[ASR] add code-switch asr tal_cs recipe (#2796)

* add tal_cs asr recipe.

* add readme and result, and fix some bug.

* add commit id and date.

dataset/tal_cs/README.md

+# [TAL_CSASR](https://ai.100tal.com/dataset/)
+
+This data set is TAL English class audio, including mixed Chinese and English speech. Each audio has only one speaker, and this data set has more than 100 speakers. (File 63.36G) This data contains the sample of intra sentence and inter sentence mixing. The ratio between Chinese characters and English words in the data is 13:1. 
+
+- Total data: 587H (train_set: 555.9H, dev_set: 8H, test_set: 23.6H)
+- Sample rate: 16000
+- Sample bit: 16
+- Recording device: microphone
+- Speaker number: 200+
+- Recording time: 2019
+- Data format: audio: .wav; test: .txt
+- Audio duration: 1-60s
+- Data type: audio of English teachers' teaching

dataset/tal_cs/tal_cs.py

+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Prepare TALCS ASR datasets.
+
+create manifest files.
+Manifest file is a json-format file with each line containing the
+meta data (i.e. audio filepath, transcript and audio duration)
+of each audio file in the data set.
+"""
+import argparse
+import codecs
+import io
+import json
+import os
+
+import soundfile
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    "--target_dir",
+    type=str,
+    help="Directory to save the dataset. (default: %(default)s)")
+parser.add_argument(
+    "--manifest_prefix",
+    type=str,
+    help="Filepath prefix for output manifests. (default: %(default)s)")
+args = parser.parse_args()
+
+TRAIN_SET = os.path.join(args.target_dir, "train_set")
+DEV_SET = os.path.join(args.target_dir, "dev_set")
+TEST_SET = os.path.join(args.target_dir, "test_set")
+
+manifest_train_path = os.path.join(args.manifest_prefix, "manifest.train.raw")
+manifest_dev_path = os.path.join(args.manifest_prefix, "manifest.dev.raw")
+manifest_test_path = os.path.join(args.manifest_prefix, "manifest.test.raw")
+
+
+def create_manifest(data_dir, manifest_path):
+    """Create a manifest json file summarizing the data set, with each line
+    containing the meta data (i.e. audio filepath, transcription text, audio
+    duration) of each audio file within the data set.
+    """
+    print("Creating manifest %s ..." % manifest_path)
+    json_lines = []
+    total_sec = 0.0
+    total_char = 0.0
+    total_num = 0
+    wav_dir = os.path.join(data_dir, 'wav')
+    text_filepath = os.path.join(data_dir, 'label.txt')
+    for subfolder, _, filelist in sorted(os.walk(wav_dir)):
+        for line in io.open(text_filepath, encoding="utf8"):
+            segments = line.strip().split()
+            nchars = len(segments[1:])
+            text = ' '.join(segments[1:]).lower()
+
+            audio_filepath = os.path.abspath(
+                os.path.join(subfolder, segments[0] + '.wav'))
+            audio_data, samplerate = soundfile.read(audio_filepath)
+            duration = float(len(audio_data)) / samplerate
+
+            utt = os.path.splitext(os.path.basename(audio_filepath))[0]
+            utt2spk = '-'.join(utt.split('-')[:2])
+
+            json_lines.append(
+                json.dumps({
+                    'utt': utt,
+                    'utt2spk': utt2spk,
+                    'feat': audio_filepath,
+                    'feat_shape': (duration, ),  # second
+                    'text': text,
+                }))
+
+            total_sec += duration
+            total_char += nchars
+            total_num += 1
+
+    with codecs.open(manifest_path, 'w', 'utf-8') as out_file:
+        for line in json_lines:
+            out_file.write(line + '\n')
+
+    subset = os.path.splitext(manifest_path)[1][1:]
+    manifest_dir = os.path.dirname(manifest_path)
+    data_dir_name = os.path.split(data_dir)[-1]
+    meta_path = os.path.join(manifest_dir, data_dir_name) + '.meta'
+    with open(meta_path, 'w') as f:
+        print(f"{subset}:", file=f)
+        print(f"{total_num} utts", file=f)
+        print(f"{total_sec / (60*60)} h", file=f)
+        print(f"{total_char} char", file=f)
+        print(f"{total_char / total_sec} char/sec", file=f)
+        print(f"{total_sec / total_num} sec/utt", file=f)
+
+
+def main():
+    if args.target_dir.startswith('~'):
+        args.target_dir = os.path.expanduser(args.target_dir)
+
+    create_manifest(TRAIN_SET, manifest_train_path)
+    create_manifest(DEV_SET, manifest_dev_path)
+    create_manifest(TEST_SET, manifest_test_path)
+    print("Data download and manifest prepare done!")
+
+
+if __name__ == '__main__':
+    main()

