and marked at the phone level. Seventy percent of the speakers are male; most of the speakers are white adults.
+
+## Dataset
+### Download and Extract
+Download TIMIT from it's [official website](https://catalog.ldc.upenn.edu/LDC93S1) and extract it to `~/datasets`. Assume unzip the dataset in the directory `~/datasets/timit`.
+
+## Overview
+All the scripts you need are in `run.sh`. There are several stages in `run.sh`, and each stage has its function.
+| Stage | Function                                                     |
+|:---- |:----------------------------------------------------------- |
+| 0     | Process data. It includes: <br>       (1) Download the dataset <br>       (2) Calculate the CMVN of the train dataset <br>       (3) Get the vocabulary file <br>       (4) Get the manifest files of the train, development and test dataset |
+| 1     | Train the model                                              |
+| 2     | Get the final model by averaging the top-k models, set k = 1 means to choose the best model |
+| 3     | Test the final model performance                             |
+| 4     | Get ctc alignment of test data using the final model         |
+
+You can choose to run a range of stages by setting `stage` and `stop_stage `.
+
+For example, if you want to execute the code in stage 2 and stage 3, you can run this script:
+```bash
+bash run.sh --stage 2 --stop_stage 3
+```
+Or you can set `stage` equal to `stop-stage` to only run one stage.
+For example, if you only want to run `stage 0`, you can use the script below:
+```bash
+bash run.sh --stage 0 --stop_stage 0
+```
+The document below will describe the scripts in `run.sh` in detail.
+## The Environment Variables
+The path.sh contains the environment variables.
+```bash
+source path.sh
+```
+This script needs to be run first. And another script is also needed:
+```bash
+source ${MAIN_ROOT}/utils/parse_options.sh
+```
+It will support the way of using `--variable value` in the shell scripts.
+## The Local Variables
+Some local variables are set in `run.sh`. 
+`gpus` denotes the GPU number you want to use. If you set `gpus=`, it means you only use CPU. 
+`stage` denotes the number of the stage you want to start from in the experiments.
+`stop stage` denotes the number of the stage you want to end at in the experiments. 
+`conf_path` denotes the config path of the model.
+`avg_num` denotes the number K of top-K models you want to average to get the final model.
+`audio_file` denotes the file path of the single file you want to infer in stage 5
+`ckpt` denotes the checkpoint prefix of the model, e.g. "conformer"
+You can set the local variables (except `ckpt`) when you use `run.sh`
+
+For example, you can set the `gpus` and `avg_num` when you use the command line.:
+```bash
+bash run.sh --gpus 0,1,2,3 --avg_num 10
+```
+## Stage 0: Data Processing
+To use this example, you need to process data firstly and you can use stage 0 in `run.sh` to do this. The code is shown below:
+```bash
+ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+     # prepare data
+     bash ./local/timit_data_prep.sh ${TIMIT_path}
+     bash ./local/data.sh || exit -1
+ fi
+```
+
+Stage 0 is for processing the data.
+
+If you only want to process the data. You can run
+```bash
+bash run.sh --stage 0 --stop_stage 0
+```
+You can also just run these scripts in your command line.
+```bash
+source path.sh
+bash ./local/timit_data_prep.sh ${TIMIT_path}
+bash ./local/data.sh
+```
+After processing the data, the ``data`` directory will look like this:
+```bash
+data/
+|-- lang_char
+|   `-- vocab.txt
+|-- local
+|   `-- dev_sph.flist
+|   `-- dev_sph.scp
+|   `-- dev.text
+|   `-- dev.trans
+|   `-- dev.uttids
+|   `-- test_sph.flist
+|   `-- test_sph.scp
+|   `-- test.text
+|   `-- test.trans
+|   `-- test.uttids
+|   `-- train_sph.flist
+|   `-- train_sph.scp
+|   `-- train.text
+|   `-- train.trans
+|   `-- train.uttids
+|-- manifest.dev
+|-- manifest.dev.raw
+|-- manifest.test
+|-- manifest.test.raw
+|-- manifest.train
+|-- manifest.train.raw
+|-- mean_std.json
+|-- test.meta
+```
+## Stage 1: Model Training
+If you want to train the model. you can use stage 1 in `run.sh`. The code is shown below. 
+```bash
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+     # train model, all `ckpt` under `exp` dir
+     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
+ fi
+```
+If you want to train the model, you can use the script below to execute stage 0 and stage 1:
+```bash
+bash run.sh --stage 0 --stop_stage 1
+```
+or you can run these scripts in the command line.
+```bash
+source path.sh
+bash ./local/timit_data_prep.sh ${TIMIT_path}
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
+```
+## Stage 2: Top-k Models Averaging
+After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below:
+```bash
+ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+     # avg n best model
+     avg.sh best exp/${ckpt}/checkpoints ${avg_num}
+ fi
+```
+The `avg.sh`is in the `../../../utils/` which is define in the `path.sh`.
+If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:
+```bash
+bash run.sh --stage 0 --stop_stage 2
+```
+or you can run these scripts in the command line.
+```bash
+bash ./local/timit_data_prep.sh ${TIMIT_path}
+source path.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
+avg.sh best exp/conformer/checkpoints 10
+```
+## Stage 3: Model Testing
+The test stage is to evaluate the model performance. The code of the test stage is shown below:
+```bash
+ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+     # test ckpt avg_n
+     CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+ fi
+```
+If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
+```bash
+bash run.sh --stage 0 --stop_stage 3
+```
+or you can run these scripts in the command line.
+```bash
+source path.sh
+bash ./local/timit_data_prep.sh ${TIMIT_path}
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
+avg.sh best exp/transformer/checkpoints 10
+CUDA_VISIBLE_DEVICES=0 ./local/test.sh conf/transformer.yaml exp/transformer/checkpoints/avg_10
+```
+## Stage 4: CTC Alignment 
+If you want to get the alignment between the audio and the text, you can use the ctc alignment. The code of this stage is shown below:
+```bash
+ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+     # ctc alignment of test data
+     CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+ fi
+```
+If you want to train the model, test it and do the alignment, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
+```bash
+bash run.sh --stage 0 --stop_stage 4
+```
+or if you only need to train a model and do the alignment, you can use these scripts to escape stage 3(test stage):
+```bash
+bash run.sh --stage 0 --stop_stage 2
+bash run.sh --stage 4 --stop_stage 4
+```
+or you can also use these scripts in the command line.
+```bash
+source path.sh
+bash ./local/timit_data_prep.sh ${TIMIT_path}
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh conf/transformer.yaml transformer
+avg.sh best exp/transformer/checkpoints 10
+# test stage is optional
+CUDA_VISIBLE_DEVICES=0 ./local/test.sh conf/transformer.yaml exp/transformer/checkpoints/avg_10
+CUDA_VISIBLE_DEVICES=0 ./local/align.sh conf/transformer.yaml exp/transformer/checkpoints/avg_10
+```

examples/timit/asr1/run.sh

@@ -9,7 +9,7 @@ stop_stage=50
 conf_path=conf/transformer.yaml
 decode_conf_path=conf/tuning/decode.yaml
 avg_num=10
-TIMIT_path=/path/to/TIMIT
+TIMIT_path=~/datasets/timit/data/lisa/data/timit/raw/TIMIT
 
 . ${MAIN_ROOT}/utils/parse_options.sh || exit 1;