mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
aishell example
This commit is contained in:
游雁
parent
d92cd5ae03
commit
1448e021ac
2
data/list/text.txt
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2
data/list/text.txt
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@ -0,0 +1,2 @@
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ID0012W0013 当客户风险承受能力评估依据发生变化时
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ID0012W0014 杨涛不得不将工厂关掉
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2
data/list/wav.scp
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2
data/list/wav.scp
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ID0012W0013 /Users/zhifu/funasr_github/test_local/aishell2_dev_ios/wav/D0012/ID0012W0013.wav
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ID0012W0014 /Users/zhifu/funasr_github/test_local/aishell2_dev_ios/wav/D0012/ID0012W0014.wav
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@ -1,6 +1,6 @@
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# network architecture
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model: funasr.cli.models.paraformer:Paraformer
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model: Paraformer
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1
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@ -9,9 +9,8 @@ model_conf:
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sampling_ratio: 0.4
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use_1st_decoder_loss: true
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# encoder related
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encoder: conformer
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# encoder
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encoder: ConformerEncoder
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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@ -29,8 +28,8 @@ encoder_conf:
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use_cnn_module: true
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cnn_module_kernel: 15
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# decoder related
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decoder: paraformer_decoder_san
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# decoder
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decoder: ParaformerSANDecoder
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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@ -40,8 +39,17 @@ decoder_conf:
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# predictor
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predictor: CifPredictor
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predictor_conf:
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idim: 256
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threshold: 1.0
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l_order: 1
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r_order: 1
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tail_threshold: 0.45
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# frontend related
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frontend: wav_frontend
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frontend: WavFrontend
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frontend_conf:
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fs: 16000
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window: hamming
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@ -51,29 +59,7 @@ frontend_conf:
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lfr_m: 1
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lfr_n: 1
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train_conf:
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accum_grad: 1
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grad_clip: 5
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max_epoch: 150
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val_scheduler_criterion:
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- valid
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- acc
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best_model_criterion:
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- - valid
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- acc
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- max
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keep_nbest_models: 10
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log_interval: 50
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optim: adam
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optim_conf:
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lr: 0.0005
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 30000
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specaug: specaug
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specaug: SpecAug
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specaug_conf:
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apply_time_warp: true
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time_warp_window: 5
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@ -89,25 +75,43 @@ specaug_conf:
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- 40
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num_time_mask: 2
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predictor: cif_predictor
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predictor_conf:
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idim: 256
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threshold: 1.0
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l_order: 1
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r_order: 1
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tail_threshold: 0.45
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train_conf:
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accum_grad: 1
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grad_clip: 5
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max_epoch: 150
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keep_nbest_models: 10
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avg_nbest_model: 5
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log_interval: 50
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optim: adam
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optim_conf:
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lr: 0.0005
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 30000
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dataset: AudioDataset
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dataset_conf:
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data_names: speech,text
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data_types: sound,text
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index_ds: IndexDSJsonl
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batch_sampler: RankFullLocalShuffleBatchSampler
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batch_type: example # example or length
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batch_size: 32 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
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max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
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buffer_size: 1024
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shuffle: True
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shuffle_conf:
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shuffle_size: 2048
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sort_size: 500
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batch_conf:
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batch_type: example
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batch_size: 2
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num_workers: 8
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num_workers: 0
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tokenizer: CharTokenizer
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tokenizer_conf:
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unk_symbol: <unk>
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split_with_space: true
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ctc_conf:
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dropout_rate: 0.0
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ctc_type: builtin
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reduce: true
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ignore_nan_grad: true
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normalize: null
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normalize: null
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9
examples/aishell/finetune.sh
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9
examples/aishell/finetune.sh
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cmd="funasr/bin/train.py"
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python $cmd \
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--config-path "/Users/zhifu/funasr_github/test_local/funasr_cli_egs" \
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--config-name "config.yaml" \
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++token_list="/Users/zhifu/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \
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++train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len.jsonl" \
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++output_dir="/nfs/zhifu.gzf/ckpt/funasr2/exp1"
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66
examples/aishell/paraformer/local/aishell_data_prep.sh
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66
examples/aishell/paraformer/local/aishell_data_prep.sh
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#!/bin/bash
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# Copyright 2017 Xingyu Na
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# Apache 2.0
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#. ./path.sh || exit 1;
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if [ $# != 3 ]; then
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echo "Usage: $0 <audio-path> <text-path> <output-path>"
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echo " $0 /export/a05/xna/data/data_aishell/wav /export/a05/xna/data/data_aishell/transcript data"
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exit 1;
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fi
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aishell_audio_dir=$1
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aishell_text=$2/aishell_transcript_v0.8.txt
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output_dir=$3
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train_dir=$output_dir/data/local/train
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dev_dir=$output_dir/data/local/dev
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test_dir=$output_dir/data/local/test
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tmp_dir=$output_dir/data/local/tmp
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mkdir -p $train_dir
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mkdir -p $dev_dir
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mkdir -p $test_dir
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mkdir -p $tmp_dir
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# data directory check
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if [ ! -d $aishell_audio_dir ] || [ ! -f $aishell_text ]; then
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echo "Error: $0 requires two directory arguments"
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exit 1;
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fi
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# find wav audio file for train, dev and test resp.
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find $aishell_audio_dir -iname "*.wav" > $tmp_dir/wav.flist
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n=`cat $tmp_dir/wav.flist | wc -l`
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[ $n -ne 141925 ] && \
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echo Warning: expected 141925 data data files, found $n
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grep -i "wav/train" $tmp_dir/wav.flist > $train_dir/wav.flist || exit 1;
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grep -i "wav/dev" $tmp_dir/wav.flist > $dev_dir/wav.flist || exit 1;
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grep -i "wav/test" $tmp_dir/wav.flist > $test_dir/wav.flist || exit 1;
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rm -r $tmp_dir
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# Transcriptions preparation
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for dir in $train_dir $dev_dir $test_dir; do
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echo Preparing $dir transcriptions
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sed -e 's/\.wav//' $dir/wav.flist | awk -F '/' '{print $NF}' > $dir/utt.list
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paste -d' ' $dir/utt.list $dir/wav.flist > $dir/wav.scp_all
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utils/filter_scp.pl -f 1 $dir/utt.list $aishell_text > $dir/transcripts.txt
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awk '{print $1}' $dir/transcripts.txt > $dir/utt.list
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utils/filter_scp.pl -f 1 $dir/utt.list $dir/wav.scp_all | sort -u > $dir/wav.scp
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sort -u $dir/transcripts.txt > $dir/text
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done
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mkdir -p $output_dir/data/train $output_dir/data/dev $output_dir/data/test
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for f in wav.scp text; do
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cp $train_dir/$f $output_dir/data/train/$f || exit 1;
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cp $dev_dir/$f $output_dir/data/dev/$f || exit 1;
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cp $test_dir/$f $output_dir/data/test/$f || exit 1;
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done
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echo "$0: AISHELL data preparation succeeded"
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exit 0;
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105
examples/aishell/paraformer/local/download_and_untar.sh
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examples/aishell/paraformer/local/download_and_untar.sh
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#!/usr/bin/env bash
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# Copyright 2014 Johns Hopkins University (author: Daniel Povey)
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# 2017 Xingyu Na
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# Apache 2.0
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remove_archive=false
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if [ "$1" == --remove-archive ]; then
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remove_archive=true
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shift
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fi
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if [ $# -ne 3 ]; then
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echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
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echo "e.g.: $0 /export/a05/xna/data www.openslr.org/resources/33 data_aishell"
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echo "With --remove-archive it will remove the archive after successfully un-tarring it."
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echo "<corpus-part> can be one of: data_aishell, resource_aishell."
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fi
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data=$1
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url=$2
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part=$3
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if [ ! -d "$data" ]; then
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echo "$0: no such directory $data"
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exit 1;
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fi
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part_ok=false
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list="data_aishell resource_aishell"
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for x in $list; do
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if [ "$part" == $x ]; then part_ok=true; fi
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done
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if ! $part_ok; then
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echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
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exit 1;
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fi
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if [ -z "$url" ]; then
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echo "$0: empty URL base."
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exit 1;
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fi
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if [ -f $data/$part/.complete ]; then
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echo "$0: data part $part was already successfully extracted, nothing to do."
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exit 0;
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fi
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# sizes of the archive files in bytes.
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sizes="15582913665 1246920"
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if [ -f $data/$part.tgz ]; then
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size=$(/bin/ls -l $data/$part.tgz | awk '{print $5}')
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size_ok=false
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for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
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if ! $size_ok; then
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echo "$0: removing existing file $data/$part.tgz because its size in bytes $size"
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echo "does not equal the size of one of the archives."
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rm $data/$part.tgz
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else
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echo "$data/$part.tgz exists and appears to be complete."
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fi
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fi
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if [ ! -f $data/$part.tgz ]; then
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if ! command -v wget >/dev/null; then
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echo "$0: wget is not installed."
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exit 1;
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fi
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full_url=$url/$part.tgz
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echo "$0: downloading data from $full_url. This may take some time, please be patient."
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cd $data || exit 1
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if ! wget --no-check-certificate $full_url; then
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echo "$0: error executing wget $full_url"
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exit 1;
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fi
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fi
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cd $data || exit 1
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if ! tar -xvzf $part.tgz; then
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echo "$0: error un-tarring archive $data/$part.tgz"
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exit 1;
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fi
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touch $data/$part/.complete
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if [ $part == "data_aishell" ]; then
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cd $data/$part/wav || exit 1
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for wav in ./*.tar.gz; do
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echo "Extracting wav from $wav"
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tar -zxf $wav && rm $wav
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done
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fi
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echo "$0: Successfully downloaded and un-tarred $data/$part.tgz"
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if $remove_archive; then
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echo "$0: removing $data/$part.tgz file since --remove-archive option was supplied."
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rm $data/$part.tgz
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fi
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exit 0;
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203
examples/aishell/paraformer/run.sh
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203
examples/aishell/paraformer/run.sh
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#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="0,1"
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gpu_num=2
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=1
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# general configuration
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feats_dir="../DATA" #feature output dictionary
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exp_dir="."
