mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
hnluo
GitHub
commit
6f2f91ccd1
@ -0,0 +1,6 @@
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beam_size: 10
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penalty: 0.0
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maxlenratio: 0.0
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minlenratio: 0.0
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ctc_weight: 0.5
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lm_weight: 0.7
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80
egs/librispeech/conformer/conf/train_asr_conformer.yaml
Normal file
80
egs/librispeech/conformer/conf/train_asr_conformer.yaml
Normal file
@ -0,0 +1,80 @@
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encoder: conformer
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encoder_conf:
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output_size: 512
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attention_heads: 8
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linear_units: 2048
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num_blocks: 12
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.1
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input_layer: conv2d
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normalize_before: true
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macaron_style: true
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rel_pos_type: latest
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pos_enc_layer_type: rel_pos
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selfattention_layer_type: rel_selfattn
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activation_type: swish
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use_cnn_module: true
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cnn_module_kernel: 31
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decoder: transformer
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decoder_conf:
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attention_heads: 8
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.1
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src_attention_dropout_rate: 0.1
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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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length_normalized_loss: false
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accum_grad: 2
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max_epoch: 50
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patience: none
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init: none
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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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optim: adam
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optim_conf:
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lr: 0.0025
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weight_decay: 0.000001
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 40000
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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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time_warp_mode: bicubic
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apply_freq_mask: true
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freq_mask_width_range:
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- 0
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- 27
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num_freq_mask: 2
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apply_time_mask: true
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time_mask_width_ratio_range:
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- 0.
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- 0.05
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num_time_mask: 10
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dataset_conf:
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shuffle: True
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shuffle_conf:
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shuffle_size: 1024
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sort_size: 500
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batch_conf:
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batch_type: token
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batch_size: 10000
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num_workers: 8
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log_interval: 50
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normalize: None
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@ -0,0 +1,80 @@
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encoder: conformer
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encoder_conf:
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output_size: 512
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attention_heads: 8
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linear_units: 2048
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num_blocks: 12
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.1
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input_layer: conv2d
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normalize_before: true
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macaron_style: true
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rel_pos_type: latest
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pos_enc_layer_type: rel_pos
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selfattention_layer_type: rel_selfattn
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activation_type: swish
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use_cnn_module: true
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cnn_module_kernel: 31
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decoder: transformer
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decoder_conf:
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attention_heads: 8
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.1
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src_attention_dropout_rate: 0.1
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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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length_normalized_loss: false
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accum_grad: 2
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max_epoch: 50
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patience: none
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init: none
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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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optim: adam
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optim_conf:
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lr: 0.0025
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weight_decay: 0.000001
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 40000
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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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time_warp_mode: bicubic
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apply_freq_mask: true
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freq_mask_width_range:
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- 0
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- 27
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num_freq_mask: 2
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apply_time_mask: true
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time_mask_width_ratio_range:
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- 0.
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- 0.05
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num_time_mask: 10
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dataset_conf:
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shuffle: True
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shuffle_conf:
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shuffle_size: 1024
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sort_size: 500
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batch_conf:
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batch_type: token
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batch_size: 10000
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num_workers: 8
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log_interval: 50
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normalize: utterance_mvn
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58
egs/librispeech/conformer/local/data_prep_librispeech.sh
Executable file
58
egs/librispeech/conformer/local/data_prep_librispeech.sh
Executable file
@ -0,0 +1,58 @@
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#!/usr/bin/env bash
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# Copyright 2014 Vassil Panayotov
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# 2014 Johns Hopkins University (author: Daniel Povey)
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# Apache 2.0
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if [ "$#" -ne 2 ]; then
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echo "Usage: $0 <src-dir> <dst-dir>"
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echo "e.g.: $0 /export/a15/vpanayotov/data/LibriSpeech/dev-clean data/dev-clean"
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exit 1
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fi
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src=$1
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dst=$2
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# all utterances are FLAC compressed
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if ! which flac >&/dev/null; then
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echo "Please install 'flac' on ALL worker nodes!"
