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| # Copyright 2024 Alibaba Inc. All Rights Reserved. | |
| data_url=www.openslr.org/resources/60 | |
| data_dir=data | |
| pretrained_model_dir=./pretrained_models/CosyVoice2-0.5B | |
| # if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then | |
| # echo "Data Download" | |
| # for part in test-clean; do | |
| # local/download_and_untar.sh ${data_dir} ${data_url} ${part} | |
| # done | |
| # fi | |
| # if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then | |
| # echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt" | |
| # for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do | |
| # mkdir -p data/$x | |
| # python local/prepare_data.py --src_dir $data_dir/LibriTTS/$x --des_dir data/$x | |
| # done | |
| # fi | |
| # if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then | |
| # echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir" | |
| # for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do | |
| # tools/extract_embedding.py --dir data/$x \ | |
| # --onnx_path $pretrained_model_dir/campplus.onnx | |
| # done | |
| # fi | |
| # if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then | |
| # echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" | |
| # for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do | |
| # tools/extract_speech_token.py --dir data/$x \ | |
| # --onnx_path $pretrained_model_dir/speech_tokenizer_v2.onnx | |
| # done | |
| # fi | |
| # if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then | |
| # echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt" | |
| # for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do | |
| # mkdir -p data/$x/parquet | |
| # tools/make_parquet_list.py --num_utts_per_parquet 1000 \ | |
| # --num_processes 10 \ | |
| # --src_dir data/$x \ | |
| # --des_dir data/$x/parquet | |
| # done | |
| # fi | |
| # train llm | |
| export CUDA_VISIBLE_DEVICES="0" | |
| num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') | |
| job_id=1986 | |
| dist_backend="nccl" | |
| num_workers=2 | |
| prefetch=100 | |
| train_engine=torch_ddp | |
| model=flow | |
| torchrun --nnodes=1 --nproc_per_node=$num_gpus --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \ | |
| train.py \ | |
| --train_engine $train_engine \ | |
| --config config.yaml \ | |
| --train_data data/data.list \ | |
| --cv_data data/data.list \ | |
| --qwen_pretrain_path $pretrained_model_dir/CosyVoice-BlankEN \ | |
| --model $model \ | |
| --model_dir /data/checkpoint/$model/ \ | |
| --num_workers ${num_workers} \ | |
| --prefetch ${prefetch} \ | |
| --pin_memory \ | |
| --use_amp \ | |
| --checkpoint /data/checkpoint/flow/epoch_88_step_14001.pt | |
| # # average model | |
| # average_num=5 | |
| # if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then | |
| # for model in llm flow hifigan; do | |
| # decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt | |
| # echo "do model average and final checkpoint is $decode_checkpoint" | |
| # python cosyvoice/bin/average_model.py \ | |
| # --dst_model $decode_checkpoint \ | |
| # --src_path `pwd`/exp/cosyvoice/$model/$train_engine \ | |
| # --num ${average_num} \ | |
| # --val_best | |
| # done | |
| # fi | |
| # if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then | |
| # echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" | |
| # python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir | |
| # python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir | |
| # fi |