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#!/bin/bash
# This script is an example of training ZipVoice-Dialog on OpenDialog dataset.
# Add project root to PYTHONPATH
export PYTHONPATH=../../:$PYTHONPATH
# Set bash to 'debug' mode, it will exit on:
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail
stage=1
stop_stage=6
# Number of jobs for data preparation
nj=20
# We assume that you have downloaded the OpenDialog dataset
# to download/OpenDialog and untarred the tar files in audio/en
# and audio/zh so that the mp3 files are placed under these two directories.
# Download OpenDialog at https://huggingface.co/datasets/k2-fsa/OpenDialog
# or https://www.modelscope.cn/datasets/k2-fsa/OpenDialog
data_dir=download/OpenDialog
download_dir=download/
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Stage 1: Prepare manifests for OpenDialog dataset"
python3 local/prepare_opendialog.py \
--dataset-path ${data_dir} \
--num-jobs ${nj} \
--output-dir data/manifests
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Stage 2: Add tokens to manifests"
for subset in ZH-dev ZH-train EN-dev EN-train;do
python3 -m zipvoice.bin.prepare_tokens \
--input-file data/manifests/opendialog_cuts_raw_${subset}.jsonl.gz \
--output-file data/manifests/opendialog_cuts_${subset}.jsonl.gz \
--tokenizer dialog
done
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
echo "Stage 3: Compute Fbank for opendialog dataset"
# You can skip this step and use `--on-the-fly-feats 1` in training stage
for subset in ZH-dev ZH-train EN-dev EN-train;do
python3 -m zipvoice.bin.compute_fbank \
--source-dir data/manifests \
--dest-dir data/fbank \
--dataset opendialog \
--subset ${subset} \
--num-jobs ${nj}
done
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "Stage 4: Download tokens file, pretrained models"
# Uncomment this line to use HF mirror
# export HF_ENDPOINT=https://hf-mirror.com
# The token file is obtained by extending some tokens
# on the bases of the Emilia token file.
mkdir -p ${download_dir}
hf_repo=k2-fsa/ZipVoice
huggingface-cli download \
--local-dir ${download_dir} \
${hf_repo} \
zipvoice_dialog/tokens.txt
# Pre-trained ZipVoice model is required as
# the initialization model.
for file in model.pt tokens.txt model.json; do
huggingface-cli download \
--local-dir ${download_dir} \
${hf_repo} \
zipvoice/${file}
done
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
echo "Stage 5: Train the ZipVoice-Dialog model"
python3 -m zipvoice.bin.train_zipvoice_dialog \
--world-size 8 \
--use-fp16 1 \
--base-lr 0.0001 \
--max-duration 500 \
--checkpoint ${download_dir}/zipvoice/model.pt \
--model-config ${download_dir}/zipvoice/model.json \
--token-file ${download_dir}/zipvoice_dialog/tokens.txt \
--dataset opendialog \
--manifest-dir data/fbank \
--exp-dir exp/zipvoice_dialog_opendialog
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "Stage 6: Average the checkpoints for ZipVoice"
python3 -m zipvoice.bin.generate_averaged_model \
--iter 60000 \
--avg 2 \
--model-name zipvoice_dialog \
--exp-dir exp/zipvoice_dialog_opendialog
# The generated model is exp/zipvoice_dialog_opendialog/iter-60000-avg-2.pt
fi
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
echo "Stage 7: Inference of the ZipVoice model"
python3 -m zipvoice.bin.infer_zipvoice_dialog \
--model-name zipvoice_dialog \
--model-dir exp/zipvoice_dialog_opendialog \
--checkpoint-name iter-60000-avg-2.pt \
--test-list test.tsv \
--res-dir results/test_dialog
fi |