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#!/bin/bash
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. ./path.sh || exit 1;
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set -e
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stage=1
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stop_stage=50
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#TARGET_DIR=${MAIN_ROOT}/dataset/ami
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TARGET_DIR=/home/dataset/AMI
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data_folder=${TARGET_DIR}/amicorpus #e.g., /path/to/amicorpus/
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manual_annot_folder=${TARGET_DIR}/ami_public_manual_1.6.2 #e.g., /path/to/ami_public_manual_1.6.2/
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save_folder=./save
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pretraind_model_dir=${save_folder}/model
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conf_path=conf/ecapa_tdnn.yaml
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. ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# Prepare data and model
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# Download AMI corpus, You need around 10GB of free space to get whole data
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# The signals are too large to package in this way,
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# so you need to use the chooser to indicate which ones you wish to download
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echo "Please follow https://groups.inf.ed.ac.uk/ami/download/ to download the data."
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echo "Annotations: AMI manual annotations v1.6.2 "
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echo "Signals: "
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echo "1) Select one or more AMI meetings: the IDs please follow ./ami_split.py"
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echo "2) Select media streams: Just select Headset mix"
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# Download the pretrained Model from HuggingFace or other pretrained model
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echo "Please download the pretrained ECAPA-TDNN Model and put the pretrainde model in given path: "${pretraind_model_dir}
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fi
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if [ $stage -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# Tune hyperparams on dev set and perform final diarization on dev and eval with best hyperparams.
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bash ./local/process.sh ${data_folder} ${manual_annot_folder} ${save_folder} ${pretraind_model_dir} ${conf_path} || exit 1
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fi
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