docs/source/released_model.md

@@ -17,6 +17,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER |
 [Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0338 | 960 h | [Conformer Librispeech ASR1](../../examples/librispeech/asr1) | python |
 [Transformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB  | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h | [Transformer Librispeech ASR1](../../examples/librispeech/asr1) | python |
 [Transformer Librispeech ASR2 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB  | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.0240 | 960 h | [Transformer Librispeech ASR2](../../examples/librispeech/asr2) | python |
+[Conformer TALCS ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/tal_cs/asr1/asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz) | TALCS Dataset | subword-based | 470 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0844 | 587 h | [Conformer TALCS ASR1](../../examples/tal_cs/asr1) | python |
 
 ### Self-Supervised Pre-trained Model
 Model | Pre-Train Method | Pre-Train Data | Finetune Data | Size | Descriptions | CER | WER |  Example Link |
@@ -29,7 +30,7 @@ Model | Pre-Train Method | Pre-Train Data | Finetune Data | Size | Descriptions
 ### Whisper Model
 Demo Link | Training Data | Size | Descriptions | CER | Model 
 :-----------: | :-----:| :-------: | :-----: | :-----: |:---------:|
-[Whisper](../../demos/whisper) | 680kh from internet | large: 5.8G,</br>medium: 2.9G,</br>small: 923M,</br>base: 277M,</br>tiny: 145M | Encoder:Transformer,</br> Decoder:Transformer, </br>Decoding method: </br>Greedy search | 2.7 </br>(large, Librispeech) | [whisper-large](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-large-model.tar.gz) </br>[whisper-medium](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-medium-model.tar.gz) </br>[whisper-medium-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-medium-en-model.tar.gz) </br>[whisper-small](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-small-model.tar.gz) </br>[whisper-small-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-small-en-model.tar.gz) </br>[whisper-base](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-base-model.tar.gz) </br>[whisper-base-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-base-en-model.tar.gz) </br>[whisper-tiny](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-tiny-model.tar.gz) </br>[whisper-tiny-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-tiny-en-model.tar.gz)
+[Whisper](../../demos/whisper) | 680kh from internet | large: 5.8G,</br>medium: 2.9G,</br>small: 923M,</br>base: 277M,</br>tiny: 145M | Encoder:Transformer,</br> Decoder:Transformer, </br>Decoding method: </br>Greedy search | 0.027 </br>(large, Librispeech) | [whisper-large](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-large-model.tar.gz) </br>[whisper-medium](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-medium-model.tar.gz) </br>[whisper-medium-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-medium-en-model.tar.gz) </br>[whisper-small](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-small-model.tar.gz) </br>[whisper-small-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-small-en-model.tar.gz) </br>[whisper-base](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-base-model.tar.gz) </br>[whisper-base-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-base-en-model.tar.gz) </br>[whisper-tiny](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-tiny-model.tar.gz) </br>[whisper-tiny-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-tiny-en-model.tar.gz)
 