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exit 1
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fi
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spk_file=$src/../SPEAKERS.TXT
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mkdir -p $dst || exit 1
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[ ! -d $src ] && echo "$0: no such directory $src" && exit 1
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[ ! -f $spk_file ] && echo "$0: expected file $spk_file to exist" && exit 1
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wav_scp=$dst/wav.scp; [[ -f "$wav_scp" ]] && rm $wav_scp
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trans=$dst/text; [[ -f "$trans" ]] && rm $trans
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for reader_dir in $(find -L $src -mindepth 1 -maxdepth 1 -type d | sort); do
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reader=$(basename $reader_dir)
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if ! [ $reader -eq $reader ]; then # not integer.
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echo "$0: unexpected subdirectory name $reader"
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exit 1
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fi
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for chapter_dir in $(find -L $reader_dir/ -mindepth 1 -maxdepth 1 -type d | sort); do
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chapter=$(basename $chapter_dir)
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if ! [ "$chapter" -eq "$chapter" ]; then
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echo "$0: unexpected chapter-subdirectory name $chapter"
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exit 1
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fi
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find -L $chapter_dir/ -iname "*.flac" | sort | xargs -I% basename % .flac | \
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awk -v "dir=$chapter_dir" '{printf "%s %s/%s.flac \n", $0, dir, $0}' >>$wav_scp|| exit 1
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chapter_trans=$chapter_dir/${reader}-${chapter}.trans.txt
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[ ! -f $chapter_trans ] && echo "$0: expected file $chapter_trans to exist" && exit 1
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cat $chapter_trans >>$trans
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done
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done
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echo "$0: successfully prepared data in $dst"
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exit 0
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5
egs/librispeech/conformer/path.sh
Executable file
5
egs/librispeech/conformer/path.sh
Executable file
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export FUNASR_DIR=$PWD/../../..
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# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
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export PYTHONIOENCODING=UTF-8
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export PATH=$FUNASR_DIR/funasr/bin:$PATH
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262
egs/librispeech/conformer/run.sh
Executable file
262
egs/librispeech/conformer/run.sh
Executable file
@ -0,0 +1,262 @@
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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="0,1,2,3,4,5,6,7"
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gpu_num=8
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count=1
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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=5
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train_cmd=utils/run.pl
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infer_cmd=utils/run.pl
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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=en
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dumpdir=dump/fbank
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feats_type=fbank
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token_type=bpe
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dataset_type=large
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scp=feats.scp
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type=kaldi_ark
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stage=3
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stop_stage=4
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# feature configuration
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feats_dim=80
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sample_frequency=16000
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nj=100
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speed_perturb="0.9,1.0,1.1"
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# data
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data_librispeech=
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# bpe model
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nbpe=5000
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bpemode=unigram
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# exp tag
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tag=""
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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_960
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valid_set=dev
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test_sets="test_clean test_other dev_clean dev_other"
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asr_config=conf/train_asr_conformer.yaml
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#asr_config=conf/train_asr_conformer_uttnorm.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_transformer.yaml