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lang=zh
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token_type=char
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stage=0
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stop_stage=5
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# feature configuration
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nj=64
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# data
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raw_data=../raw_data
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data_url=www.openslr.org/resources/33
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# exp tag
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tag="exp1"
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. utils/parse_options.sh || exit 1;
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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train_set=train
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valid_set=dev
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test_sets="dev test"
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asr_config=conf/train_asr_paraformer_conformer_12e_6d_2048_256.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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#inference_config=conf/decode_asr_transformer_noctc_1best.yaml
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#inference_asr_model=valid.acc.ave_10best.pb
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## you can set gpu num for decoding here
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#gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
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#ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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#
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#if ${gpu_inference}; then
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# inference_nj=$[${ngpu}*${njob}]
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# _ngpu=1
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#else
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# inference_nj=$njob
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# _ngpu=0
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#fi
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "stage -1: Data Download"
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local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
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local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "stage 0: Data preparation"
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# Data preparation
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local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
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for x in train dev test; do
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cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
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paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
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> ${feats_dir}/data/${x}/text
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utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
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mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
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python funasr/datasets/audio_datasets/scp2jsonl.py \
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++scp_file_list='["${feats_dir}/data/${x}/wav.scp", "${feats_dir}/data/${x}/text"]' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out=${feats_dir}/data/${x}/audio_datasets.jsonl
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done
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature and CMVN Generation"
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# utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
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python funasr/bin/compute_audio_cmvn.py \
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--config-path "/Users/zhifu/funasr1.0/examples/aishell/conf" \
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--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \
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++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
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++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
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++dataset_conf.num_workers=$nj
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fi
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token_list=${feats_dir}/data/${lang}_token_list/$token_type/tokens.txt
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echo "dictionary: ${token_list}"
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary Preparation"
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mkdir -p ${feats_dir}/data/${lang}_token_list/$token_type/
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echo "make a dictionary"
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echo "<blank>" > ${token_list}
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echo "<s>" >> ${token_list}
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echo "</s>" >> ${token_list}
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utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
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| sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
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||||
echo "<unk>" >> ${token_list}
|
||||
fi
|
||||
|
||||
# LM Training Stage
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
echo "stage 3: LM Training"
|
||||
fi
|
||||
|
||||
# ASR Training Stage
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
echo "stage 4: ASR Training"
|
||||
|
||||
torchrun \
|
||||
--nnodes 1 \
|
||||
--nproc_per_node ${gpu_num} \
|
||||
funasr/bin/train.py \
|
||||
--config-path "/Users/zhifu/funasr1.0/examples/aishell/conf" \
|
||||
--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \
|
||||
++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
|
||||
++cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
|
||||
++token_list="${token_list}" \
|
||||
++output_dir="${exp_dir}/exp/${model_dir}"
|
||||
fi
|
||||
|
||||
#
|
||||
## Testing Stage
|
||||
#if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
|
||||
# echo "stage 5: Inference"
|
||||
# for dset in ${test_sets}; do
|
||||
# asr_exp=${exp_dir}/exp/${model_dir}
|
||||
# inference_tag="$(basename "${inference_config}" .yaml)"
|
||||
# _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
|
||||
# _logdir="${_dir}/logdir"
|
||||
# if [ -d ${_dir} ]; then
|
||||
# echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
|
||||
# exit 0
|
||||
# fi
|
||||
# mkdir -p "${_logdir}"
|
||||
# _data="${feats_dir}/data/${dset}"
|
||||
# key_file=${_data}/${scp}
|
||||
# num_scp_file="$(<${key_file} wc -l)"
|
||||
# _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
||||
# split_scps=
|
||||
# for n in $(seq "${_nj}"); do
|
||||
# split_scps+=" ${_logdir}/keys.${n}.scp"
|
||||
# done
|
||||
# # shellcheck disable=SC2086
|
||||
# utils/split_scp.pl "${key_file}" ${split_scps}
|
||||
# _opts=
|
||||
# if [ -n "${inference_config}" ]; then
|
||||
# _opts+="--config ${inference_config} "
|
||||
# fi
|
||||
# ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
|
||||
# python -m funasr.bin.asr_inference_launch \
|
||||
# --batch_size 1 \
|
||||
# --ngpu "${_ngpu}" \
|
||||
# --njob ${njob} \
|
||||
# --gpuid_list ${gpuid_list} \
|
||||
# --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
|
||||
# --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \
|
||||
# --key_file "${_logdir}"/keys.JOB.scp \
|
||||
# --asr_train_config "${asr_exp}"/config.yaml \
|
||||
# --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
|
||||
# --output_dir "${_logdir}"/output.JOB \
|
||||
# --mode paraformer \
|
||||
# ${_opts}
|
||||
#
|
||||
# for f in token token_int score text; do
|
||||
# if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
|
||||
# for i in $(seq "${_nj}"); do
|
||||
# cat "${_logdir}/output.${i}/1best_recog/${f}"
|
||||
# done | sort -k1 >"${_dir}/${f}"
|
||||
# fi
|
||||
# done
|
||||
# python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
|
||||
# python utils/proce_text.py ${_data}/text ${_data}/text.proc
|
||||
# python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
|
||||
# tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
|
||||
# cat ${_dir}/text.cer.txt
|
||||
# done
|
||||
#fi
|
||||
#
|
||||
## Prepare files for ModelScope fine-tuning and inference
|
||||
#if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
|
||||
# echo "stage 6: ModelScope Preparation"
|
||||
# cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn
|
||||
# vocab_size=$(cat ${token_list} | wc -l)
|
||||
# python utils/gen_modelscope_configuration.py \
|
||||
# --am_model_name $inference_asr_model \
|
||||
# --mode paraformer \
|
||||
# --model_name paraformer \
|
||||
# --dataset aishell \
|
||||
# --output_dir $exp_dir/exp/$model_dir \
|
||||
# --vocab_size $vocab_size \
|
||||
# --nat _nat \
|
||||
# --tag $tag
|
||||
#fi
|
||||
47
examples/aishell/paraformer/utils/extract_embeds.py
Executable file
47
examples/aishell/paraformer/utils/extract_embeds.py
Executable file
@ -0,0 +1,47 @@
|
||||
from transformers import AutoTokenizer, AutoModel, pipeline
|
||||
import numpy as np
|
||||
import sys
|
||||
import os
|
||||
import torch
|
||||
from kaldiio import WriteHelper
|
||||
import re
|
||||
text_file_json = sys.argv[1]
|
||||
out_ark = sys.argv[2]
|
||||
out_scp = sys.argv[3]
|
||||
out_shape = sys.argv[4]
|
||||
device = int(sys.argv[5])
|
||||
model_path = sys.argv[6]
|
||||
|
||||
model = AutoModel.from_pretrained(model_path)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
||||
extractor = pipeline(task="feature-extraction", model=model, tokenizer=tokenizer, device=device)
|
||||
|
||||
with open(text_file_json, 'r') as f:
|
||||
js = f.readlines()
|
||||
|
||||
|
||||
f_shape = open(out_shape, "w")
|
||||
with WriteHelper('ark,scp:{},{}'.format(out_ark, out_scp)) as writer:
|
||||
with torch.no_grad():
|
||||
for idx, line in enumerate(js):
|
||||
id, tokens = line.strip().split(" ", 1)
|
||||
tokens = re.sub(" ", "", tokens.strip())
|
||||
tokens = ' '.join([j for j in tokens])
|
||||
token_num = len(tokens.split(" "))
|
||||
outputs = extractor(tokens)
|
||||
outputs = np.array(outputs)
|
||||
embeds = outputs[0, 1:-1, :]
|
||||
|
||||
token_num_embeds, dim = embeds.shape
|
||||
if token_num == token_num_embeds:
|
||||
writer(id, embeds)
|
||||
shape_line = "{} {},{}\n".format(id, token_num_embeds, dim)
|
||||
f_shape.write(shape_line)
|
||||
else:
|
||||
print("{}, size has changed, {}, {}, {}".format(id, token_num, token_num_embeds, tokens))
|
||||
|
||||
|
||||
|
||||
f_shape.close()
|
||||
|
||||
|
||||
87
examples/aishell/paraformer/utils/filter_scp.pl
Executable file
87
examples/aishell/paraformer/utils/filter_scp.pl
Executable file
@ -0,0 +1,87 @@
|
||||
#!/usr/bin/env perl
|
||||
# Copyright 2010-2012 Microsoft Corporation
|
||||
# Johns Hopkins University (author: Daniel Povey)
|
||||
|
||||
# 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
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# This script takes a list of utterance-ids or any file whose first field
|
||||
# of each line is an utterance-id, and filters an scp
|
||||
# file (or any file whose "n-th" field is an utterance id), printing
|
||||
# out only those lines whose "n-th" field is in id_list. The index of
|
||||
# the "n-th" field is 1, by default, but can be changed by using
|
||||
# the -f <n> switch
|
||||
|
||||
$exclude = 0;
|
||||
$field = 1;
|
||||
$shifted = 0;
|
||||
|
||||
do {
|
||||
$shifted=0;
|
||||
if ($ARGV[0] eq "--exclude") {
|
||||
$exclude = 1;
|
||||
shift @ARGV;
|
||||
$shifted=1;
|
||||
}
|
||||
if ($ARGV[0] eq "-f") {
|
||||
$field = $ARGV[1];
|
||||
shift @ARGV; shift @ARGV;
|
||||
$shifted=1
|
||||
}
|
||||
} while ($shifted);
|
||||
|
||||
if(@ARGV < 1 || @ARGV > 2) {
|
||||
die "Usage: filter_scp.pl [--exclude] [-f <field-to-filter-on>] id_list [in.scp] > out.scp \n" .
|
||||
"Prints only the input lines whose f'th field (default: first) is in 'id_list'.\n" .
|
||||
"Note: only the first field of each line in id_list matters. With --exclude, prints\n" .
|
||||
"only the lines that were *not* in id_list.\n" .
|
||||
"Caution: previously, the -f option was interpreted as a zero-based field index.\n" .
|
||||
"If your older scripts (written before Oct 2014) stopped working and you used the\n" .
|
||||
"-f option, add 1 to the argument.\n" .
|
||||
"See also: scripts/filter_scp.pl .\n";
|
||||
}
|
||||
|
||||
|
||||
$idlist = shift @ARGV;
|
||||
open(F, "<$idlist") || die "Could not open id-list file $idlist";
|
||||
while(<F>) {
|
||||
@A = split;
|
||||
@A>=1 || die "Invalid id-list file line $_";
|
||||
$seen{$A[0]} = 1;
|
||||
}
|
||||
|
||||
if ($field == 1) { # Treat this as special case, since it is common.
|
||||
while(<>) {
|
||||
$_ =~ m/\s*(\S+)\s*/ || die "Bad line $_, could not get first field.";
|
||||
# $1 is what we filter on.
|
||||
if ((!$exclude && $seen{$1}) || ($exclude && !defined $seen{$1})) {
|
||||
print $_;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
while(<>) {
|
||||
@A = split;
|
||||
@A > 0 || die "Invalid scp file line $_";
|
||||
@A >= $field || die "Invalid scp file line $_";
|
||||
if ((!$exclude && $seen{$A[$field-1]}) || ($exclude && !defined $seen{$A[$field-1]})) {
|
||||
print $_;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# tests:
|
||||
# the following should print "foo 1"
|
||||
# ( echo foo 1; echo bar 2 ) | scripts/filter_scp.pl <(echo foo)
|
||||
# the following should print "bar 2".
|
||||
# ( echo foo 1; echo bar 2 ) | scripts/filter_scp.pl -f 2 <(echo 2)
|
||||
35
examples/aishell/paraformer/utils/fix_data.sh
Executable file
35
examples/aishell/paraformer/utils/fix_data.sh
Executable file
@ -0,0 +1,35 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
echo "$0 $@"
|
||||
data_dir=$1
|
||||
|
||||
if [ ! -f ${data_dir}/wav.scp ]; then
|
||||
echo "$0: wav.scp is not found"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
if [ ! -f ${data_dir}/text ]; then
|
||||
echo "$0: text is not found"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
|
||||
|
||||
mkdir -p ${data_dir}/.backup
|
||||
|
||||
awk '{print $1}' ${data_dir}/wav.scp > ${data_dir}/.backup/wav_id
|
||||
awk '{print $1}' ${data_dir}/text > ${data_dir}/.backup/text_id
|
||||
|
||||
sort ${data_dir}/.backup/wav_id ${data_dir}/.backup/text_id | uniq -d > ${data_dir}/.backup/id
|
||||
|
||||
cp ${data_dir}/wav.scp ${data_dir}/.backup/wav.scp
|
||||
cp ${data_dir}/text ${data_dir}/.backup/text
|
||||
|
||||
mv ${data_dir}/wav.scp ${data_dir}/wav.scp.bak
|
||||
mv ${data_dir}/text ${data_dir}/text.bak
|
||||
|
||||
utils/filter_scp.pl -f 1 ${data_dir}/.backup/id ${data_dir}/wav.scp.bak | sort -k1,1 -u > ${data_dir}/wav.scp
|
||||
utils/filter_scp.pl -f 1 ${data_dir}/.backup/id ${data_dir}/text.bak | sort -k1,1 -u > ${data_dir}/text
|
||||
|
||||
rm ${data_dir}/wav.scp.bak
|
||||
rm ${data_dir}/text.bak
|
||||
52
examples/aishell/paraformer/utils/fix_data_feat.sh
Executable file
52
examples/aishell/paraformer/utils/fix_data_feat.sh
Executable file
@ -0,0 +1,52 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
echo "$0 $@"
|
||||
data_dir=$1
|
||||
|
||||
if [ ! -f ${data_dir}/feats.scp ]; then
|
||||
echo "$0: feats.scp is not found"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
if [ ! -f ${data_dir}/text ]; then
|
||||
echo "$0: text is not found"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
if [ ! -f ${data_dir}/speech_shape ]; then
|
||||
echo "$0: feature lengths is not found"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
if [ ! -f ${data_dir}/text_shape ]; then
|
||||
echo "$0: text lengths is not found"
|
||||
exit 1;
|
||||
fi
|
||||
|
||||
mkdir -p ${data_dir}/.backup
|
||||
|
||||
awk '{print $1}' ${data_dir}/feats.scp > ${data_dir}/.backup/wav_id
|
||||
awk '{print $1}' ${data_dir}/text > ${data_dir}/.backup/text_id
|
||||
|
||||
sort ${data_dir}/.backup/wav_id ${data_dir}/.backup/text_id | uniq -d > ${data_dir}/.backup/id
|
||||
|
||||
cp ${data_dir}/feats.scp ${data_dir}/.backup/feats.scp
|
||||
cp ${data_dir}/text ${data_dir}/.backup/text
|
||||
cp ${data_dir}/speech_shape ${data_dir}/.backup/speech_shape
|
||||
cp ${data_dir}/text_shape ${data_dir}/.backup/text_shape
|
||||
|
||||
mv ${data_dir}/feats.scp ${data_dir}/feats.scp.bak
|
||||
mv ${data_dir}/text ${data_dir}/text.bak
|
||||
mv ${data_dir}/speech_shape ${data_dir}/speech_shape.bak
|
||||
mv ${data_dir}/text_shape ${data_dir}/text_shape.bak
|
||||
|
||||
utils/filter_scp.pl -f 1 ${data_dir}/.backup/id ${data_dir}/feats.scp.bak | sort -k1,1 -u > ${data_dir}/feats.scp
|
||||
utils/filter_scp.pl -f 1 ${data_dir}/.backup/id ${data_dir}/text.bak | sort -k1,1 -u > ${data_dir}/text
|
||||
utils/filter_scp.pl -f 1 ${data_dir}/.backup/id ${data_dir}/speech_shape.bak | sort -k1,1 -u > ${data_dir}/speech_shape
|
||||
utils/filter_scp.pl -f 1 ${data_dir}/.backup/id ${data_dir}/text_shape.bak | sort -k1,1 -u > ${data_dir}/text_shape
|
||||
|
||||
rm ${data_dir}/feats.scp.bak
|
||||
rm ${data_dir}/text.bak
|
||||
rm ${data_dir}/speech_shape.bak
|
||||
rm ${data_dir}/text_shape.bak
|
||||
|
||||
97
examples/aishell/paraformer/utils/parse_options.sh
Executable file
97
examples/aishell/paraformer/utils/parse_options.sh
Executable file
@ -0,0 +1,97 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
|
||||
# Arnab Ghoshal, Karel Vesely
|
||||
|
||||
# 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
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# Parse command-line options.