 ### Language Model based on NGram
 |Language Model | Training Data | Token-based | Size | Descriptions|

examples/tal_cs/asr1/README.md

+# Transformer/Conformer ASR with TALCS
+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)
+## 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<br>       (5) Get the sentencepiece model |
+| 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         |
+| 5     | Infer the single audio file                                  |
+
+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
+. ./path.sh
+. ./cmd.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 stages 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 --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/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
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+```
+After processing the data, the `data` directory will look like this:
+```bash
+data/
+|-- dev_set.meta
+|-- lang_char
+|   `-- bpe_bpe_11297.model
+|   `-- bpe_bpe_11297.vocab
+|   `-- vocab.txt
+|-- manifest.dev
+|-- manifest.dev.raw
+|-- manifest.test
+|-- manifest.test.raw
+|-- manifest.train
+|-- manifest.train.raw
+|-- mean_std.json
+|-- test_set.meta
+`-- train_set.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 (only use CPU).
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
+```
+## 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 (only use CPU).
+
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
+avg.sh best exp/conformer/checkpoints 10
+```
+## Stage 3: Model Testing
+The test stage is to evaluate the model performance. The code of 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 (only use CPU).
+```bash
+. ./path.sh
+. ./cmd.sh
+bash ./local/data.sh
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
+avg.sh best exp/conformer/checkpoints 10
+CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_10
+```
+## Pretrained Model
+You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md).
+
+using the `tar` scripts to unpack the model and then you can use the script to test the model.
+
+For example:
+```bash
+wget https://paddlespeech.bj.bcebos.com/s2t/tal_cs/asr1/asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
+tar xzvf asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
+source path.sh
+# If you have process the data and get the manifest file， you can skip the following 2 steps
+bash local/data.sh --stage -1 --stop_stage -1
+bash local/data.sh --stage 2 --stop_stage 2
+CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_10
+```
+The performance of the released models are shown in [here](./RESULTS.md).
+
+## Stage 5: Single Audio File Inference
+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
+```bash
+ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+     # test a single .wav file
+     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+ fi
+```
+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:
+```bash
+wget https://paddlespeech.bj.bcebos.com/s2t/tal_cs/asr1/asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
+tar xzvf asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz
+```
+You can download the audio demo:
+```bash
+wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
+```
+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.
+```bash
+CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/conformer.yaml exp/conformer/checkpoints/avg_10 data/demo_01_03.wav
+```

examples/tal_cs/asr1/RESULTS.md

+# TALCS
+2023.1.6, commit id: fa724285f3b799b97b4348ad3b1084afc0764f9b
+
+## Conformer
+train: Epoch 100, 3 V100-32G, best avg: 10
+
+| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | attention | 9.85091028213501 | 0.102786 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | ctc_greedy_search | 9.85091028213501 | 0.103538 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | ctc_prefix_beam_search | 9.85091028213501 | 0.103317 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | attention_rescoring | 9.85091028213501 | 0.084374 |  

examples/tal_cs/asr1/conf/conformer.yaml

+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: 
+cmvn_file_type: "json"
+# encoder related
+encoder: conformer
+encoder_conf:
+    output_size: 512    # dimension of attention
+    attention_heads: 8
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: True
+    cnn_module_kernel: 15
+    use_cnn_module: True
+    activation_type: 'swish'
+    pos_enc_layer_type: 'rel_pos'
+    selfattention_layer_type: 'rel_selfattn'
+
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 8
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
+
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+    init_type: 'kaiming_uniform' # !Warning: need to convergence
+
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt 
+spm_model_prefix: 'data/lang_char/bpe_bpe_11297'
+unit_type: 'spm'
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+stride_ms: 20.0
+window_ms: 30.0
+sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs 
+batch_size: 5
+maxlen_in: 512  # if input length  > maxlen-in, batchsize is automatically reduced
+maxlen_out: 150  # if output length > maxlen-out, batchsize is automatically reduced
+minibatches: 0 # for debug
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 2
+subsampling_factor: 1
+num_encs: 1
+
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 100 
+accum_grad: 4
+global_grad_clip: 5.0
+dist_sampler: False
+optim: adam
+optim_conf:
+  lr: 0.002
+  weight_decay: 1.0e-6
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/tal_cs/asr1/conf/preprocess.yaml

+process:
+  # extract kaldi fbank from PCM
+  - type: fbank_kaldi
+    fs: 16000
+    n_mels: 80
+    n_shift: 160
+    win_length: 400
+    dither: 1.0
+  - type: cmvn_json
+    cmvn_path: data/mean_std.json
+  # these three processes are a.k.a. SpecAugument
+  - type: time_warp
+    max_time_warp: 5
+    inplace: true
+    mode: PIL
+  - type: freq_mask
+    F: 30
+    n_mask: 2
+    inplace: true
+    replace_with_zero: false
+  - type: time_mask
+    T: 40
+    n_mask: 2
+    inplace: true
+    replace_with_zero: false
+
+
+
+

examples/tal_cs/asr1/conf/tuning/chunk_decode.yaml

+beam_size: 10
+decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+reverse_weight: 0.3 # reverse weight for attention rescoring decode mode.
+decoding_chunk_size: 16 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: True  # simulate streaming inference. Defaults to False.
+decode_batch_size: 128
+error_rate_type: cer 

