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* init sre recipe * [examples] add sre recipe * Update README.md * [examples] add sre recipe, delete used files * merge from master * update specify sample number each epoch * remove trailing whitespace * fix the repeat read dataset problem in the evaluation process and update results * fix the repeat read dataset problem in the evaluation process and update results * update as hongji mentioned * Update README.md * Update sre recipe README.md * Update recipe part in README.md
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| ## Results for SRE16 | ||
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| * Setup: fbank40, num_frms200, epoch150, Softmax, aug_prob0.6 | ||
| * Scoring: cosine & PLDA & PLDA Adaptation | ||
| * Metric: EER(%) | ||
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| Without PLDA training data augmentation: | ||
| | Model | Params | Backend | Pooled | Tagalog | Cantonese | | ||
| |:------|:------:|:------------:|:------------:|:------------:|:------------:| | ||
| | ResNet34-TSTP-emb256 | 6.63M | Cosine | 15.4 | 19.82 | 10.39 | | ||
| | | | PLDA | 9.36 | 14.26 | 4.513 | | ||
| | | | Adapt PLDA | 6.608 | 10.01 | 2.974 | | ||
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| With PLDA training data augmentation: | ||
| | Model | Params | Backend | Pooled | Tagalog | Cantonese | | ||
| |:------|:------:|:------------:|:------------:|:------------:|:------------:| | ||
| | ResNet34-TSTP-emb256 | 6.63M | Cosine | 15.4 | 19.82 | 10.39 | | ||
| | | | PLDA | 8.944 | 13.54 | 4.462 | | ||
| | | | Adapt PLDA | 6.543 | 9.666 | 3.254 | | ||
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| * 🔥 UPDATE 2023.07.14: Support the [NIST SRE16 recipe](https://www.nist.gov/itl/iad/mig/speaker-recognition-evaluation-2016), see [#177](https://github.com/wenet-e2e/wespeaker/pull/177). |
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| ### train configuration | ||
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| exp_dir: exp/ResNet34-TSTP-emb256-fbank40-num_frms200-aug0.6-spFalse-saFalse-Softmax-SGD-epoch150 | ||
| gpus: "[0,1]" | ||
| num_avg: 10 | ||
| enable_amp: False # whether enable automatic mixed precision training | ||
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| seed: 42 | ||
| num_epochs: 150 | ||
| save_epoch_interval: 5 # save model every 5 epochs | ||
| log_batch_interval: 100 # log every 100 batchs | ||
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| dataloader_args: | ||
| batch_size: 256 | ||
| num_workers: 16 | ||
| pin_memory: False | ||
| prefetch_factor: 8 | ||
| drop_last: True | ||
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| dataset_args: | ||
| # the sample number which will be traversed within one epoch, if the value equals to 0, | ||
| # the utterance number in the dataset will be used as the sample_num_per_epoch. | ||
| sample_num_per_epoch: 780000 | ||
| shuffle: True | ||
| shuffle_args: | ||
| shuffle_size: 1500 | ||
| filter: True | ||
| filter_args: | ||
| min_num_frames: 100 | ||
| max_num_frames: 300 | ||
| resample_rate: 8000 | ||
| speed_perturb: False | ||
| num_frms: 200 | ||
| aug_prob: 0.6 # prob to add reverb & noise aug per sample | ||
| fbank_args: | ||
| num_mel_bins: 40 | ||
| frame_shift: 10 | ||
| frame_length: 25 | ||
| dither: 1.0 | ||
| spec_aug: False | ||
| spec_aug_args: | ||
| num_t_mask: 1 | ||
| num_f_mask: 1 | ||
| max_t: 10 | ||
| max_f: 8 | ||
| prob: 0.6 | ||
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| model: ResNet34 # ResNet18, ResNet34, ResNet50, ResNet101, ResNet152 | ||