|
||||
# To be sourced by another script (as in ". parse_options.sh").
|
||||
# Option format is: --option-name arg
|
||||
# and shell variable "option_name" gets set to value "arg."
|
||||
# The exception is --help, which takes no arguments, but prints the
|
||||
# $help_message variable (if defined).
|
||||
|
||||
|
||||
###
|
||||
### The --config file options have lower priority to command line
|
||||
### options, so we need to import them first...
|
||||
###
|
||||
|
||||
# Now import all the configs specified by command-line, in left-to-right order
|
||||
for ((argpos=1; argpos<$#; argpos++)); do
|
||||
if [ "${!argpos}" == "--config" ]; then
|
||||
argpos_plus1=$((argpos+1))
|
||||
config=${!argpos_plus1}
|
||||
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
|
||||
. $config # source the config file.
|
||||
fi
|
||||
done
|
||||
|
||||
|
||||
###
|
||||
### Now we process the command line options
|
||||
###
|
||||
while true; do
|
||||
[ -z "${1:-}" ] && break; # break if there are no arguments
|
||||
case "$1" in
|
||||
# If the enclosing script is called with --help option, print the help
|
||||
# message and exit. Scripts should put help messages in $help_message
|
||||
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
|
||||
else printf "$help_message\n" 1>&2 ; fi;
|
||||
exit 0 ;;
|
||||
--*=*) echo "$0: options to scripts must be of the form --name value, got '$1'"
|
||||
exit 1 ;;
|
||||
# If the first command-line argument begins with "--" (e.g. --foo-bar),
|
||||
# then work out the variable name as $name, which will equal "foo_bar".
|
||||
--*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`;
|
||||
# Next we test whether the variable in question is undefned-- if so it's
|
||||
# an invalid option and we die. Note: $0 evaluates to the name of the
|
||||
# enclosing script.
|
||||
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
|
||||
# is undefined. We then have to wrap this test inside "eval" because
|
||||
# foo_bar is itself inside a variable ($name).
|
||||
eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
|
||||
|
||||
oldval="`eval echo \\$$name`";
|
||||
# Work out whether we seem to be expecting a Boolean argument.
|
||||
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
|
||||
was_bool=true;
|
||||
else
|
||||
was_bool=false;
|
||||
fi
|
||||
|
||||
# Set the variable to the right value-- the escaped quotes make it work if
|
||||
# the option had spaces, like --cmd "queue.pl -sync y"
|
||||
eval $name=\"$2\";
|
||||
|
||||
# Check that Boolean-valued arguments are really Boolean.
|
||||
if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
|
||||
echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
|
||||
exit 1;
|
||||
fi
|
||||
shift 2;
|
||||
;;
|
||||
*) break;
|
||||
esac
|
||||
done
|
||||
|
||||
|
||||
# Check for an empty argument to the --cmd option, which can easily occur as a
|
||||
# result of scripting errors.
|
||||
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
|
||||
|
||||
|
||||
true; # so this script returns exit code 0.
|
||||
44
examples/aishell/paraformer/utils/shuffle_list.pl
Executable file
44
examples/aishell/paraformer/utils/shuffle_list.pl
Executable file
@ -0,0 +1,44 @@
|
||||
#!/usr/bin/env perl
|
||||
|
||||
# Copyright 2013 Johns Hopkins University (author: Daniel Povey)
|
||||
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
if ($ARGV[0] eq "--srand") {
|
||||
$n = $ARGV[1];
|
||||
$n =~ m/\d+/ || die "Bad argument to --srand option: \"$n\"";
|
||||
srand($ARGV[1]);
|
||||
shift;
|
||||
shift;
|
||||
} else {
|
||||
srand(0); # Gives inconsistent behavior if we don't seed.
|
||||
}
|
||||
|
||||
if (@ARGV > 1 || $ARGV[0] =~ m/^-.+/) { # >1 args, or an option we
|
||||
# don't understand.
|
||||
print "Usage: shuffle_list.pl [--srand N] [input file] > output\n";
|
||||
print "randomizes the order of lines of input.\n";
|
||||
exit(1);
|
||||
}
|
||||
|
||||
@lines;
|
||||
while (<>) {
|
||||
push @lines, [ (rand(), $_)] ;
|
||||
}
|
||||
|
||||
@lines = sort { $a->[0] cmp $b->[0] } @lines;
|
||||
foreach $l (@lines) {
|
||||
print $l->[1];
|
||||
}
|
||||
246
examples/aishell/paraformer/utils/split_scp.pl
Executable file
246
examples/aishell/paraformer/utils/split_scp.pl
Executable file
@ -0,0 +1,246 @@
|
||||
#!/usr/bin/env perl
|
||||
|
||||
# Copyright 2010-2011 Microsoft Corporation
|
||||
|
||||
# See ../../COPYING for clarification regarding multiple authors
|
||||
#
|
||||
# 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
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# This program splits up any kind of .scp or archive-type file.
|
||||
# If there is no utt2spk option it will work on any text file and
|
||||
# will split it up with an approximately equal number of lines in
|
||||
# each but.
|
||||
# With the --utt2spk option it will work on anything that has the
|
||||
# utterance-id as the first entry on each line; the utt2spk file is
|
||||
# of the form "utterance speaker" (on each line).
|
||||
# It splits it into equal size chunks as far as it can. If you use the utt2spk
|
||||
# option it will make sure these chunks coincide with speaker boundaries. In
|
||||
# this case, if there are more chunks than speakers (and in some other
|
||||
# circumstances), some of the resulting chunks will be empty and it will print
|
||||
# an error message and exit with nonzero status.
|
||||
# You will normally call this like:
|
||||
# split_scp.pl scp scp.1 scp.2 scp.3 ...
|
||||
# or
|
||||
# split_scp.pl --utt2spk=utt2spk scp scp.1 scp.2 scp.3 ...
|
||||
# Note that you can use this script to split the utt2spk file itself,
|
||||
# e.g. split_scp.pl --utt2spk=utt2spk utt2spk utt2spk.1 utt2spk.2 ...
|
||||
|
||||
# You can also call the scripts like:
|
||||
# split_scp.pl -j 3 0 scp scp.0
|
||||
# [note: with this option, it assumes zero-based indexing of the split parts,
|
||||
# i.e. the second number must be 0 <= n < num-jobs.]
|
||||
|
||||
use warnings;
|
||||
|
||||
$num_jobs = 0;
|
||||
$job_id = 0;
|
||||
$utt2spk_file = "";
|
||||
$one_based = 0;
|
||||
|
||||
for ($x = 1; $x <= 3 && @ARGV > 0; $x++) {
|
||||
if ($ARGV[0] eq "-j") {
|
||||
shift @ARGV;
|
||||
$num_jobs = shift @ARGV;
|
||||
$job_id = shift @ARGV;
|
||||
}
|
||||
if ($ARGV[0] =~ /--utt2spk=(.+)/) {
|
||||
$utt2spk_file=$1;
|
||||
shift;
|
||||
}
|
||||
if ($ARGV[0] eq '--one-based') {
|
||||
$one_based = 1;
|
||||
shift @ARGV;
|
||||
}
|
||||
}
|
||||
|
||||
if ($num_jobs != 0 && ($num_jobs < 0 || $job_id - $one_based < 0 ||
|
||||
$job_id - $one_based >= $num_jobs)) {
|
||||
die "$0: Invalid job number/index values for '-j $num_jobs $job_id" .
|
||||
($one_based ? " --one-based" : "") . "'\n"
|
||||
}
|
||||
|
||||
$one_based
|
||||
and $job_id--;
|
||||
|
||||
if(($num_jobs == 0 && @ARGV < 2) || ($num_jobs > 0 && (@ARGV < 1 || @ARGV > 2))) {
|
||||
die
|
||||
"Usage: split_scp.pl [--utt2spk=<utt2spk_file>] in.scp out1.scp out2.scp ...
|
||||
or: split_scp.pl -j num-jobs job-id [--one-based] [--utt2spk=<utt2spk_file>] in.scp [out.scp]
|
||||
... where 0 <= job-id < num-jobs, or 1 <= job-id <- num-jobs if --one-based.\n";
|
||||
}
|
||||
|
||||
$error = 0;
|
||||
$inscp = shift @ARGV;
|
||||
if ($num_jobs == 0) { # without -j option
|
||||
@OUTPUTS = @ARGV;
|
||||
} else {
|
||||
for ($j = 0; $j < $num_jobs; $j++) {
|
||||
if ($j == $job_id) {
|
||||
if (@ARGV > 0) { push @OUTPUTS, $ARGV[0]; }
|
||||
else { push @OUTPUTS, "-"; }
|
||||
} else {
|
||||
push @OUTPUTS, "/dev/null";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if ($utt2spk_file ne "") { # We have the --utt2spk option...
|
||||
open($u_fh, '<', $utt2spk_file) || die "$0: Error opening utt2spk file $utt2spk_file: $!\n";
|
||||
while(<$u_fh>) {
|
||||
@A = split;
|
||||
@A == 2 || die "$0: Bad line $_ in utt2spk file $utt2spk_file\n";
|
||||
($u,$s) = @A;
|
||||
$utt2spk{$u} = $s;
|
||||
}
|
||||
close $u_fh;
|
||||
open($i_fh, '<', $inscp) || die "$0: Error opening input scp file $inscp: $!\n";
|
||||
@spkrs = ();
|
||||
while(<$i_fh>) {
|
||||
@A = split;
|
||||
if(@A == 0) { die "$0: Empty or space-only line in scp file $inscp\n"; }
|
||||
$u = $A[0];
|
||||
$s = $utt2spk{$u};
|
||||
defined $s || die "$0: No utterance $u in utt2spk file $utt2spk_file\n";
|
||||
if(!defined $spk_count{$s}) {
|
||||
push @spkrs, $s;
|
||||
$spk_count{$s} = 0;
|
||||
$spk_data{$s} = []; # ref to new empty array.
|
||||
}
|
||||
$spk_count{$s}++;
|
||||
push @{$spk_data{$s}}, $_;
|
||||
}
|
||||
# Now split as equally as possible ..
|
||||
# First allocate spks to files by allocating an approximately
|
||||
# equal number of speakers.
|
||||
$numspks = @spkrs; # number of speakers.
|
||||
$numscps = @OUTPUTS; # number of output files.
|
||||
if ($numspks < $numscps) {
|
||||
die "$0: Refusing to split data because number of speakers $numspks " .
|
||||
"is less than the number of output .scp files $numscps\n";
|
||||
}
|
||||
for($scpidx = 0; $scpidx < $numscps; $scpidx++) {
|
||||
$scparray[$scpidx] = []; # [] is array reference.
