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https://github.com/k2-fsa/icefall.git
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915e8e399c
- Added CHiME-4 dataset integration in asr_datamodule.py - Added Hugging Face upload script - Added RIR augmentation - Added Self-Distillation Training
97 lines
3.9 KiB
Bash
97 lines
3.9 KiB
Bash
#!/bin/# Data Augmentation Controls (modify these as needed)
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enable_spec_aug=true # SpecAugment (frequency/time masking)
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enable_musan=true # MUSAN noise augmentation
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enable_rir=true # RIR (Room Impulse Response) augmentation - FIXED AND RE-ENABLED
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enable_cutmix=true # Cut mixing: 두 오디오의 시간 구간을 섞음
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enable_concatenate=true # Cut concatenation: 짧은 발화들을 연결하여 패딩 최소화
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# train.sh - LibriSpeech ASR Training Script with Data Augmentation Control
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# Usage: bash train.sh
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set -euo pipefail
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# Data Augmentation Controls (modify these as needed)
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enable_spec_aug=true # SpecAugment (frequency/time masking)
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enable_musan=true # MUSAN noise augmentation
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enable_rir=true # RIR (Room Impulse Response) augmentation - RE-ENABLED
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enable_cutmix=true # Cut mixing: 두 오디오의 시간 구간을 섞음
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enable_concatenate=true # Cut concatenation: 짧은 발화들을 연결하여 패딩 최소화
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# RIR settings (used when enable_rir=true)
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rir_cuts_path="data/manifests/rir.scp" # Path to RIR file list (updated to use rir.scp)
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rir_prob=0.5 # Probability of applying RIR
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# Training parameters
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world_size=4 # Multi-GPU restored since test passed
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max_duration=300 # Further reduced from 320 to save memory
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valid_max_duration=15 # Very small for multi-GPU safety
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num_buckets=200 # Reduced for memory saving
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num_workers=24 # Much smaller to save memory
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warm_step=40000
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lang_dir="./data/lang_bpe_5000"
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method="ctc-decoding"
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# Model parameters
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att_rate=0 # 0 for pure CTC, >0 for CTC+Attention
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num_decoder_layers=0 # 0 for pure CTC
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# Other settings
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start_epoch=0
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master_port=12345
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sanity_check=false # Set to true for OOM checking (slower)
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# Validation settings
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enable_validation=true # Set to false to disable validation completely
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valid_interval=5000 # Increased from 50 to allow more training before validation
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# Validation decoding settings
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validation_decoding_method="greedy" # "greedy" or "beam" - use greedy for faster validation
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validation_search_beam=10.0 # Beam size for validation (only used if method="beam")
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validation_output_beam=5.0 # Output beam for validation (only used if method="beam")
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validation_skip_wer=false # Skip WER computation for even faster validation (디버깅용 - 이제 false로 변경)
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if [ "$enable_rir" = "true" ]; then
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echo " - RIR Path: $rir_cuts_path"
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echo " - RIR Probability: $rir_prob"
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fi
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# gdb --args python ./conformer_ctc/train.py
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if [ -z "${PYTHONPATH:-}" ]; then
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export PYTHONPATH="/tmp/icefall"
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else
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export PYTHONPATH="${PYTHONPATH}:/tmp/icefall"
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fi
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python3 ./conformer_ctc/train.py \
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--master-port $master_port \
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--sanity-check $sanity_check \
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--world-size $world_size \
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--warm-step $warm_step \
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--start-epoch $start_epoch \
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--att-rate $att_rate \
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--num-decoder-layers $num_decoder_layers \
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--num-workers $num_workers \
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--enable-spec-aug $enable_spec_aug \
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--enable-musan $enable_musan \
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--enable-rir $enable_rir \
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--rir-cuts-path $rir_cuts_path \
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--rir-prob $rir_prob \
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--on-the-fly-feats true \
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--max-duration $max_duration \
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--valid-max-duration $valid_max_duration \
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--num-buckets $num_buckets \
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--bucketing-sampler true \
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--concatenate-cuts $enable_concatenate \
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--duration-factor 1.0 \
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--drop-last true \
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--shuffle true \
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--lang-dir $lang_dir \
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--method $method \
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--enable-validation $enable_validation \
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--valid-interval $valid_interval \
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--validation-decoding-method $validation_decoding_method \
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--validation-search-beam $validation_search_beam \
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--validation-output-beam $validation_output_beam \
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--validation-skip-wer $validation_skip_wer
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