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# Transformer/Conformer ASR with TALCS
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This example contains code used to train [u2](https://arxiv.org/pdf/2012.05481.pdf) model (Transformer or [Conformer](https://arxiv.org/pdf/2005.08100.pdf) model) with [TALCS dataset](https://ai.100tal.com/dataset)
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## Overview
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All the scripts you need are in `run.sh`. There are several stages in `run.sh`, and each stage has its function.
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| Stage | Function |
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|:---- |:----------------------------------------------------------- |
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| 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<br> (5) Get the sentencepiece model |
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| 1 | Train the model |
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| 2 | Get the final model by averaging the top-k models, set k = 1 means to choose the best model |
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| 3 | Test the final model performance |
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| 4 | Get ctc alignment of test data using the final model |
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| 5 | Infer the single audio file |
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You can choose to run a range of stages by setting `stage` and `stop_stage `.
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For example, if you want to execute the code in stage 2 and stage 3, you can run this script:
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```bash
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bash run.sh --stage 2 --stop_stage 3
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```
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Or you can set `stage` equal to `stop-stage` to only run one stage.
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For example, if you only want to run `stage 0`, you can use the script below:
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```bash
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bash run.sh --stage 0 --stop_stage 0
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```
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The document below will describe the scripts in `run.sh` in detail.
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## The Environment Variables
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The path.sh contains the environment variables.
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```bash
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. ./path.sh
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. ./cmd.sh
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```
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This script needs to be run first. And another script is also needed:
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```bash
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source ${MAIN_ROOT}/utils/parse_options.sh
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```
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It will support the way of using `--variable value` in the shell scripts.
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## The Local Variables
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Some local variables are set in `run.sh`.
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`gpus` denotes the GPU number you want to use. If you set `gpus=`, it means you only use CPU.
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`stage` denotes the number of stages you want to start from in the experiments.
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`stop stage` denotes the number of the stage you want to end at in the experiments.
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`conf_path` denotes the config path of the model.
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`avg_num` denotes the number K of top-K models you want to average to get the final model.
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`audio file` denotes the file path of the single file you want to infer in stage 5
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`ckpt` denotes the checkpoint prefix of the model, e.g. "conformer"
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You can set the local variables (except `ckpt`) when you use `run.sh`
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For example, you can set the `gpus` and `avg_num` when you use the command line:
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```bash
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bash run.sh --gpus 0,1 --avg_num 10
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```
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## Stage 0: Data Processing
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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:
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```bash
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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bash ./local/data.sh || exit -1
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fi
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```
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Stage 0 is for processing the data.
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If you only want to process the data. You can run
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```bash
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bash run.sh --stage 0 --stop_stage 0
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```
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You can also just run these scripts in your command line.
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```bash
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. ./path.sh
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. ./cmd.sh
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bash ./local/data.sh
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```
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After processing the data, the `data` directory will look like this:
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```bash
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data/
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|-- dev_set.meta
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|-- lang_char
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| `-- bpe_bpe_11297.model
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| `-- bpe_bpe_11297.vocab
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| `-- vocab.txt
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|-- manifest.dev
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|-- manifest.dev.raw
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|-- manifest.test
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|-- manifest.test.raw
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|-- manifest.train
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|-- manifest.train.raw
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|-- mean_std.json
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|-- test_set.meta
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`-- train_set.meta
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```
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## Stage 1: Model Training
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If you want to train the model. you can use stage 1 in `run.sh`. The code is shown below.
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```bash
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `exp` dir
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
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fi
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```
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If you want to train the model, you can use the script below to execute stage 0 and stage 1:
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```bash
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bash run.sh --stage 0 --stop_stage 1
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```
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or you can run these scripts in the command line (only use CPU).
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```bash
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. ./path.sh
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. ./cmd.sh
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bash ./local/data.sh
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CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
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```
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## Stage 2: Top-k Models Averaging
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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:
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```bash
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# avg n best model
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avg.sh best exp/${ckpt}/checkpoints ${avg_num}
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fi
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```
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The `avg.sh` is in the `../../../utils/` which is define in the `path.sh`.
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If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:
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```bash
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bash run.sh --stage 0 --stop_stage 2
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```
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or you can run these scripts in the command line (only use CPU).
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```bash
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. ./path.sh
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. ./cmd.sh
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bash ./local/data.sh
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CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
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avg.sh best exp/conformer/checkpoints 10
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```
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## Stage 3: Model Testing
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The test stage is to evaluate the model performance. The code of test stage is shown below:
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```bash
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# test ckpt avg_n
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CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
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fi
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```
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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 :
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```bash
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bash run.sh --stage 0 --stop_stage 3
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```
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or you can run these scripts in the command line (only use CPU).
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```bash
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. ./path.sh
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. ./cmd.sh
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bash ./local/data.sh
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CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
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avg.sh best exp/conformer/checkpoints 10
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CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_10
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```
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## Pretrained Model
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You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md).
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using the `tar` scripts to unpack the model and then you can use the script to test the model.
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For example:
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```bash
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wget https://paddlespeech.bj.bcebos.com/s2t/tal_cs/asr1/asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
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tar xzvf asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
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source path.sh
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# If you have process the data and get the manifest file， you can skip the following 2 steps
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bash local/data.sh --stage -1 --stop_stage -1
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bash local/data.sh --stage 2 --stop_stage 2
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CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_10
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```
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The performance of the released models are shown in [here](./RESULTS.md).
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## Stage 5: Single Audio File Inference
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In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
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```bash
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# test a single .wav file
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CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
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fi
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```
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you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below:
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```bash
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wget https://paddlespeech.bj.bcebos.com/s2t/tal_cs/asr1/asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
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tar xzvf asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
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```
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You can download the audio demo:
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```bash
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wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
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```
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You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result of the audio demo by running the script below.
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```bash
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CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/conformer.yaml exp/conformer/checkpoints/avg_10 data/demo_01_03.wav
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```
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zxcd
2 years ago