| model_init: null | ||
| model_args: | ||
| feat_dim: 40 | ||
| embed_dim: 256 | ||
| pooling_func: "TSTP" # TSTP, ASTP, MQMHASTP | ||
| two_emb_layer: False | ||
| projection_args: | ||
| project_type: "softmax" # add_margin, arc_margin, sphere, softmax, arc_margin_intertopk_subcenter | ||
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| margin_scheduler: MarginScheduler | ||
| margin_update: | ||
| initial_margin: 0.0 | ||
| final_margin: 0.2 | ||
| increase_start_epoch: 20 | ||
| fix_start_epoch: 40 | ||
| update_margin: True | ||
| increase_type: "exp" # exp, linear | ||
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| loss: CrossEntropyLoss | ||
| loss_args: {} | ||
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| optimizer: SGD | ||
| optimizer_args: | ||
| momentum: 0.9 | ||
| nesterov: True | ||
| weight_decay: 0.0001 | ||
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| scheduler: ExponentialDecrease | ||
| scheduler_args: | ||
| initial_lr: 0.1 | ||
| final_lr: 0.00005 | ||
| warm_up_epoch: 6 | ||
| warm_from_zero: True |
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| #!/bin/bash | ||
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| # Copyright (c) 2022 Hongji Wang ([email protected]) | ||
| # 2023 Zhengyang Chen ([email protected]) | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| exp_dir='' | ||
| model_path='' | ||
| nj=4 | ||
| gpus="[0,1]" | ||
| data_type="shard" # shard/raw/feat | ||
| data=data | ||
| reverb_data=data/rirs/lmdb | ||
| noise_data=data/musan/lmdb | ||
| aug_plda_data=0 | ||
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| . tools/parse_options.sh | ||
| set -e | ||
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| if [ $aug_plda_data = 0 ];then | ||
| sre_plda_data=sre | ||
| else | ||
| sre_plda_data=sre_aug | ||
| fi | ||
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| data_name_array=( | ||
| "${sre_plda_data}" | ||
| "sre16_major" | ||
| "sre16_eval_enroll" | ||
| "sre16_eval_test" | ||
| ) | ||
| data_list_path_array=( | ||
| "${data}/${sre_plda_data}/${data_type}.list" | ||
| "${data}/sre16_major/${data_type}.list" | ||
| "${data}/sre16_eval_enroll/${data_type}.list" | ||
| "${data}/sre16_eval_test/${data_type}.list" | ||
| ) | ||
| data_scp_path_array=( | ||
| "${data}/${sre_plda_data}/wav.scp" | ||
| "${data}/sre16_major/wav.scp" | ||
| "${data}/sre16_eval_enroll/wav.scp" | ||
| "${data}/sre16_eval_test/wav.scp" | ||
| ) # to count the number of wavs | ||
| nj_array=($nj $nj $nj $nj) | ||
| batch_size_array=(1 1 1 1) # batch_size of test set must be 1 !!! | ||
| num_workers_array=(1 1 1 1) | ||
| if [ $aug_plda_data = 0 ];then | ||
| aug_prob_array=(0.0 0.0 0.0 0.0) | ||
| else | ||
| aug_prob_array=(0.67 0.0 0.0 0.0) | ||
| fi | ||
| count=${#data_name_array[@]} | ||
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| for i in $(seq 0 $(($count - 1))); do | ||
| wavs_num=$(wc -l ${data_scp_path_array[$i]} | awk '{print $1}') | ||
| bash tools/extract_embedding.sh --exp_dir ${exp_dir} \ | ||
| --model_path $model_path \ | ||
| --data_type ${data_type} \ | ||
| --data_list ${data_list_path_array[$i]} \ | ||
| --wavs_num ${wavs_num} \ | ||
| --store_dir ${data_name_array[$i]} \ | ||
| --batch_size ${batch_size_array[$i]} \ | ||
| --num_workers ${num_workers_array[$i]} \ | ||
| --aug_prob ${aug_prob_array[$i]} \ | ||
| --reverb_data ${reverb_data} \ | ||
| --noise_data ${noise_data} \ | ||
| --nj ${nj_array[$i]} \ | ||
| --gpus $gpus | ||
| done | ||
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| wait | ||