|
||||
}
|
||||
for ($spkidx = 0; $spkidx < $numspks; $spkidx++) {
|
||||
$scpidx = int(($spkidx*$numscps) / $numspks);
|
||||
$spk = $spkrs[$spkidx];
|
||||
push @{$scparray[$scpidx]}, $spk;
|
||||
$scpcount[$scpidx] += $spk_count{$spk};
|
||||
}
|
||||
|
||||
# Now will try to reassign beginning + ending speakers
|
||||
# to different scp's and see if it gets more balanced.
|
||||
# Suppose objf we're minimizing is sum_i (num utts in scp[i] - average)^2.
|
||||
# We can show that if considering changing just 2 scp's, we minimize
|
||||
# this by minimizing the squared difference in sizes. This is
|
||||
# equivalent to minimizing the absolute difference in sizes. This
|
||||
# shows this method is bound to converge.
|
||||
|
||||
$changed = 1;
|
||||
while($changed) {
|
||||
$changed = 0;
|
||||
for($scpidx = 0; $scpidx < $numscps; $scpidx++) {
|
||||
# First try to reassign ending spk of this scp.
|
||||
if($scpidx < $numscps-1) {
|
||||
$sz = @{$scparray[$scpidx]};
|
||||
if($sz > 0) {
|
||||
$spk = $scparray[$scpidx]->[$sz-1];
|
||||
$count = $spk_count{$spk};
|
||||
$nutt1 = $scpcount[$scpidx];
|
||||
$nutt2 = $scpcount[$scpidx+1];
|
||||
if( abs( ($nutt2+$count) - ($nutt1-$count))
|
||||
< abs($nutt2 - $nutt1)) { # Would decrease
|
||||
# size-diff by reassigning spk...
|
||||
$scpcount[$scpidx+1] += $count;
|
||||
$scpcount[$scpidx] -= $count;
|
||||
pop @{$scparray[$scpidx]};
|
||||
unshift @{$scparray[$scpidx+1]}, $spk;
|
||||
$changed = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
if($scpidx > 0 && @{$scparray[$scpidx]} > 0) {
|
||||
$spk = $scparray[$scpidx]->[0];
|
||||
$count = $spk_count{$spk};
|
||||
$nutt1 = $scpcount[$scpidx-1];
|
||||
$nutt2 = $scpcount[$scpidx];
|
||||
if( abs( ($nutt2-$count) - ($nutt1+$count))
|
||||
< abs($nutt2 - $nutt1)) { # Would decrease
|
||||
# size-diff by reassigning spk...
|
||||
$scpcount[$scpidx-1] += $count;
|
||||
$scpcount[$scpidx] -= $count;
|
||||
shift @{$scparray[$scpidx]};
|
||||
push @{$scparray[$scpidx-1]}, $spk;
|
||||
$changed = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
# Now print out the files...
|
||||
for($scpidx = 0; $scpidx < $numscps; $scpidx++) {
|
||||
$scpfile = $OUTPUTS[$scpidx];
|
||||
($scpfile ne '-' ? open($f_fh, '>', $scpfile)
|
||||
: open($f_fh, '>&', \*STDOUT)) ||
|
||||
die "$0: Could not open scp file $scpfile for writing: $!\n";
|
||||
$count = 0;
|
||||
if(@{$scparray[$scpidx]} == 0) {
|
||||
print STDERR "$0: eError: split_scp.pl producing empty .scp file " .
|
||||
"$scpfile (too many splits and too few speakers?)\n";
|
||||
$error = 1;
|
||||
} else {
|
||||
foreach $spk ( @{$scparray[$scpidx]} ) {
|
||||
print $f_fh @{$spk_data{$spk}};
|
||||
$count += $spk_count{$spk};
|
||||
}
|
||||
$count == $scpcount[$scpidx] || die "Count mismatch [code error]";
|
||||
}
|
||||
close($f_fh);
|
||||
}
|
||||
} else {
|
||||
# This block is the "normal" case where there is no --utt2spk
|
||||
# option and we just break into equal size chunks.
|
||||
|
||||
open($i_fh, '<', $inscp) || die "$0: Error opening input scp file $inscp: $!\n";
|
||||
|
||||
$numscps = @OUTPUTS; # size of array.
|
||||
@F = ();
|
||||
while(<$i_fh>) {
|
||||
push @F, $_;
|
||||
}
|
||||
$numlines = @F;
|
||||
if($numlines == 0) {
|
||||
print STDERR "$0: error: empty input scp file $inscp\n";
|
||||
$error = 1;
|
||||
}
|
||||
$linesperscp = int( $numlines / $numscps); # the "whole part"..
|
||||
$linesperscp >= 1 || die "$0: You are splitting into too many pieces! [reduce \$nj ($numscps) to be smaller than the number of lines ($numlines) in $inscp]\n";
|
||||
$remainder = $numlines - ($linesperscp * $numscps);
|
||||
($remainder >= 0 && $remainder < $numlines) || die "bad remainder $remainder";
|
||||
# [just doing int() rounds down].
|
||||
$n = 0;
|
||||
for($scpidx = 0; $scpidx < @OUTPUTS; $scpidx++) {
|
||||
$scpfile = $OUTPUTS[$scpidx];
|
||||
($scpfile ne '-' ? open($o_fh, '>', $scpfile)
|
||||
: open($o_fh, '>&', \*STDOUT)) ||
|
||||
die "$0: Could not open scp file $scpfile for writing: $!\n";
|
||||
for($k = 0; $k < $linesperscp + ($scpidx < $remainder ? 1 : 0); $k++) {
|
||||
print $o_fh $F[$n++];
|
||||
}
|
||||
close($o_fh) || die "$0: Eror closing scp file $scpfile: $!\n";
|
||||
}
|
||||
$n == $numlines || die "$n != $numlines [code error]";
|
||||
}
|
||||
|
||||
exit ($error);
|
||||
135
examples/aishell/paraformer/utils/text2token.py
Executable file
135
examples/aishell/paraformer/utils/text2token.py
Executable file
@ -0,0 +1,135 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
|
||||
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
|
||||
|
||||
|
||||
import argparse
|
||||
import codecs
|
||||
import re
|
||||
import sys
|
||||
|
||||
is_python2 = sys.version_info[0] == 2
|
||||
|
||||
|
||||
def exist_or_not(i, match_pos):
|
||||
start_pos = None
|
||||
end_pos = None
|
||||
for pos in match_pos:
|
||||
if pos[0] <= i < pos[1]:
|
||||
start_pos = pos[0]
|
||||
end_pos = pos[1]
|
||||
break
|
||||
|
||||
return start_pos, end_pos
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="convert raw text to tokenized text",
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--nchar",
|
||||
"-n",
|
||||
default=1,
|
||||
type=int,
|
||||
help="number of characters to split, i.e., \
|
||||
aabb -> a a b b with -n 1 and aa bb with -n 2",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--skip-ncols", "-s", default=0, type=int, help="skip first n columns"
|
||||
)
|
||||
parser.add_argument("--space", default="<space>", type=str, help="space symbol")
|
||||
parser.add_argument(
|
||||
"--non-lang-syms",
|
||||
"-l",
|
||||
default=None,
|
||||
type=str,
|
||||
help="list of non-linguistic symobles, e.g., <NOISE> etc.",
|
||||
)
|
||||
parser.add_argument("text", type=str, default=False, nargs="?", help="input text")
|
||||
parser.add_argument(
|
||||
"--trans_type",
|
||||
"-t",
|
||||
type=str,
|
||||
default="char",
|
||||
choices=["char", "phn"],
|
||||
help="""Transcript type. char/phn. e.g., for TIMIT FADG0_SI1279 -
|
||||
If trans_type is char,
|
||||
read from SI1279.WRD file -> "bricks are an alternative"
|
||||
Else if trans_type is phn,
|
||||
read from SI1279.PHN file -> "sil b r ih sil k s aa r er n aa l
|
||||
sil t er n ih sil t ih v sil" """,
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
rs = []
|
||||
if args.non_lang_syms is not None:
|
||||
with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f:
|
||||
nls = [x.rstrip() for x in f.readlines()]
|
||||
rs = [re.compile(re.escape(x)) for x in nls]
|
||||
|
||||
if args.text:
|
||||
f = codecs.open(args.text, encoding="utf-8")
|
||||
else:
|
||||
f = codecs.getreader("utf-8")(sys.stdin if is_python2 else sys.stdin.buffer)
|
||||
|
||||
sys.stdout = codecs.getwriter("utf-8")(
|
||||
sys.stdout if is_python2 else sys.stdout.buffer
|
||||
)
|
||||
line = f.readline()
|
||||
n = args.nchar
|
||||
while line:
|
||||
x = line.split()
|
||||
print(" ".join(x[: args.skip_ncols]), end=" ")
|
||||
a = " ".join(x[args.skip_ncols :])
|
||||
|
||||
# get all matched positions
|
||||
match_pos = []
|
||||
for r in rs:
|
||||
i = 0
|
||||
while i >= 0:
|
||||
m = r.search(a, i)
|
||||
if m:
|
||||
match_pos.append([m.start(), m.end()])
|
||||
i = m.end()
|
||||
else:
|
||||
break
|
||||
|
||||
if args.trans_type == "phn":
|
||||
a = a.split(" ")
|
||||
else:
|
||||
if len(match_pos) > 0:
|
||||
chars = []
|
||||
i = 0
|
||||
while i < len(a):
|
||||
start_pos, end_pos = exist_or_not(i, match_pos)
|
||||
if start_pos is not None:
|
||||
chars.append(a[start_pos:end_pos])
|
||||
i = end_pos
|
||||
else:
|
||||
chars.append(a[i])
|
||||
i += 1
|
||||
a = chars
|
||||
|
||||
a = [a[j : j + n] for j in range(0, len(a), n)]
|
||||
|
||||
a_flat = []
|
||||
for z in a:
|
||||
a_flat.append("".join(z))
|
||||
|
||||
a_chars = [z.replace(" ", args.space) for z in a_flat]
|
||||
if args.trans_type == "phn":
|
||||
a_chars = [z.replace("sil", args.space) for z in a_chars]
|
||||
print(" ".join(a_chars))
|
||||
line = f.readline()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
106
examples/aishell/paraformer/utils/text_tokenize.py
Executable file
106
examples/aishell/paraformer/utils/text_tokenize.py
Executable file
@ -0,0 +1,106 @@
|
||||
import re
|
||||
import argparse
|
||||
|
||||
|
||||
def load_dict(seg_file):
|
||||
seg_dict = {}
|
||||
with open(seg_file, 'r') as infile:
|
||||
for line in infile:
|
||||
s = line.strip().split()
|
||||
key = s[0]
|
||||
value = s[1:]
|
||||
seg_dict[key] = " ".join(value)
|
||||
return seg_dict
|
||||
|
||||
|
||||
def forward_segment(text, dic):
|
||||
word_list = []
|
||||
i = 0
|
||||
while i < len(text):
|
||||
longest_word = text[i]
|
||||
for j in range(i + 1, len(text) + 1):
|
||||
word = text[i:j]
|
||||
if word in dic:
|
||||
if len(word) > len(longest_word):
|
||||
longest_word = word
|
||||
word_list.append(longest_word)
|
||||
i += len(longest_word)
|
||||
return word_list
|
||||
|
||||
|
||||
def tokenize(txt,
|
||||
seg_dict):
|
||||
out_txt = ""
|
||||
pattern = re.compile(r"([\u4E00-\u9FA5A-Za-z0-9])")
|
||||
for word in txt:
|
||||
if pattern.match(word):
|
||||
if word in seg_dict:
|
||||
out_txt += seg_dict[word] + " "
|
||||
else:
|
||||
out_txt += "<unk>" + " "
|
||||
else:
|
||||
continue
|
||||
return out_txt.strip()
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="text tokenize",
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--text-file",
|
||||
"-t",
|
||||
default=False,
|
||||
required=True,
|
||||
type=str,
|
||||
help="input text",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seg-file",
|
||||
"-s",
|
||||
default=False,
|
||||
required=True,
|
||||
type=str,
|
||||
help="seg file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--txt-index",
|
||||
"-i",
|
||||
default=1,
|
||||
required=True,
|
||||
type=int,
|
||||
help="txt index",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-dir",
|
||||
"-o",
|
||||
default=False,
|
||||
required=True,
|
||||
type=str,
|
||||
help="output dir",
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
txt_writer = open("{}/text.{}.txt".format(args.output_dir, args.txt_index), 'w')
|
||||
shape_writer = open("{}/len.{}".format(args.output_dir, args.txt_index), 'w')
|
||||
seg_dict = load_dict(args.seg_file)
|
||||
with open(args.text_file, 'r') as infile:
|
||||
for line in infile:
|
||||
s = line.strip().split()
|
||||
text_id = s[0]
|
||||
text_list = forward_segment("".join(s[1:]).lower(), seg_dict)
|
||||
text = tokenize(text_list, seg_dict)
|
||||
lens = len(text.strip().split())
|
||||
txt_writer.write(text_id + " " + text + '\n')
|
||||
shape_writer.write(text_id + " " + str(lens) + '\n')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