examples/tal_cs/asr1/conf/tuning/decode.yaml

+beam_size: 10
+decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+#reverse_weight: 0.3 # reverse weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.
+decode_batch_size: 1
+error_rate_type: cer 

examples/tal_cs/asr1/local/data.sh

+#!/bin/bash
+stage=-1
+stop_stage=100
+dict_dir=data/lang_char
+
+# bpemode (unigram or bpe)
+nbpe=11297
+bpemode=bpe
+bpeprefix="${dict_dir}/bpe_${bpemode}_${nbpe}"
+
+stride_ms=20
+window_ms=30
+sample_rate=16000
+feat_dim=80
+
+source ${MAIN_ROOT}/utils/parse_options.sh
+
+
+mkdir -p data
+mkdir -p ${dict_dir}
+TARGET_DIR=${MAIN_ROOT}/dataset
+mkdir -p ${TARGET_DIR}
+
+#prepare data
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    if [ ! -d "${MAIN_ROOT}/dataset/tal_cs/TALCS_corpus" ]; then
+        echo "${MAIN_ROOT}/dataset/tal_cs/TALCS_corpus does not exist. Please donwload tal_cs data and unpack it from https://ai.100tal.com/dataset first."
+        echo "data md5 reference: 4c879b3c9c05365fc9dee1fc68713afe"
+        exit
+    fi
+    # create manifest json file from TALCS_corpus
+    python ${MAIN_ROOT}/dataset/tal_cs/tal_cs.py --target_dir ${MAIN_ROOT}/dataset/tal_cs/TALCS_corpus/ --manifest_prefix data/
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # compute mean and stddev for normalizer
+    num_workers=$(nproc)
+    python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
+    --manifest_path="data/manifest.train.raw" \
+    --num_samples=-1 \
+    --spectrum_type="fbank" \
+    --feat_dim=${feat_dim}  \
+    --delta_delta=false \
+    --sample_rate=${sample_rate} \
+    --stride_ms=${stride_ms} \
+    --window_ms=${window_ms} \
+    --use_dB_normalization=False \
+    --num_workers=${num_workers} \
+    --output_path="data/mean_std.json"
+    echo "compute mean and stddev done."
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    #use train_set build dict
+    python3 ${MAIN_ROOT}/utils/build_vocab.py \
+    --unit_type 'spm' \
+    --count_threshold=0 \
+    --vocab_path="${dict_dir}/vocab.txt"  \
+    --manifest_paths="data/manifest.train.raw"  \
+    --spm_mode=${bpemode} \
+    --spm_vocab_size=${nbpe}  \
+    --spm_model_prefix=${bpeprefix} \
+    --spm_character_coverage=1 
+    echo "build dict done."
+fi
+
+#use new dict format data
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # format manifest with tokenids, vocab size
+    for sub in train dev test ; do
+    {
+        python3 ${MAIN_ROOT}/utils/format_data.py \
+        --cmvn_path "data/mean_std.json" \
+        --unit_type "spm" \
+        --spm_model_prefix ${bpeprefix} \
+        --vocab_path="${dict_dir}/vocab.txt" \
+        --manifest_path="data/manifest.${sub}.raw" \
+        --output_path="data/manifest.${sub}"
+
+        if [ $? -ne 0 ]; then
+            echo "Formt mnaifest failed. Terminated."
+            exit 1
+        fi
+    }&
+    done
+    wait
+    echo "format data done."
+fi

examples/tal_cs/asr1/local/test.sh

+#!/bin/bash
+
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+decode_config_path=$2
+ckpt_prefix=$3
+
+chunk_mode=false
+if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
+    chunk_mode=true
+fi
+
+# download language model
+#bash local/download_lm_ch.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+
+for type in attention ctc_greedy_search; do
+    echo "decoding ${type}"
+    if [ ${chunk_mode} == true ];then
+        # stream decoding only support batchsize=1
+        batch_size=1
+    else
+        batch_size=64
+    fi
+    output_dir=${ckpt_prefix}
+    mkdir -p ${output_dir}
+    python3 -u ${BIN_DIR}/test.py \
+    --ngpu ${ngpu} \
+    --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
+    --result_file ${output_dir}/${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+
+for type in ctc_prefix_beam_search attention_rescoring; do
+    echo "decoding ${type}"
+    batch_size=1
+    output_dir=${ckpt_prefix}
+    mkdir -p ${output_dir}
+    python3 -u ${BIN_DIR}/test.py \
+    --ngpu ${ngpu} \
+    --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
+    --result_file ${output_dir}/${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+
+exit 0