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| echo "mean vector of enroll" | ||
| python tools/vector_mean.py \ | ||
| --spk2utt ${data}/sre16_eval_enroll/spk2utt \ | ||
| --xvector_scp $exp_dir/embeddings/sre16_eval_enroll/xvector.scp \ | ||
| --spk_xvector_ark $exp_dir/embeddings/sre16_eval_enroll/enroll_spk_xvector.ark | ||
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| mkdir -p ${exp_dir}/embeddings/eval | ||
| cat ${exp_dir}/embeddings/sre16_eval_enroll/enroll_spk_xvector.scp \ | ||
| ${exp_dir}/embeddings/sre16_eval_test/xvector.scp \ | ||
| > ${exp_dir}/embeddings/eval/xvector.scp | ||
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| echo "Embedding dir is (${exp_dir}/embeddings)." |
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| # Copyright (c) 2023 Zhengyang Chen | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| import fire | ||
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| def main(wav_scp, utt2voice_dur, filter_wav_scp, dur_thres=5.0): | ||
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| utt2voice_dur_dict = {} | ||
| with open(utt2voice_dur, "r") as f: | ||
| for line in f: | ||
| utt, dur = line.strip().split() | ||
| utt2voice_dur_dict[utt] = float(dur) | ||
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| with open(wav_scp, "r") as f, open(filter_wav_scp, "w") as fw: | ||
| for line in f: | ||
| utt = line.strip().split()[0] | ||
| if utt in utt2voice_dur_dict: | ||
| if utt2voice_dur_dict[utt] > dur_thres: | ||
| fw.write(line) | ||
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| if __name__ == "__main__": | ||
| fire.Fire(main) |
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| # Copyright (c) 2023 Zhengyang Chen | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| import os | ||
| import fire | ||
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| def main(ori_dir, aug_dir, aug_copy_num=2): | ||
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| if not os.path.exists(aug_dir): | ||
| os.makedirs(aug_dir) | ||
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| read_wav_scp = os.path.join(ori_dir, 'wav.scp') | ||
| aug_wav_scp = os.path.join(aug_dir, 'wav.scp') | ||
| read_utt2spk = os.path.join(ori_dir, 'utt2spk') | ||
| aug_utt2spk = os.path.join(aug_dir, 'utt2spk') | ||
| read_vad = os.path.join(ori_dir, 'vad') | ||
| store_vad = os.path.join(aug_dir, 'vad') | ||
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| with open(read_wav_scp, 'r') as f, open(aug_wav_scp, 'w') as wf: | ||
| for line in f: | ||
| line = line.strip().split() | ||
| utt, other_info = line[0], ' '.join(line[1:]) | ||
| for i in range(aug_copy_num + 1): | ||
| wf.write(utt + '_copy-' + str(i) + ' ' + other_info + '\n') | ||
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| with open(read_utt2spk, 'r') as f, open(aug_utt2spk, 'w') as wf: | ||
| for line in f: | ||
| line = line.strip().split() | ||
| utt, spk = line[0], line[1] | ||
| for i in range(aug_copy_num + 1): | ||
| wf.write(utt + '_copy-' + str(i) + ' ' + spk + '\n') | ||
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| with open(read_vad, 'r') as f, open(store_vad, 'w') as wf: | ||
| for line in f: | ||
| line = line.strip().split() | ||
| seg, utt, vad = line[0], line[1], ' '.join(line[2:]) | ||
| for i in range(aug_copy_num + 1): | ||
| new_seg = seg + '_copy-' + str(i) | ||
| new_utt = utt + '_copy-' + str(i) | ||
| wf.write(new_seg + ' ' + new_utt + ' ' + vad + '\n') | ||
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| if __name__ == "__main__": | ||
| fire.Fire(main) |
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