35
examples/aishell/paraformer/utils/text_tokenize.sh
Executable file
35
examples/aishell/paraformer/utils/text_tokenize.sh
Executable file
@ -0,0 +1,35 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
|
||||
# Begin configuration section.
|
||||
nj=32
|
||||
cmd=utils/run.pl
|
||||
|
||||
echo "$0 $@"
|
||||
|
||||
. utils/parse_options.sh || exit 1;
|
||||
|
||||
# tokenize configuration
|
||||
text_dir=$1
|
||||
seg_file=$2
|
||||
logdir=$3
|
||||
output_dir=$4
|
||||
|
||||
txt_dir=${output_dir}/txt; mkdir -p ${output_dir}/txt
|
||||
mkdir -p ${logdir}
|
||||
|
||||
$cmd JOB=1:$nj $logdir/text_tokenize.JOB.log \
|
||||
python utils/text_tokenize.py -t ${text_dir}/txt/text.JOB.txt \
|
||||
-s ${seg_file} -i JOB -o ${txt_dir} \
|
||||
|| exit 1;
|
||||
|
||||
# concatenate the text files together.
|
||||
for n in $(seq $nj); do
|
||||
cat ${txt_dir}/text.$n.txt || exit 1
|
||||
done > ${output_dir}/text || exit 1
|
||||
|
||||
for n in $(seq $nj); do
|
||||
cat ${txt_dir}/len.$n || exit 1
|
||||
done > ${output_dir}/text_shape || exit 1
|
||||
|
||||
echo "$0: Succeeded text tokenize"
|
||||
834
examples/aishell/paraformer/utils/textnorm_zh.py
Executable file
834
examples/aishell/paraformer/utils/textnorm_zh.py
Executable file
@ -0,0 +1,834 @@
|
||||
#!/usr/bin/env python3
|
||||
# coding=utf-8
|
||||
|
||||
# Authors:
|
||||
# 2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git)
|
||||
# 2019.9 Jiayu DU
|
||||
#
|
||||
# requirements:
|
||||
# - python 3.X
|
||||
# notes: python 2.X WILL fail or produce misleading results
|
||||
|
||||
import sys, os, argparse, codecs, string, re
|
||||
|
||||
# ================================================================================ #
|
||||
# basic constant
|
||||
# ================================================================================ #
|
||||
CHINESE_DIGIS = u'零一二三四五六七八九'
|
||||
BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖'
|
||||
BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖'
|
||||
SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万'
|
||||
SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬'
|
||||
LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载'
|
||||
LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載'
|
||||
SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万'
|
||||
SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬'
|
||||
|
||||
ZERO_ALT = u'〇'
|
||||
ONE_ALT = u'幺'
|
||||
TWO_ALTS = [u'两', u'兩']
|
||||
|
||||
POSITIVE = [u'正', u'正']
|
||||
NEGATIVE = [u'负', u'負']
|
||||
POINT = [u'点', u'點']
|
||||
# PLUS = [u'加', u'加']
|
||||
# SIL = [u'杠', u'槓']
|
||||
|
||||
FILLER_CHARS = ['呃', '啊']
|
||||
ER_WHITELIST = '(儿女|儿子|儿孙|女儿|儿媳|妻儿|' \
|
||||
'胎儿|婴儿|新生儿|婴幼儿|幼儿|少儿|小儿|儿歌|儿童|儿科|托儿所|孤儿|' \
|
||||
'儿戏|儿化|台儿庄|鹿儿岛|正儿八经|吊儿郎当|生儿育女|托儿带女|养儿防老|痴儿呆女|' \
|
||||
'佳儿佳妇|儿怜兽扰|儿无常父|儿不嫌母丑|儿行千里母担忧|儿大不由爷|苏乞儿)'
|
||||
|
||||
# 中文数字系统类型
|
||||
NUMBERING_TYPES = ['low', 'mid', 'high']
|
||||
|
||||
CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \
|
||||
'里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)'
|
||||
CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)'
|
||||
COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \
|
||||
'砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \
|
||||
'针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \
|
||||
'毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \
|
||||
'盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \
|
||||
'纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)'
|
||||
|
||||
# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git)
|
||||
CHINESE_PUNC_STOP = '！？｡。'
|
||||
CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏'
|
||||
CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP
|
||||
|
||||
# ================================================================================ #
|
||||
# basic class
|
||||
# ================================================================================ #
|
||||
class ChineseChar(object):
|
||||
"""
|
||||
中文字符
|
||||
每个字符对应简体和繁体,
|
||||
e.g. 简体 = '负', 繁体 = '負'
|
||||
转换时可转换为简体或繁体
|
||||
"""
|
||||
|
||||
def __init__(self, simplified, traditional):
|
||||
self.simplified = simplified
|
||||
self.traditional = traditional
|
||||
#self.__repr__ = self.__str__
|
||||
|
||||
def __str__(self):
|
||||
return self.simplified or self.traditional or None
|
||||
|
||||
def __repr__(self):
|
||||
return self.__str__()
|
||||
|
||||
|
||||
class ChineseNumberUnit(ChineseChar):
|
||||
"""
|
||||
中文数字/数位字符
|
||||
每个字符除繁简体外还有一个额外的大写字符
|
||||
e.g. '陆' 和 '陸'
|
||||
"""
|
||||
|
||||
def __init__(self, power, simplified, traditional, big_s, big_t):
|
||||
super(ChineseNumberUnit, self).__init__(simplified, traditional)
|
||||
self.power = power
|
||||
self.big_s = big_s
|
||||
self.big_t = big_t
|
||||
|
||||
def __str__(self):
|
||||
return '10^{}'.format(self.power)
|
||||
|
||||
@classmethod
|
||||
def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
|
||||
|
||||
if small_unit:
|
||||
return ChineseNumberUnit(power=index + 1,
|
||||
simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1])
|
||||
elif numbering_type == NUMBERING_TYPES[0]:
|
||||
return ChineseNumberUnit(power=index + 8,
|
||||
simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
|
||||
elif numbering_type == NUMBERING_TYPES[1]:
|
||||
return ChineseNumberUnit(power=(index + 2) * 4,
|
||||
simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
|
||||
elif numbering_type == NUMBERING_TYPES[2]:
|
||||
return ChineseNumberUnit(power=pow(2, index + 3),
|
||||
simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
|
||||
else:
|
||||
raise ValueError(
|
||||
'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type))
|
||||
|
||||
|
||||
class ChineseNumberDigit(ChineseChar):
|
||||
"""
|
||||
中文数字字符
|
||||
"""
|
||||
|
||||
def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None):
|
||||
super(ChineseNumberDigit, self).__init__(simplified, traditional)
|
||||
self.value = value
|
||||
self.big_s = big_s
|
||||
self.big_t = big_t
|
||||
self.alt_s = alt_s
|
||||
self.alt_t = alt_t
|
||||
|
||||
def __str__(self):
|
||||
return str(self.value)
|
||||
|
||||
@classmethod
|
||||
def create(cls, i, v):
|
||||
return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
|
||||
|
||||
|
||||
class ChineseMath(ChineseChar):
|
||||
"""
|
||||
中文数位字符
|
||||
"""
|
||||
|
||||
def __init__(self, simplified, traditional, symbol, expression=None):
|
||||
super(ChineseMath, self).__init__(simplified, traditional)
|
||||
self.symbol = symbol
|
||||
self.expression = expression
|
||||
self.big_s = simplified
|
||||
self.big_t = traditional
|
||||
|
||||
|
||||
CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
|
||||
|
||||
|
||||
class NumberSystem(object):
|
||||
"""
|
||||
中文数字系统
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class MathSymbol(object):
|
||||
"""
|
||||
用于中文数字系统的数学符号 (繁/简体), e.g.
|
||||
positive = ['正', '正']
|
||||
negative = ['负', '負']
|
||||
point = ['点', '點']
|
||||
"""
|
||||
|
||||
def __init__(self, positive, negative, point):
|
||||
self.positive = positive
|
||||
self.negative = negative
|
||||
self.point = point
|
||||
|
||||
def __iter__(self):
|
||||
for v in self.__dict__.values():
|
||||
yield v
|
||||
|
||||
|
||||
# class OtherSymbol(object):
|
||||
# """
|
||||
# 其他符号
|
||||
# """
|
||||
#
|
||||
# def __init__(self, sil):
|
||||
# self.sil = sil
|
||||
#
|
||||
# def __iter__(self):
|
||||
# for v in self.__dict__.values():
|
||||
# yield v
|
||||
|
||||
|
||||
# ================================================================================ #
|
||||
# basic utils
|
||||
# ================================================================================ #
|
||||
def create_system(numbering_type=NUMBERING_TYPES[1]):
|
||||
"""
|
||||
根据数字系统类型返回创建相应的数字系统，默认为 mid
|
||||
NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
|
||||
low: '兆' = '亿' * '十' = $10^{9}$, '京' = '兆' * '十', etc.
|
||||
mid: '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
|
||||
high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
|
||||
返回对应的数字系统
|
||||
"""
|
||||
|
||||
# chinese number units of '亿' and larger
|
||||
all_larger_units = zip(
|
||||
LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL)
|
||||
larger_units = [CNU.create(i, v, numbering_type, False)
|
||||
for i, v in enumerate(all_larger_units)]
|
||||
# chinese number units of '十, 百, 千, 万'
|
||||
all_smaller_units = zip(
|
||||
SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL)
|
||||
smaller_units = [CNU.create(i, v, small_unit=True)
|
||||
for i, v in enumerate(all_smaller_units)]
|
||||
# digis
|
||||
chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS,
|
||||
BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL)
|
||||
digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
|
||||
digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
|
||||
digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
|
||||
digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
|
||||
|
||||
# symbols
|
||||
positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x)
|
||||
negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x)
|
||||
point_cn = CM(POINT[0], POINT[1], '.', lambda x,
|
||||
y: float(str(x) + '.' + str(y)))
|
||||
# sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
|
||||
system = NumberSystem()
|
||||
system.units = smaller_units + larger_units
|
||||
system.digits = digits
|
||||
system.math = MathSymbol(positive_cn, negative_cn, point_cn)
|
||||
# system.symbols = OtherSymbol(sil_cn)
|
||||
return system
|
||||
|
||||
|
||||
def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
|
||||
|
||||
def get_symbol(char, system):
|
||||
for u in system.units:
|
||||
if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
|
||||
return u
|
||||
for d in system.digits:
|
||||
if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]:
|
||||
return d
|
||||
for m in system.math:
|
||||
if char in [m.traditional, m.simplified]:
|
||||
return m
|
||||
|
||||
def string2symbols(chinese_string, system):
|
||||
int_string, dec_string = chinese_string, ''
|
||||
for p in [system.math.point.simplified, system.math.point.traditional]:
|
||||
if p in chinese_string:
|
||||
int_string, dec_string = chinese_string.split(p)
|
||||
break
|
||||
return [get_symbol(c, system) for c in int_string], \
|
||||
[get_symbol(c, system) for c in dec_string]
|
||||
|
||||
def correct_symbols(integer_symbols, system):
|
||||
"""
|
||||
一百八 to 一百八十
|
||||
一亿一千三百万 to 一亿 一千万 三百万
|
||||
"""
|
||||
|
||||
if integer_symbols and isinstance(integer_symbols[0], CNU):
|
||||
if integer_symbols[0].power == 1:
|
||||
integer_symbols = [system.digits[1]] + integer_symbols
|
||||
|
||||
if len(integer_symbols) > 1:
|
||||
if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU):
|
||||
integer_symbols.append(
|
||||
CNU(integer_symbols[-2].power - 1, None, None, None, None))
|
||||
|
||||
result = []
|
||||
unit_count = 0
|
||||
for s in integer_symbols:
|
||||
if isinstance(s, CND):
|
||||
result.append(s)
|
||||
unit_count = 0
|
||||
elif isinstance(s, CNU):
|
||||
current_unit = CNU(s.power, None, None, None, None)
|
||||
unit_count += 1
|
||||
|
||||
if unit_count == 1:
|
||||
result.append(current_unit)
|
||||
elif unit_count > 1:
|
||||
for i in range(len(result)):
|
||||
if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power:
|
||||
result[-i - 1] = CNU(result[-i - 1].power +
|
||||
current_unit.power, None, None, None, None)
|
||||
return result
|
||||
|
||||
def compute_value(integer_symbols):
|
||||
"""
|
||||
Compute the value.
