Hui Zhang
3 years ago
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# [WenetSpeech](https://github.com/wenet-e2e/WenetSpeech)
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A 10000+ Hours Multi-domain Chinese Corpus for Speech Recognition
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## Description
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### Creation
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All the data are collected from YouTube and Podcast. Optical character recognition (OCR) and automatic speech recognition (ASR) techniques are adopted to label each YouTube and Podcast recording, respectively. To improve the quality of the corpus, we use a novel end-to-end label error detection method to further validate and filter the data.
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### Categories
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In summary, WenetSpeech groups all data into 3 categories, as the following table shows:
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| Set | Hours | Confidence | Usage |
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|------------|-------|-------------|---------------------------------------|
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| High Label | 10005 | >=0.95 | Supervised Training |
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| Weak Label | 2478 | [0.6, 0.95] | Semi-supervised or noise training |
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| Unlabel | 9952 | / | Unsupervised training or Pre-training |
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| In Total | 22435 | / | All above |
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### High Label Data
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We classify the high label into 10 groups according to its domain, speaking style, and scenarios.
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| Domain | Youtube | Podcast | Total |
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|-------------|---------|---------|--------|
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| audiobook | 0 | 250.9 | 250.9 |
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| commentary | 112.6 | 135.7 | 248.3 |
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| documentary | 386.7 | 90.5 | 477.2 |
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| drama | 4338.2 | 0 | 4338.2 |
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| interview | 324.2 | 614 | 938.2 |
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| news | 0 | 868 | 868 |
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| reading | 0 | 1110.2 | 1110.2 |
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| talk | 204 | 90.7 | 294.7 |
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| variety | 603.3 | 224.5 | 827.8 |
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| others | 144 | 507.5 | 651.5 |
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| Total | 6113 | 3892 | 10005 |
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As shown in the following table, we provide 3 training subsets, namely `S`, `M` and `L` for building ASR systems on different data scales.
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| Training Subsets | Confidence | Hours |
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|------------------|-------------|-------|
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| L | [0.95, 1.0] | 10005 |
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| M | 1.0 | 1000 |
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| S | 1.0 | 100 |
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### Evaluation Sets
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| Evaluation Sets | Hours | Source | Description |
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|-----------------|-------|--------------|-----------------------------------------------------------------------------------------|
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| DEV | 20 | Internet | Specially designed for some speech tools which require cross-validation set in training |
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| TEST\_NET | 23 | Internet | Match test |
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| TEST\_MEETING | 15 | Real meeting | Mismatch test which is a far-field, conversational, spontaneous, and meeting dataset |
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@ -0,0 +1,3 @@
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data
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exp
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*.profile
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@ -0,0 +1,24 @@
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# WenetSpeech
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## Conformer
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | dev | attention | | |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | test net | ctc_greedy_search | | |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | test meeting | ctc_prefix_beam_search | | |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | test net | attention_rescoring | | |
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## Conformer Pretrain Model
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Pretrain model from http://mobvoi-speech-public.ufile.ucloud.cn/public/wenet/wenetspeech/20211025_conformer_exp.tar.gz
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| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | aishell1 | attention | - | 0.048456 |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | aishell1 | ctc_greedy_search | - | 0.052534 |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | aishell1 | ctc_prefix_beam_search | - | 0.052915 |
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| conformer | 32.52 M | conf/conformer.yaml | spec_aug | aishell1 | attention_rescoring | - | 0.047904 |
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@ -1 +1,69 @@
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decode_modes="attention_rescoring ctc_greedy_search"
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#!/bin/bash
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if [ $# != 2 ];then
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echo "usage: ${0} config_path ckpt_path_prefix"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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config_path=$1
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ckpt_prefix=$2
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chunk_mode=false
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if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
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chunk_mode=true
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fi
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# download language model
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#bash local/download_lm_ch.sh
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#if [ $? -ne 0 ]; then
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# exit 1
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#fi
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for type in attention ctc_greedy_search; do
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echo "decoding ${type}"
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if [ ${chunk_mode} == true ];then
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# stream decoding only support batchsize=1
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batch_size=1
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else
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batch_size=64
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fi
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output_dir=${ckpt_prefix}
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mkdir -p ${output_dir}
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python3 -u ${BIN_DIR}/test.py \
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--nproc ${ngpu} \
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--config ${config_path} \
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--result_file ${output_dir}/${type}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.decoding_method ${type} \
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--opts decoding.batch_size ${batch_size}
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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done
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for type in ctc_prefix_beam_search attention_rescoring; do
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echo "decoding ${type}"
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batch_size=1
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output_dir=${ckpt_prefix}
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mkdir -p ${output_dir}
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python3 -u ${BIN_DIR}/test.py \
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--nproc ${ngpu} \
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--config ${config_path} \
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--result_file ${output_dir}/${type}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.decoding_method ${type} \
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--opts decoding.batch_size ${batch_size}
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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done
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exit 0
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