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#inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml
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inference_asr_model=valid.acc.ave_10best.pth
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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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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 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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for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
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local/data_prep_librispeech.sh ${data_librispeech}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
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done
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fi
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feat_train_dir=${feats_dir}/${dumpdir}/$train_set; mkdir -p ${feat_train_dir}
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feat_dev_clean_dir=${feats_dir}/${dumpdir}/dev_clean; mkdir -p ${feat_dev_clean_dir}
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feat_dev_other_dir=${feats_dir}/${dumpdir}/dev_other; mkdir -p ${feat_dev_other_dir}
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feat_test_clean_dir=${feats_dir}/${dumpdir}/test_clean; mkdir -p ${feat_test_clean_dir}
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feat_test_other_dir=${feats_dir}/${dumpdir}/test_other; mkdir -p ${feat_test_other_dir}
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feat_dev_dir=${feats_dir}/${dumpdir}/$valid_set; mkdir -p ${feat_dev_dir}
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature Generation"
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# compute fbank features
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fbankdir=${feats_dir}/fbank
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for x in dev_clean dev_other test_clean test_other; do
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utils/compute_fbank.sh --cmd "$train_cmd" --nj 1 --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
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${feats_dir}/data/${x} ${exp_dir}/exp/make_fbank/${x} ${fbankdir}/${x}
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utils/fix_data_feat.sh ${fbankdir}/${x}
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done
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|
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mkdir ${feats_dir}/data/$train_set
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train_sets="train_clean_100 train_clean_360 train_other_500"
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for file in wav.scp text; do
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( for f in $train_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$train_set/$file || exit 1;
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done
|
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
|
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${feats_dir}/data/$train_set ${exp_dir}/exp/make_fbank/$train_set ${fbankdir}/$train_set
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utils/fix_data_feat.sh ${fbankdir}/$train_set
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|
||||
# compute global cmvn
|
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utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
|
||||
${fbankdir}/$train_set ${exp_dir}/exp/make_fbank/$train_set
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|
||||
# apply cmvn
|
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
|
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${fbankdir}/$train_set ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/$train_set ${feat_train_dir}
|
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
|
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${fbankdir}/dev_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_clean ${feat_dev_clean_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1\
|
||||
${fbankdir}/dev_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_other ${feat_dev_other_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
|
||||
${fbankdir}/test_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_clean ${feat_test_clean_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
|
||||
${fbankdir}/test_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_other ${feat_test_other_dir}
|
||||
|
||||
cp ${fbankdir}/$train_set/text ${fbankdir}/$train_set/speech_shape ${fbankdir}/$train_set/text_shape ${feat_train_dir}
|
||||
cp ${fbankdir}/dev_clean/text ${fbankdir}/dev_clean/speech_shape ${fbankdir}/dev_clean/text_shape ${feat_dev_clean_dir}
|
||||
cp ${fbankdir}/dev_other/text ${fbankdir}/dev_other/speech_shape ${fbankdir}/dev_other/text_shape ${feat_dev_other_dir}
|
||||
cp ${fbankdir}/test_clean/text ${fbankdir}/test_clean/speech_shape ${fbankdir}/test_clean/text_shape ${feat_test_clean_dir}
|
||||
cp ${fbankdir}/test_other/text ${fbankdir}/test_other/speech_shape ${fbankdir}/test_other/text_shape ${feat_test_other_dir}
|
||||
|
||||
dev_sets="dev_clean dev_other"
|
||||
for file in feats.scp text speech_shape text_shape; do
|
||||
( for f in $dev_sets; do cat $feats_dir/${dumpdir}/$f/$file; done ) | sort -k1 > $feat_dev_dir/$file || exit 1;
|
||||
done
|
||||
|
||||
#generate ark list
|
||||
utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/${train_set} ${feat_train_dir}
|
||||
utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/${valid_set} ${feat_dev_dir}
|
||||
fi
|
||||
|
||||
dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
|
||||
bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
|
||||
echo "dictionary: ${dict}"
