[feature] add a frontend module in wespeaker and support wavlm

.gitignore

@@ -42,3 +42,4 @@ tensorboard
 *.onnx
 external_tools
 pretrained_models
+s3prl_hub

README.md

@@ -60,6 +60,7 @@ pre-commit install  # for clean and tidy code
 ```
 
 ## 🔥 News
+* 2024.08.18: Support using ssl pre-trained models as the frontend. The [WavLM recipe](https://github.com/wenet-e2e/wespeaker/blob/master/examples/voxceleb/v2/run_wavlm.sh) is also provided, see [#344](https://github.com/wenet-e2e/wespeaker/pull/344).
 * 2024.05.15: Add support for [quality-aware score calibration](https://arxiv.org/pdf/2211.00815), see [#320](https://github.com/wenet-e2e/wespeaker/pull/320).
 * 2024.04.25: Add support for the gemini-dfresnet model, see [#291](https://github.com/wenet-e2e/wespeaker/pull/291).
 * 2024.04.23: Support MNN inference engine in runtime, see [#310](https://github.com/wenet-e2e/wespeaker/pull/310).

examples/voxceleb/v2/README.md

@@ -70,3 +70,25 @@ The results on ResNet34 (large margin, no asnorm) are:
 |:--------------:|:------------:|:------------:|:------------:|
 |      PLDA      |    1.207     |    1.350     |    2.528     |
 
+
+## WavLM results
+
+* Pre-trained frontend: the [WavLM](https://arxiv.org/abs/2110.13900) Large model, multilayer features are used
+* Speaker model: ECAPA_TDNN_GLOB_c512-ASTP-emb192
+* Training strategy: Frozen => Joint ft => Joint lmft
+
+```bash
+bash run_wavlm.sh --stage 3 --stop_stage 9
+```
+
+| Training strategy | AS-Norm | QMF | vox1-O-clean | vox1-E-clean | vox1-H-clean |
+|:------------------|:-------:|:---:|:------------:|:------------:|:------------:|
+| Frozen            | × | × | 0.595 | 0.719 | 1.501 |
+|                   | √ | × | 0.548 | 0.656 | 1.355 |
+|                   | √ | √ | 0.489 | 0.619 | 1.224 |
+| Frozen => Joint ft | × | × | 0.542 | 0.635 | 1.355 |
+|                    | √ | × | 0.521 | 0.594 | 1.237 |
+|                    | √ | √ | 0.494 | 0.576 | 1.205 |
+| Frozen => Joint ft => Joint lmft | × | × | 0.521 | 0.626 | 1.344 |
+|                                  | √ | × | 0.495 | 0.588 | 1.247 |
+|                                  | √ | √ | **0.415** | **0.551** | **1.118** |

examples/voxceleb/v2/conf/ecapa_tdnn.yaml

@@ -32,6 +32,7 @@ dataset_args:
   speed_perturb: True
   num_frms: 200
   aug_prob: 0.6 # prob to add reverb & noise aug per sample
+  frontend: "fbank" # fbank, s3prl
   fbank_args:
     num_mel_bins: 80
     frame_shift: 10

examples/voxceleb/v2/conf/ecapa_tdnn_WavLM_frozen.yaml

@@ -0,0 +1,91 @@
+### train configuraton
+
+exp_dir: exp/ECAPA_TDNN_GLOB_c512-ASTP-emb192-WavLM_Large_frozen-num_frms150-aug0.6-spTrue-saFalse-ArcMargin_intertopk_subcenter-SGD-epoch150
+gpus: "[0,1,2,3,4,5,6,7]"
+num_avg: 10
+enable_amp: True # whether enable automatic mixed precision training
+
+seed: 42
+num_epochs: 150
+save_epoch_interval: 5 # save model every 5 epochs
+log_batch_interval: 100 # log every 100 batchs
+
+dataloader_args:
+  batch_size: 256
+  num_workers: 16
+  pin_memory: False
+  prefetch_factor: 16
+  drop_last: True
+
+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: 0
+  shuffle: True
+  shuffle_args:
+    shuffle_size: 2500
+  filter: True
+  filter_args:
+    min_num_frames: 50
+    max_num_frames: 400
+  resample_rate: 16000
+  speed_perturb: True
+  num_frms: 150
+  aug_prob: 0.6 # prob to add reverb & noise aug per sample
+  frontend: "s3prl" # fbank, s3prl
+  s3prl_args:
+    upstream_args:
+      name: "wavlm_large"
+    download_dir: ./s3prl_hub
+    multilayer_feature: True
+    layer: -1
+    frozen: True
+    frame_shift: 20
+    frame_length: 20
+  cmvn: True
+  cmvn_args:
+    norm_mean: True
+    norm_var: False
+  spec_aug: False
+  spec_aug_args:
+    num_t_mask: 1
+    num_f_mask: 1