|
||||
When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
|
||||
e.g. '两千万' = 2000 * 10000 not 2000 + 10000
|
||||
"""
|
||||
value = [0]
|
||||
last_power = 0
|
||||
for s in integer_symbols:
|
||||
if isinstance(s, CND):
|
||||
value[-1] = s.value
|
||||
elif isinstance(s, CNU):
|
||||
value[-1] *= pow(10, s.power)
|
||||
if s.power > last_power:
|
||||
value[:-1] = list(map(lambda v: v *
|
||||
pow(10, s.power), value[:-1]))
|
||||
last_power = s.power
|
||||
value.append(0)
|
||||
return sum(value)
|
||||
|
||||
system = create_system(numbering_type)
|
||||
int_part, dec_part = string2symbols(chinese_string, system)
|
||||
int_part = correct_symbols(int_part, system)
|
||||
int_str = str(compute_value(int_part))
|
||||
dec_str = ''.join([str(d.value) for d in dec_part])
|
||||
if dec_part:
|
||||
return '{0}.{1}'.format(int_str, dec_str)
|
||||
else:
|
||||
return int_str
|
||||
|
||||
|
||||
def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False,
|
||||
traditional=False, alt_zero=False, alt_one=False, alt_two=True,
|
||||
use_zeros=True, use_units=True):
|
||||
|
||||
def get_value(value_string, use_zeros=True):
|
||||
|
||||
striped_string = value_string.lstrip('0')
|
||||
|
||||
# record nothing if all zeros
|
||||
if not striped_string:
|
||||
return []
|
||||
|
||||
# record one digits
|
||||
elif len(striped_string) == 1:
|
||||
if use_zeros and len(value_string) != len(striped_string):
|
||||
return [system.digits[0], system.digits[int(striped_string)]]
|
||||
else:
|
||||
return [system.digits[int(striped_string)]]
|
||||
|
||||
# recursively record multiple digits
|
||||
else:
|
||||
result_unit = next(u for u in reversed(
|
||||
system.units) if u.power < len(striped_string))
|
||||
result_string = value_string[:-result_unit.power]
|
||||
return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:])
|
||||
|
||||
system = create_system(numbering_type)
|
||||
|
||||
int_dec = number_string.split('.')
|
||||
if len(int_dec) == 1:
|
||||
int_string = int_dec[0]
|
||||
dec_string = ""
|
||||
elif len(int_dec) == 2:
|
||||
int_string = int_dec[0]
|
||||
dec_string = int_dec[1]
|
||||
else:
|
||||
raise ValueError(
|
||||
"invalid input num string with more than one dot: {}".format(number_string))
|
||||
|
||||
if use_units and len(int_string) > 1:
|
||||
result_symbols = get_value(int_string)
|
||||
else:
|
||||
result_symbols = [system.digits[int(c)] for c in int_string]
|
||||
dec_symbols = [system.digits[int(c)] for c in dec_string]
|
||||
if dec_string:
|
||||
result_symbols += [system.math.point] + dec_symbols
|
||||
|
||||
if alt_two:
|
||||
liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t,
|
||||
system.digits[2].big_s, system.digits[2].big_t)
|
||||
for i, v in enumerate(result_symbols):
|
||||
if isinstance(v, CND) and v.value == 2:
|
||||
next_symbol = result_symbols[i +
|
||||
1] if i < len(result_symbols) - 1 else None
|
||||
previous_symbol = result_symbols[i - 1] if i > 0 else None
|
||||
if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))):
|
||||
if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)):
|
||||
result_symbols[i] = liang
|
||||
|
||||
# if big is True, '两' will not be used and `alt_two` has no impact on output
|
||||
if big:
|
||||
attr_name = 'big_'
|
||||
if traditional:
|
||||
attr_name += 't'
|
||||
else:
|
||||
attr_name += 's'
|
||||
else:
|
||||
if traditional:
|
||||
attr_name = 'traditional'
|
||||
else:
|
||||
attr_name = 'simplified'
|
||||
|
||||
result = ''.join([getattr(s, attr_name) for s in result_symbols])
|
||||
|
||||
# if not use_zeros:
|
||||
# result = result.strip(getattr(system.digits[0], attr_name))
|
||||
|
||||
if alt_zero:
|
||||
result = result.replace(
|
||||
getattr(system.digits[0], attr_name), system.digits[0].alt_s)
|
||||
|
||||
if alt_one:
|
||||
result = result.replace(
|
||||
getattr(system.digits[1], attr_name), system.digits[1].alt_s)
|
||||
|
||||
for i, p in enumerate(POINT):
|
||||
if result.startswith(p):
|
||||
return CHINESE_DIGIS[0] + result
|
||||
|
||||
# ^10, 11, .., 19
|
||||
if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
|
||||
SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \
|
||||
result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]:
|
||||
result = result[1:]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ================================================================================ #
|
||||
# different types of rewriters
|
||||
# ================================================================================ #
|
||||
class Cardinal:
|
||||
"""
|
||||
CARDINAL类
|
||||
"""
|
||||
|
||||
def __init__(self, cardinal=None, chntext=None):
|
||||
self.cardinal = cardinal
|
||||
self.chntext = chntext
|
||||
|
||||
def chntext2cardinal(self):
|
||||
return chn2num(self.chntext)
|
||||
|
||||
def cardinal2chntext(self):
|
||||
return num2chn(self.cardinal)
|
||||
|
||||
class Digit:
|
||||
"""
|
||||
DIGIT类
|
||||
"""
|
||||
|
||||
def __init__(self, digit=None, chntext=None):
|
||||
self.digit = digit
|
||||
self.chntext = chntext
|
||||
|
||||
# def chntext2digit(self):
|
||||
# return chn2num(self.chntext)
|
||||
|
||||
def digit2chntext(self):
|
||||
return num2chn(self.digit, alt_two=False, use_units=False)
|
||||
|
||||
|
||||
class TelePhone:
|
||||
"""
|
||||
TELEPHONE类
|
||||
"""
|
||||
|
||||
def __init__(self, telephone=None, raw_chntext=None, chntext=None):
|
||||
self.telephone = telephone
|
||||
self.raw_chntext = raw_chntext
|
||||
self.chntext = chntext
|
||||
|
||||
# def chntext2telephone(self):
|
||||
# sil_parts = self.raw_chntext.split('<SIL>')
|
||||
# self.telephone = '-'.join([
|
||||
# str(chn2num(p)) for p in sil_parts
|
||||
# ])
|
||||
# return self.telephone
|
||||
|
||||
def telephone2chntext(self, fixed=False):
|
||||
|
||||
if fixed:
|
||||
sil_parts = self.telephone.split('-')
|
||||
self.raw_chntext = '<SIL>'.join([
|
||||
num2chn(part, alt_two=False, use_units=False) for part in sil_parts
|
||||
])
|
||||
self.chntext = self.raw_chntext.replace('<SIL>', '')
|
||||
else:
|
||||
sp_parts = self.telephone.strip('+').split()
|
||||
self.raw_chntext = '<SP>'.join([
|
||||
num2chn(part, alt_two=False, use_units=False) for part in sp_parts
|
||||
])
|
||||
self.chntext = self.raw_chntext.replace('<SP>', '')
|
||||
return self.chntext
|
||||
|
||||
|
||||
class Fraction:
|
||||
"""
|
||||
FRACTION类
|
||||
"""
|
||||
|
||||
def __init__(self, fraction=None, chntext=None):
|
||||
self.fraction = fraction
|
||||
self.chntext = chntext
|
||||
|
||||
def chntext2fraction(self):
|
||||
denominator, numerator = self.chntext.split('分之')
|
||||
return chn2num(numerator) + '/' + chn2num(denominator)
|
||||
|
||||
def fraction2chntext(self):
|
||||
numerator, denominator = self.fraction.split('/')
|
||||
return num2chn(denominator) + '分之' + num2chn(numerator)
|
||||
|
||||
|
||||
class Date:
|
||||
"""
|
||||
DATE类
|
||||
"""
|
||||
|
||||
def __init__(self, date=None, chntext=None):
|
||||
self.date = date
|
||||
self.chntext = chntext
|
||||
|
||||
# def chntext2date(self):
|
||||
# chntext = self.chntext
|
||||
# try:
|
||||
# year, other = chntext.strip().split('年', maxsplit=1)
|
||||
# year = Digit(chntext=year).digit2chntext() + '年'
|
||||
# except ValueError:
|
||||
# other = chntext
|
||||
# year = ''
|
||||
# if other:
|
||||
# try:
|
||||
# month, day = other.strip().split('月', maxsplit=1)
|
||||
# month = Cardinal(chntext=month).chntext2cardinal() + '月'
|
||||
# except ValueError:
|
||||
# day = chntext
|
||||
# month = ''
|
||||
# if day:
|
||||
# day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
|
||||
# else:
|
||||
# month = ''
|
||||
# day = ''
|
||||
# date = year + month + day
|
||||
# self.date = date
|
||||
# return self.date
|
||||
|
||||
def date2chntext(self):
|
||||
date = self.date
|
||||
try:
|
||||
year, other = date.strip().split('年', 1)
|
||||
year = Digit(digit=year).digit2chntext() + '年'
|
||||
except ValueError:
|
||||
other = date
|
||||
year = ''
|
||||
if other:
|
||||
try:
|
||||
month, day = other.strip().split('月', 1)
|
||||
month = Cardinal(cardinal=month).cardinal2chntext() + '月'
|
||||
except ValueError:
|
||||
day = date
|
||||
month = ''
|
||||
if day:
|
||||
day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
|
||||
else:
|
||||
month = ''
|
||||
day = ''
|
||||
chntext = year + month + day
|
||||
self.chntext = chntext
|
||||
return self.chntext
|
||||
|
||||
|
||||
class Money:
|
||||
"""
|
||||
MONEY类
|
||||
"""
|
||||
|
||||
def __init__(self, money=None, chntext=None):
|
||||
self.money = money
|
||||
self.chntext = chntext
|
||||
|
||||
# def chntext2money(self):
|
||||
# return self.money
|
||||
|
||||
def money2chntext(self):
|
||||
money = self.money
|
||||
pattern = re.compile(r'(\d+(\.\d+)?)')
|
||||
matchers = pattern.findall(money)
|
||||
if matchers:
|
||||
for matcher in matchers:
|
||||
money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext())
|
||||
self.chntext = money
|
||||
return self.chntext
|
||||
|
||||
|
||||
class Percentage:
|
||||
"""
|
||||
PERCENTAGE类
|
||||
"""
|
||||
|
||||
def __init__(self, percentage=None, chntext=None):
|
||||
self.percentage = percentage
|
||||
self.chntext = chntext
|
||||
|
||||
def chntext2percentage(self):
|
||||
return chn2num(self.chntext.strip().strip('百分之')) + '%'
|
||||
|
||||
def percentage2chntext(self):
|
||||
return '百分之' + num2chn(self.percentage.strip().strip('%'))
|
||||
|
||||
|
||||
def remove_erhua(text, er_whitelist):
|
||||
"""
|
||||
去除儿化音词中的儿:
|
||||
他女儿在那边儿 -> 他女儿在那边
|
||||
"""
|
||||
|
||||
er_pattern = re.compile(er_whitelist)
|
||||
new_str=''
|
||||
while re.search('儿',text):
|
||||
a = re.search('儿',text).span()
|
||||
remove_er_flag = 0
|
||||
|
||||
if er_pattern.search(text):
|
||||
b = er_pattern.search(text).span()
|
||||
if b[0] <= a[0]:
|
||||
remove_er_flag = 1
|
||||
|
||||
if remove_er_flag == 0 :
|
||||
new_str = new_str + text[0:a[0]]
|
||||
text = text[a[1]:]
|
||||
else:
|
||||
new_str = new_str + text[0:b[1]]
|
||||
text = text[b[1]:]
|
||||
|
||||
text = new_str + text
|
||||
return text
|
||||
|
||||
# ================================================================================ #
|
||||
# NSW Normalizer
|
||||
# ================================================================================ #
|
||||
class NSWNormalizer:
|
||||
def __init__(self, raw_text):
|
||||
self.raw_text = '^' + raw_text + '$'
|
||||
self.norm_text = ''
|
||||
|
||||
def _particular(self):
|
||||
text = self.norm_text
|
||||
pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
# print('particular')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], matcher[1]+'2'+matcher[2], 1)
|
||||
self.norm_text = text
|
||||
return self.norm_text
|
||||
|
||||
def normalize(self):
|
||||
text = self.raw_text
|
||||
|
||||
# 规范化日期
|
||||
pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('date')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
|
||||
|
||||
# 规范化金钱
|
||||
pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('money')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1)
|
||||
|
||||
# 规范化固话/手机号码
|
||||
# 手机
|
||||
# http://www.jihaoba.com/news/show/13680
|
||||
# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
|
||||
# 联通：130、131、132、156、155、186、185、176
|
||||
# 电信：133、153、189、180、181、177
|
||||
pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('telephone')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1)
|
||||
# 固话
|
||||
pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
# print('fixed telephone')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1)
|
||||
|
||||
# 规范化分数
|
||||
pattern = re.compile(r"(\d+/\d+)")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('fraction')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1)
|
||||
|
||||
# 规范化百分数
|
||||
text = text.replace('％', '%')
|
||||
pattern = re.compile(r"(\d+(\.\d+)?%)")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('percentage')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1)
|
||||
|
||||
# 规范化纯数+量词
|
||||
pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('cardinal+quantifier')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
|
||||
|
||||
# 规范化数字编号
|
||||
pattern = re.compile(r"(\d{4,32})")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('digit')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
|
||||
|
||||
# 规范化纯数
|
||||
pattern = re.compile(r"(\d+(\.\d+)?)")