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
### Task dependent. You have to check non-linguistic symbols used in the corpus.
|
||||
echo "stage 2: Dictionary and Json Data Preparation"
|
||||
mkdir -p ${feats_dir}/data/lang_char/
|
||||
echo "<blank>" > ${dict}
|
||||
echo "<s>" >> ${dict}
|
||||
echo "</s>" >> ${dict}
|
||||
cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
|
||||
spm_train --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
|
||||
spm_encode --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
|
||||
echo "<unk>" >> ${dict}
|
||||
wc -l ${dict}
|
||||
|
||||
vocab_size=$(cat ${dict} | wc -l)
|
||||
awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
|
||||
awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
|
||||
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/$train_set
|
||||
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/$valid_set
|
||||
cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/$train_set
|
||||
cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/$valid_set
|
||||
fi
|
||||
|
||||
|
||||
# Training Stage
|
||||
world_size=$gpu_num # run on one machine
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
echo "stage 3: Training"
|
||||
mkdir -p ${exp_dir}/exp/${model_dir}
|
||||
mkdir -p ${exp_dir}/exp/${model_dir}/log
|
||||
INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
|
||||
if [ -f $INIT_FILE ];then
|
||||
rm -f $INIT_FILE
|
||||
fi
|
||||
init_method=file://$(readlink -f $INIT_FILE)
|
||||
echo "$0: init method is $init_method"
|
||||
for ((i = 0; i < $gpu_num; ++i)); do
|
||||
{
|
||||
rank=$i
|
||||
local_rank=$i
|
||||
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
|
||||
asr_train.py \
|
||||
--gpu_id $gpu_id \
|
||||
--use_preprocessor true \
|
||||
--split_with_space false \
|
||||
--bpemodel ${bpemodel}.model \
|
||||
--token_type $token_type \
|
||||
--dataset_type $dataset_type \
|
||||
--token_list $dict \
|
||||
--train_data_file $feats_dir/$dumpdir/${train_set}/ark_txt.scp \
|
||||
--valid_data_file $feats_dir/$dumpdir/${valid_set}/ark_txt.scp \
|
||||
--resume true \
|
||||
--output_dir ${exp_dir}/exp/${model_dir} \
|
||||
--config $asr_config \
|
||||
--input_size $feats_dim \
|
||||
--ngpu $gpu_num \
|
||||
--num_worker_count $count \
|
||||
--multiprocessing_distributed true \
|
||||
--dist_init_method $init_method \
|
||||
--dist_world_size $world_size \
|
||||
--dist_rank $rank \
|
||||
--local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
|
||||
} &
|
||||
done
|
||||
wait
|
||||
fi
|
||||
|
||||
# Testing Stage
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
echo "stage 4: 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}/${dumpdir}/${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}" \
|
||||
--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 asr \
|
||||
${_opts}
|
||||
|
||||
for f in token token_int score text; do
|
||||
if [ -f "${_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/compute_wer.py ${_data}/text ${_dir}/text ${_dir}/text.cer
|
||||
tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
|
||||
cat ${_dir}/text.cer.txt
|
||||
done
|
||||
fi
|
||||
1
egs/librispeech/conformer/utils
Symbolic link
1
egs/librispeech/conformer/utils
Symbolic link
@ -0,0 +1 @@
|
||||
../../aishell/transformer/utils
|
||||
@ -37,7 +37,7 @@ def tokenize(data,
|
||||
vad = -2
|
||||
|
||||
if bpe_tokenizer is not None:
|
||||
text = bpe_tokenizer.text2tokens(text)
|
||||
text = bpe_tokenizer.text2tokens("".join(text))
|
||||
|
||||
if seg_dict is not None:
|
||||
assert isinstance(seg_dict, dict)
|
||||
|
||||
@ -1355,15 +1355,15 @@ class AbsTask(ABC):
|
||||
from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
|
||||
train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
|
||||
frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
|
||||
seg_dict_file=args.seg_dict_file if hasattr(args,
|
||||
"seg_dict_file") else None,
|
||||
seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
|
||||
punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
|
||||
bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
|
||||
mode="train")
|
||||
valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
|
||||
frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
|
||||
seg_dict_file=args.seg_dict_file if hasattr(args,
|
||||
"seg_dict_file") else None,
|
||||
seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
|
||||
punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
|
||||
bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
|
||||
mode="eval")
|
||||
elif args.dataset_type == "small":
|
||||
train_iter_factory = cls.build_iter_factory(
|
||||
|
||||
@ -579,9 +579,10 @@ class Trainer:
|
||||
reporter.measure_iter_time(iterator, "iter_time"), 1
|
||||
):
|
||||
assert isinstance(batch, dict), type(batch)
|
||||
|
||||
|
||||
if rank == 0 and hasattr(model.module, "num_updates"):
|
||||
num_batch_updates = model.module.get_num_updates()
|
||||
if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")):
|
||||
num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else model.module.get_num_updates()
|
||||
if (num_batch_updates%batch_interval == 0) and (options.oss_bucket is not None) and options.use_pai:
|
||||
buffer = BytesIO()
|
||||
torch.save(model.state_dict(), buffer)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user