examples/tal_cs/asr1/local/test_wav.sh

+#!/bin/bash
+
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+decode_config_path=$2
+ckpt_prefix=$3
+audio_file=$4
+
+mkdir -p data
+wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
+if [ $? -ne 0 ]; then
+   exit 1
+fi
+
+if [ ! -f ${audio_file} ]; then
+    echo "Plase input the right audio_file path"
+    exit 1
+fi
+
+chunk_mode=false
+if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
+    chunk_mode=true
+fi
+
+# download language model
+#bash local/download_lm_ch.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+for type in  attention_rescoring; do
+    echo "decoding ${type}"
+    batch_size=1
+    output_dir=${ckpt_prefix}
+    mkdir -p ${output_dir}
+    python3 -u ${BIN_DIR}/test_wav.py \
+    --ngpu ${ngpu} \
+    --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
+    --result_file ${output_dir}/${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size} \
+    --audio_file ${audio_file}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+exit 0

examples/tal_cs/asr1/local/train.sh

+#!/bin/bash
+
+profiler_options=
+benchmark_batch_size=0
+benchmark_max_step=0
+
+# seed may break model convergence
+seed=0
+
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+if [ ${seed} != 0  ]; then
+    export FLAGS_cudnn_deterministic=True
+    echo "using seed $seed & FLAGS_cudnn_deterministic=True ..."
+fi
+
+if [ $# -lt 2 ] && [ $# -gt 3 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)"
+    exit -1
+fi
+
+config_path=$1
+ckpt_name=$2
+ips=$3
+
+if [ ! $ips ];then
+  ips_config=
+else
+  ips_config="--ips="${ips}
+fi
+echo ${ips_config}
+
+mkdir -p exp
+
+# default memeory allocator strategy may case gpu training hang
+# for no OOM raised when memory exhaused
+export FLAGS_allocator_strategy=naive_best_fit
+
+if [ ${ngpu} == 0 ]; then
+python3 -u ${BIN_DIR}/train.py \
+--ngpu ${ngpu} \
+--seed ${seed} \
+--config ${config_path} \
+--output exp/${ckpt_name} \
+--profiler-options "${profiler_options}" \
+--benchmark-batch-size ${benchmark_batch_size} \
+--benchmark-max-step ${benchmark_max_step}
+else
+python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${ips_config} ${BIN_DIR}/train.py \
+--ngpu ${ngpu} \
+--seed ${seed} \
+--config ${config_path} \
+--output exp/${ckpt_name} \
+--profiler-options "${profiler_options}" \
+--benchmark-batch-size ${benchmark_batch_size} \
+--benchmark-max-step ${benchmark_max_step}
+fi
+
+
+if [ ${seed} != 0  ]; then
+    unset FLAGS_cudnn_deterministic
+fi
+
+if [ $? -ne 0 ]; then
+    echo "Failed in training!"
+    exit 1
+fi
+
+exit 0

examples/tal_cs/asr1/path.sh

+export MAIN_ROOT=`realpath ${PWD}/../../../`
+
+export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
+export LC_ALL=C
+
+export PYTHONDONTWRITEBYTECODE=1
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
+
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
+
+# model exp
+MODEL=u2
+export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/tal_cs/asr1/run.sh

+#!/bin/bash
+source path.sh || exit 1;
+set -e
+
+gpus=0,1,2,3
+stage=0
+stop_stage=50
+conf_path=conf/conformer.yaml
+ips=  #xxx.xxx.xxx.xxx,xxx.xxx.xxx.xxx
+decode_conf_path=conf/tuning/decode.yaml
+average_checkpoint=true
+avg_num=10
+
+. ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+avg_ckpt=avg_${avg_num}
+ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
+echo "checkpoint name ${ckpt}"
+
+audio_file="data/demo_01_03.wav"
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    bash ./local/data.sh || exit -1
+fi
+
+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} ${ips}
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # avg n best model
+    avg.sh best exp/${ckpt}/checkpoints ${avg_num}
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # test ckpt avg_n
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # test a single .wav file
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+fi
+
+# Not supported at now!!!
+if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
+     # export ckpt avg_n
+     CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+fi
\ No newline at end of file

examples/tal_cs/asr1/utils

+../../../utils
\ No newline at end of file