|
||||
matchers = pattern.findall(text)
|
||||
if matchers:
|
||||
#print('cardinal')
|
||||
for matcher in matchers:
|
||||
text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
|
||||
|
||||
self.norm_text = text
|
||||
self._particular()
|
||||
|
||||
return self.norm_text.lstrip('^').rstrip('$')
|
||||
|
||||
|
||||
def nsw_test_case(raw_text):
|
||||
print('I:' + raw_text)
|
||||
print('O:' + NSWNormalizer(raw_text).normalize())
|
||||
print('')
|
||||
|
||||
|
||||
def nsw_test():
|
||||
nsw_test_case('固话：0595-23865596或23880880。')
|
||||
nsw_test_case('固话：0595-23865596或23880880。')
|
||||
nsw_test_case('手机：+86 19859213959或15659451527。')
|
||||
nsw_test_case('分数：32477/76391。')
|
||||
nsw_test_case('百分数：80.03%。')
|
||||
nsw_test_case('编号：31520181154418。')
|
||||
nsw_test_case('纯数：2983.07克或12345.60米。')
|
||||
nsw_test_case('日期：1999年2月20日或09年3月15号。')
|
||||
nsw_test_case('金钱：12块5，34.5元，20.1万')
|
||||
nsw_test_case('特殊：O2O或B2C。')
|
||||
nsw_test_case('3456万吨')
|
||||
nsw_test_case('2938个')
|
||||
nsw_test_case('938')
|
||||
nsw_test_case('今天吃了115个小笼包231个馒头')
|
||||
nsw_test_case('有62％的概率')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#nsw_test()
|
||||
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument('ifile', help='input filename, assume utf-8 encoding')
|
||||
p.add_argument('ofile', help='output filename')
|
||||
p.add_argument('--to_upper', action='store_true', help='convert to upper case')
|
||||
p.add_argument('--to_lower', action='store_true', help='convert to lower case')
|
||||
p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.")
|
||||
p.add_argument('--remove_fillers', type=bool, default=True, help='remove filler chars such as "呃, 啊"')
|
||||
p.add_argument('--remove_erhua', type=bool, default=True, help='remove erhua chars such as "这儿"')
|
||||
p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines')
|
||||
args = p.parse_args()
|
||||
|
||||
ifile = codecs.open(args.ifile, 'r', 'utf8')
|
||||
ofile = codecs.open(args.ofile, 'w+', 'utf8')
|
||||
|
||||
n = 0
|
||||
for l in ifile:
|
||||
key = ''
|
||||
text = ''
|
||||
if args.has_key:
|
||||
cols = l.split(maxsplit=1)
|
||||
key = cols[0]
|
||||
if len(cols) == 2:
|
||||
text = cols[1].strip()
|
||||
else:
|
||||
text = ''
|
||||
else:
|
||||
text = l.strip()
|
||||
|
||||
# cases
|
||||
if args.to_upper and args.to_lower:
|
||||
sys.stderr.write('text norm: to_upper OR to_lower?')
|
||||
exit(1)
|
||||
if args.to_upper:
|
||||
text = text.upper()
|
||||
if args.to_lower:
|
||||
text = text.lower()
|
||||
|
||||
# Filler chars removal
|
||||
if args.remove_fillers:
|
||||
for ch in FILLER_CHARS:
|
||||
text = text.replace(ch, '')
|
||||
|
||||
if args.remove_erhua:
|
||||
text = remove_erhua(text, ER_WHITELIST)
|
||||
|
||||
# NSW(Non-Standard-Word) normalization
|
||||
text = NSWNormalizer(text).normalize()
|
||||
|
||||
# Punctuations removal
|
||||
old_chars = CHINESE_PUNC_LIST + string.punctuation # includes all CN and EN punctuations
|
||||
new_chars = ' ' * len(old_chars)
|
||||
del_chars = ''
|
||||
text = text.translate(str.maketrans(old_chars, new_chars, del_chars))
|
||||
|
||||
#
|
||||
if args.has_key:
|
||||
ofile.write(key + '\t' + text + '\n')
|
||||
else:
|
||||
ofile.write(text + '\n')
|
||||
|
||||
n += 1
|
||||
if n % args.log_interval == 0:
|
||||
sys.stderr.write("text norm: {} lines done.\n".format(n))
|
||||
|
||||
sys.stderr.write("text norm: {} lines done in total.\n".format(n))
|
||||
|
||||
ifile.close()
|
||||
ofile.close()
|
||||
@ -1,9 +0,0 @@
|
||||
|
||||
cmd="funasr_cli/cli/train_cli.py"
|
||||
|
||||
python $cmd \
|
||||
--config-path "/Users/zhifu/funasr_github/test_local/funasr_cli_egs" \
|
||||
--config-name "config.yaml" \
|
||||
+token_list="/Users/zhifu/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \
|
||||
+train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len.jsonl" \
|
||||
+output_dir="/nfs/zhifu.gzf/ckpt/funasr2/exp1"
|
||||
@ -5,7 +5,14 @@
|
||||
#local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
|
||||
#git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
|
||||
|
||||
|
||||
## generate jsonl from wav.scp and text.txt
|
||||
#python funasr/datasets/audio_datasets/scp2jsonl.py \
|
||||
#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
|
||||
#++data_type_list='["source", "target"]' \
|
||||
#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
|
||||
# torchrun \
|
||||
# --nnodes 1 \
|
||||
# --nproc_per_node 1 \
|
||||
python funasr/bin/train.py \
|
||||
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
|
||||
+model_revision="v2.0.4" \
|
||||
|
||||
123
funasr/bin/compute_audio_cmvn.py
Normal file
123
funasr/bin/compute_audio_cmvn.py
Normal file
@ -0,0 +1,123 @@
|
||||
import os
|
||||
import json
|
||||
import numpy as np
|
||||
import torch
|
||||
import hydra
|
||||
import logging
|
||||
from omegaconf import DictConfig, OmegaConf
|
||||
|
||||
from funasr.register import tables
|
||||
from funasr.download.download_from_hub import download_model
|
||||
from funasr.train_utils.set_all_random_seed import set_all_random_seed
|
||||
|
||||
|
||||
@hydra.main(config_name=None, version_base=None)
|
||||
def main_hydra(kwargs: DictConfig):
|
||||
if kwargs.get("debug", False):
|
||||
import pdb; pdb.set_trace()
|
||||
|
||||
assert "model" in kwargs
|
||||
if "model_conf" not in kwargs:
|
||||
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
|
||||
kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
|
||||
|
||||
|
||||
main(**kwargs)
|
||||
|
||||
|
||||
def main(**kwargs):
|
||||
print(kwargs)
|
||||
# set random seed
|
||||
tables.print()
|
||||
set_all_random_seed(kwargs.get("seed", 0))
|
||||
torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
|
||||
torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
|
||||
torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
|
||||
|
||||
|
||||
|
||||
|
||||
tokenizer = kwargs.get("tokenizer", None)
|
||||
|
||||
# build frontend if frontend is none None
|
||||
frontend = kwargs.get("frontend", None)
|
||||
if frontend is not None:
|
||||
frontend_class = tables.frontend_classes.get(frontend)
|
||||
frontend = frontend_class(**kwargs["frontend_conf"])
|
||||
kwargs["frontend"] = frontend
|
||||
kwargs["input_size"] = frontend.output_size()
|
||||
|
||||
|
||||
|
||||
# dataset
|
||||
dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
|
||||
dataset_train = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=None, is_training=False, **kwargs.get("dataset_conf"))
|
||||
|
||||
# dataloader
|
||||
batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
|
||||
batch_sampler_train = None
|
||||
if batch_sampler is not None:
|
||||
batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
|
||||
dataset_conf = kwargs.get("dataset_conf")
|
||||
dataset_conf["batch_type"] = "example"
|
||||
dataset_conf["batch_size"] = 1
|
||||
batch_sampler_train = batch_sampler_class(dataset_train, is_training=False, **dataset_conf)
|
||||
|
||||
|
||||
dataloader_train = torch.utils.data.DataLoader(dataset_train,
|
||||
collate_fn=dataset_train.collator,
|
||||
batch_sampler=batch_sampler_train,
|
||||
num_workers=int(kwargs.get("dataset_conf").get("num_workers", 4)),
|
||||
pin_memory=True)
|
||||
|
||||
iter_stop = int(kwargs.get("scale", 1.0)*len(dataloader_train))
|
||||
|
||||
total_frames = 0
|
||||
for batch_idx, batch in enumerate(dataloader_train):
|
||||
if batch_idx >= iter_stop:
|
||||
break
|
||||
|
||||
fbank = batch["speech"].numpy()[0, :, :]
|
||||
if total_frames == 0:
|
||||
mean_stats = fbank
|
||||
var_stats = np.square(fbank)
|
||||
else:
|
||||
mean_stats += np.sum(fbank, axis=0)
|
||||
var_stats += np.sum(np.square(fbank), axis=0)
|
||||
total_frames += fbank.shape[0]
|
||||
|
||||
|
||||
cmvn_info = {
|
||||
'mean_stats': list(mean_stats.tolist()),
|
||||
'var_stats': list(var_stats.tolist()),
|
||||
'total_frames': total_frames
|
||||
}
|
||||
cmvn_file = kwargs.get("cmvn_file", "cmvn.json")
|
||||
with open(cmvn_file, 'w') as fout:
|
||||
fout.write(json.dumps(cmvn_info))
|
||||
|
||||
mean = -1.0 * mean_stats / total_frames
|
||||
var = 1.0 / np.sqrt(var_stats / total_frames - mean * mean)
|
||||
dims = mean.shape[0]
|
||||
am_mvn = os.path.dirname(cmvn_file) + "/am.mvn"
|
||||
with open(am_mvn, 'w') as fout:
|
||||
fout.write("<Nnet>" + "\n" + "<Splice> " + str(dims) + " " + str(dims) + '\n' + "[ 0 ]" + "\n" + "<AddShift> " + str(dims) + " " + str(dims) + "\n")
|
||||
mean_str = str(list(mean)).replace(',', '').replace('[', '[ ').replace(']', ' ]')
|
||||
fout.write("<LearnRateCoef> 0 " + mean_str + '\n')
|
||||
fout.write("<Rescale> " + str(dims) + " " + str(dims) + '\n')
|
||||
var_str = str(list(var)).replace(',', '').replace('[', '[ ').replace(']', ' ]')
|
||||
fout.write("<LearnRateCoef> 0 " + var_str + '\n')
|
||||
fout.write("</Nnet>" + '\n')
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_hydra()
|
||||
"""
|
||||
python funasr/bin/compute_status.py \
|
||||
--config-path "/Users/zhifu/funasr1.0/examples/aishell/conf" \
|
||||
--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \
|
||||
++train_data_set_list="/Users/zhifu/funasr1.0/data/list/audio_datasets.jsonl" \
|
||||
++cmvn_file="/Users/zhifu/funasr1.0/data/list/cmvn.json" \
|
||||
++dataset_conf.num_workers=32
|
||||
"""
|
||||
@ -1,3 +1,6 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- encoding: utf-8 -*-
|
||||
|
||||
import os
|
||||
import sys
|
||||
import torch
|
||||
@ -144,9 +147,8 @@ def main(**kwargs):
|
||||
|
||||
# dataset
|
||||
dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
|
||||
dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
|
||||
dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer,
|
||||
**kwargs.get("dataset_conf"))
|
||||
dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=True, **kwargs.get("dataset_conf"))
|
||||
dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=False, **kwargs.get("dataset_conf"))
|
||||
|
||||
# dataloader
|
||||
batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
|
||||
|
||||
@ -19,7 +19,7 @@ class AudioDataset(torch.utils.data.Dataset):
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
