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68 lines
2.7 KiB
68 lines
2.7 KiB
#!/bin/bash
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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. ./path.sh
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set -e
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#######################################################################
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# stage 0: data prepare, including voxceleb1 download and generate {train,dev,enroll,test}.csv
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# voxceleb2 data is m4a format, so we need convert the m4a to wav yourselves with the script local/convert.sh
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# stage 1: train the speaker identification model
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# stage 2: test speaker identification
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# stage 3: (todo)extract the training embeding to train the LDA and PLDA
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######################################################################
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# the vox2 dataset is stored in m4a format, we need to convert the audio from m4a to wav yourself
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# and put all of them to ${MAIN_ROOT}/datasets/vox2
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# we will find the wav from ${MAIN_ROOT}/datasets/vox1/{dev,test}/wav and ${MAIN_ROOT}/datasets/vox2/wav
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stage=0
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stop_stage=50
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# data directory
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# if we set the variable ${dir}, we will store the wav info to this directory
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# otherwise, we will store the wav info to data/vox1 and data/vox2 directory respectively
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# vox2 wav path, we must convert the m4a format to wav format
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dir=data/ # data info directory
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exp_dir=exp/ecapa-tdnn-vox12-big/ # experiment directory
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conf_path=conf/ecapa_tdnn.yaml
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gpus=0,1,2,3
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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mkdir -p ${exp_dir}
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if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# stage 0: data prepare for vox1 and vox2, vox2 must be converted from m4a to wav
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bash ./local/data.sh ${dir} ${conf_path}|| exit -1;
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fi
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if [ $stage -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# stage 1: train the speaker identification model
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CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${dir} ${exp_dir} ${conf_path}
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fi
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if [ $stage -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# stage 2: get the speaker verification scores with cosine function
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# now we only support use cosine to get the scores
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CUDA_VISIBLE_DEVICES=0 bash ./local/test.sh ${dir} ${exp_dir} ${conf_path}
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fi
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# if [ $stage -le 3 ]; then
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# # stage 3: extract the training embeding to train the LDA and PLDA
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# # todo: extract the training embedding
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# fi
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