index_ds_class = tables.index_ds_classes.get(index_ds)
|
||||
self.index_ds = index_ds_class(path)
|
||||
self.index_ds = index_ds_class(path, **kwargs)
|
||||
preprocessor_speech = kwargs.get("preprocessor_speech", None)
|
||||
if preprocessor_speech:
|
||||
preprocessor_speech_class = tables.preprocessor_speech_classes.get(preprocessor_speech)
|
||||
@ -63,9 +63,14 @@ class AudioDataset(torch.utils.data.Dataset):
|
||||
target = item["target"]
|
||||
if self.preprocessor_text:
|
||||
target = self.preprocessor_text(target)
|
||||
ids = self.tokenizer.encode(target)
|
||||
if self.tokenizer:
|
||||
ids = self.tokenizer.encode(target)
|
||||
text = torch.tensor(ids, dtype=torch.int64)
|
||||
else:
|
||||
ids = target
|
||||
text = ids
|
||||
ids_lengths = len(ids)
|
||||
text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
|
||||
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
|
||||
|
||||
return {"speech": speech[0, :, :],
|
||||
"speech_lengths": speech_lengths,
|
||||
@ -83,11 +88,13 @@ class AudioDataset(torch.utils.data.Dataset):
|
||||
outputs[key].append(sample[key])
|
||||
|
||||
for key, data_list in outputs.items():
|
||||
if data_list[0].dtype == torch.int64:
|
||||
|
||||
pad_value = self.int_pad_value
|
||||
else:
|
||||
pad_value = self.float_pad_value
|
||||
outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
|
||||
if isinstance(data_list[0], torch.Tensor):
|
||||
if data_list[0].dtype == torch.int64:
|
||||
|
||||
pad_value = self.int_pad_value
|
||||
else:
|
||||
pad_value = self.float_pad_value
|
||||
|
||||
outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
|
||||
return outputs
|
||||
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
import os
|
||||
import json
|
||||
import torch
|
||||
import logging
|
||||
import concurrent.futures
|
||||
import librosa
|
||||
import torch.distributed as dist
|
||||
|
||||
from funasr.register import tables
|
||||
@ -71,9 +74,19 @@ class IndexDSJsonlRankSplit(torch.utils.data.Dataset):
|
||||
@tables.register("index_ds_classes", "IndexDSJsonlRankFull")
|
||||
class IndexDSJsonlRankFull(torch.utils.data.Dataset):
|
||||
|
||||
def __init__(self, path):
|
||||
def __init__(self, path: str, **kwargs):
|
||||
super().__init__()
|
||||
|
||||
if isinstance(path, (list, tuple)): # wav.scp, text.txt/text.trans
|
||||
from funasr.datasets.audio_datasets.scp2jsonl import gen_jsonl_from_wav_text_list
|
||||
jsonl_outdir = os.path.dirname(path[0])
|
||||
jsonl_name = "datalist_train.jsonl" if kwargs.get("is_training", True) else "datalist_val.jsonl"
|
||||
jsonl_file_out = os.path.join(jsonl_outdir, jsonl_name)
|
||||
if not os.path.exists(jsonl_file_out):
|
||||
print(f"datalist is: {path}, generate jsonl from it")
|
||||
gen_jsonl_from_wav_text_list(path, jsonl_file_out=jsonl_file_out, **kwargs)
|
||||
path = jsonl_file_out
|
||||
|
||||
contents = []
|
||||
with open(path, encoding='utf-8') as fin:
|
||||
for line in fin:
|
||||
|
||||
94
funasr/datasets/audio_datasets/scp2jsonl.py
Normal file
94
funasr/datasets/audio_datasets/scp2jsonl.py
Normal file
@ -0,0 +1,94 @@
|
||||
import os
|
||||
import json
|
||||
import torch
|
||||
import logging
|
||||
import hydra
|
||||
from omegaconf import DictConfig, OmegaConf
|
||||
import concurrent.futures
|
||||
import librosa
|
||||
import torch.distributed as dist
|
||||
|
||||
|
||||
|
||||
def gen_jsonl_from_wav_text_list(path, data_type_list=("source", "target"), jsonl_file_out:str=None, **kwargs):
|
||||
try:
|
||||
rank = dist.get_rank()
|
||||
world_size = dist.get_world_size()
|
||||
except:
|
||||
rank = 0
|
||||
world_size = 1
|
||||
|
||||
cpu_cores = os.cpu_count() or 1
|
||||
|
||||
if rank == 0:
|
||||
json_dict = {}
|
||||
for data_type, data_file in zip(data_type_list, path):
|
||||
json_dict[data_type] = {}
|
||||
with open(data_file, "r") as f:
|
||||
|
||||
data_file_lists = f.readlines()
|
||||
lines_for_each_th = (len(data_file_lists)-1)//cpu_cores + 1
|
||||
task_num = cpu_cores if len(data_file_lists) > cpu_cores else 1
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=cpu_cores) as executor:
|
||||
|
||||
futures = [executor.submit(parse_context_length, data_file_lists[i*lines_for_each_th:(i+1)*lines_for_each_th], data_type) for i in range(task_num)]
|
||||
|
||||
for future in concurrent.futures.as_completed(futures):
|
||||
|
||||
json_dict[data_type].update(future.result())
|
||||
# print(json_dict)
|
||||
|
||||
with open(jsonl_file_out, "w") as f:
|
||||
for key in json_dict[data_type_list[0]].keys():
|
||||
jsonl_line = {"key": key}
|
||||
for data_file in data_type_list:
|
||||
jsonl_line.update(json_dict[data_file][key])
|
||||
jsonl_line = json.dumps(jsonl_line, ensure_ascii=False)
|
||||
f.write(jsonl_line+"\n")
|
||||
f.flush()
|
||||
|
||||
else:
|
||||
pass
|
||||
|
||||
if world_size > 1:
|
||||
dist.barrier()
|
||||
|
||||
|
||||
def parse_context_length(data_list: list, data_type: str):
|
||||
|
||||
res = {}
|
||||
for i, line in enumerate(data_list):
|
||||
key, line = line.strip().split(maxsplit=1)
|
||||
line = line.strip()
|
||||
if os.path.exists(line):
|
||||
waveform, _ = librosa.load(line, sr=16000)
|
||||
sample_num = len(waveform)
|
||||
context_len = int(sample_num//16000*1000/10)
|
||||
else:
|
||||
context_len = len(line)
|
||||
res[key] = {data_type: line, f"{data_type}_len": context_len}
|
||||
return res
|
||||
|
||||
|
||||
@hydra.main(config_name=None, version_base=None)
|
||||
def main_hydra(cfg: DictConfig):
|
||||
"""
|
||||
python funasr/datasets/audio_datasets/scp2jsonl.py \
|
||||
++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
|
||||
++data_type_list='["source", "target"]' \
|
||||
++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
|
||||
|
||||
"""
|
||||
|
||||
kwargs = OmegaConf.to_container(cfg, resolve=True)
|
||||
|
||||
scp_file_list = kwargs.get("scp_file_list", ("/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"))
|
||||
data_type_list = kwargs.get("data_type_list", ("source", "target"))
|
||||
jsonl_file_out = kwargs.get("jsonl_file_out", "/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl")
|
||||
gen_jsonl_from_wav_text_list(scp_file_list, data_type_list=data_type_list, jsonl_file_out=jsonl_file_out)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_hydra()
|
||||
|
||||
|
||||
11
funasr/download/download_dataset_from_hub.py
Normal file
11
funasr/download/download_dataset_from_hub.py
Normal file
@ -0,0 +1,11 @@
|
||||
|
||||
def download_dataset():
|
||||
pass
|
||||
|
||||
def download_dataset_from_ms(**kwargs):
|
||||
from modelscope.msdatasets import MsDataset
|
||||
dataset_name = kwargs.get("dataset_name", 'speech_asr/speech_asr_aishell1_trainsets')
|
||||
subset_name = kwargs.get("subset_name", 'default')
|
||||
split = kwargs.get("split", 'train')
|
||||
data_dump_dir = kwargs.get("data_dump_dir", None)
|
||||
ds = MsDataset.load(dataset_name=dataset_name, subset_name=subset_name, split=split, cache_dir=data_dump_dir)
|
||||
0
funasr/models/model_hf/__init__.py
Normal file
0
funasr/models/model_hf/__init__.py
Normal file
@ -52,8 +52,8 @@ class RegisterTables:
|
||||
registry = getattr(self, register_tables_key)
|
||||
registry_key = key if key is not None else target_class.__name__
|
||||
|
||||
assert not registry_key in registry, "(key: {} / class: {}) has been registered already，in {}".format(
|
||||
registry_key, target_class, register_tables_key)
|
||||
# assert not registry_key in registry, "(key: {} / class: {}) has been registered already，in {}".format(
|
||||
# registry_key, target_class, register_tables_key)
|
||||
|
||||
registry[registry_key] = target_class
|
||||
|
||||
|
||||
@ -204,25 +204,25 @@ class Trainer:
|
||||
my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
|
||||
with my_context():
|
||||
time2 = time.perf_counter()
|
||||
print("before, GPU, memory: {:.1} MB, "
|
||||
"{:.1} MB, "
|
||||
"{:.1} MB, "
|
||||
"{:.1} MB".format(torch.cuda.memory_allocated()/1024/1024/1024,
|
||||
torch.cuda.max_memory_allocated()/1024/1024/1024,
|
||||
torch.cuda.memory_reserved()/1024/1024/1024,
|
||||
torch.cuda.max_memory_reserved()/1024/1024/1024,
|
||||
))
|
||||
# print("before, GPU, memory: {:.3f} GB, "
|
||||
# "{:.3f} GB, "
|
||||
# "{:.3f} GB, "
|
||||
# "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
|
||||
# torch.cuda.max_memory_allocated()/1024/1024/1024,
|
||||
# torch.cuda.memory_reserved()/1024/1024/1024,
|
||||
# torch.cuda.max_memory_reserved()/1024/1024/1024,
|
||||
# ))
|
||||
|
||||
retval = self.model(**batch)
|
||||
torch.cuda.empty_cache()
|
||||
print("after, GPU, memory: {:.1} MB, "
|
||||
"{:.1} MB, "
|
||||
"{:.1} MB, "
|
||||
"{:.1} MB".format(torch.cuda.memory_allocated()/1024/1024/1024,
|
||||
torch.cuda.max_memory_allocated()/1024/1024/1024,
|
||||
torch.cuda.memory_reserved()/1024/1024/1024,
|
||||
torch.cuda.max_memory_reserved()/1024/1024/1024,
|
||||
))
|
||||
# print("after, GPU, memory: {:.3f} GB, "
|
||||
# "{:.3f} GB, "
|
||||
# "{:.3f} GB, "
|
||||
# "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
|
||||
# torch.cuda.max_memory_allocated()/1024/1024/1024,
|
||||
# torch.cuda.memory_reserved()/1024/1024/1024,
|
||||
# torch.cuda.max_memory_reserved()/1024/1024/1024,
|
||||
# ))
|
||||
time3 = time.perf_counter()
|
||||
speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
|
||||
loss, stats, weight = retval
|
||||
@ -275,12 +275,21 @@ class Trainer:
|
||||
|
||||
pbar.update(1)
|
||||
if self.local_rank == 0:
|
||||
gpu_info = "GPU, memory: {:.3f} GB, " \
|
||||
"{:.3f} GB, "\
|
||||
"{:.3f} GB, "\
|
||||
"{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024,
|
||||
torch.cuda.max_memory_allocated()/1024/1024/1024,
|
||||
torch.cuda.memory_reserved()/1024/1024/1024,
|
||||
torch.cuda.max_memory_reserved()/1024/1024/1024,
|
||||
)
|
||||
description = (
|
||||
f"Train epoch: {epoch}/{self.max_epoch}, "
|
||||
f"step {batch_idx}/{len(self.dataloader_train)}, "
|
||||
f"{speed_stats}, "
|
||||
f"(loss: {loss.detach().cpu().item():.3f}), "
|
||||
f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
|
||||
f"{gpu_info}"
|
||||
)
|
||||
pbar.set_description(description)
|
||||
if self.writer:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user