+    max_t: 10
+    max_f: 8
+    prob: 0.6
+
+model: ECAPA_TDNN_GLOB_c512 # ECAPA_TDNN_GLOB_c512, ECAPA_TDNN_GLOB_c1024
+model_init: null
+model_args:
+  feat_dim: -1 # equals to the output_size of the frontend (will be initialized before training)
+  embed_dim: 192
+  pooling_func: "ASTP" # the default pooling_func in ECAPA_TDNN is ASTP
+projection_args:
+  project_type: "arc_margin_intertopk_subcenter" # add_margin, arc_margin, sphere, softmax, arc_margin_intertopk_subcenter
+  scale: 32.0
+  easy_margin: False
+
+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
+
+loss: CrossEntropyLoss
+loss_args: {}
+
+optimizer: SGD
+optimizer_args:
+  momentum: 0.9
+  nesterov: True
+  weight_decay: 0.0001
+
+scheduler: ExponentialDecrease
+scheduler_args:
+  initial_lr: 0.1
+  final_lr: 0.00001
+  warm_up_epoch: 6
+  warm_from_zero: True

examples/voxceleb/v2/conf/ecapa_tdnn_WavLM_joint_ft.yaml

@@ -0,0 +1,92 @@
+### train configuraton
+
+exp_dir: exp/ECAPA_TDNN_GLOB_c512-ASTP-emb192-WavLM_Large_joint_ft-num_frms150-aug0.6-spTrue-saFalse-ArcMargin_intertopk_subcenter-SGD-epoch20
+gpus: "[0,1,2,3,4,5,6,7]"
+num_avg: 3
+enable_amp: True # whether enable automatic mixed precision training
+do_lm: False
+
+seed: 42
+num_epochs: 20
+save_epoch_interval: 1 # save model every epoch
+log_batch_interval: 100 # log every 100 batchs
+
+dataloader_args:
+  batch_size: 64
+  num_workers: 8
+  pin_memory: False
+  prefetch_factor: 8
+  drop_last: True
+
+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: 0
+  shuffle: True
+  shuffle_args:
+    shuffle_size: 2500
+  filter: True
+  filter_args:
+    min_num_frames: 50
+    max_num_frames: 400
+  resample_rate: 16000
+  speed_perturb: True
+  num_frms: 150
+  aug_prob: 0.6 # prob to add reverb & noise aug per sample
+  frontend: "s3prl" # fbank, s3prl
+  s3prl_args:
+    upstream_args:
+      name: "wavlm_large"
+    download_dir: ./s3prl_hub
+    multilayer_feature: True
+    layer: -1
+    frozen: False
+    frame_shift: 20
+    frame_length: 20
+  cmvn: True
+  cmvn_args:
+    norm_mean: True
+    norm_var: False
+  spec_aug: False
+  spec_aug_args:
+    num_t_mask: 1
+    num_f_mask: 1
+    max_t: 10
+    max_f: 8
+    prob: 0.6
+
+model: ECAPA_TDNN_GLOB_c512 # ECAPA_TDNN_GLOB_c512, ECAPA_TDNN_GLOB_c1024
+model_init: null
+model_args:
+  feat_dim: -1 # equals to the output_size of the frontend (will be initialized before training)
+  embed_dim: 192
+  pooling_func: "ASTP" # the default pooling_func in ECAPA_TDNN is ASTP
+projection_args:
+  project_type: "arc_margin_intertopk_subcenter" # add_margin, arc_margin, sphere, softmax, arc_margin_intertopk_subcenter
+  scale: 32.0
+  easy_margin: False
+
+margin_scheduler: MarginScheduler
+margin_update:
+  initial_margin: 0.2
+  final_margin: 0.2
+  increase_start_epoch: 1
+  fix_start_epoch: 1
+  update_margin: True
+  increase_type: "exp" # exp, linear
+
+loss: CrossEntropyLoss
+loss_args: {}
+
+optimizer: SGD
+optimizer_args:
+  momentum: 0.9
+  nesterov: True
+  weight_decay: 0.0001
+
+scheduler: ExponentialDecrease
+scheduler_args:
+  initial_lr: 0.001
+  final_lr: 0.00025
+  warm_up_epoch: 1
+  warm_from_zero: True

examples/voxceleb/v2/conf/ecapa_tdnn_WavLM_joint_lmft.yaml

@@ -0,0 +1,92 @@
+### train configuraton
+
+exp_dir: exp/ECAPA_TDNN_GLOB_c512-ASTP-emb192-WavLM_Large_joint_lmft-num_frms300-aug0.6-spTrue-saFalse-ArcMargin_intertopk_subcenter-SGD-epoch10
+gpus: "[0,1,2,3,4,5,6,7]"
+num_avg: 1
+enable_amp: True # whether enable automatic mixed precision training
+do_lm: True
+
+seed: 42
+num_epochs: 20
+save_epoch_interval: 1 # save model every epoch
+log_batch_interval: 100 # log every 100 batchs
+
+dataloader_args:
+  batch_size: 32
+  num_workers: 8
+  pin_memory: False
+  prefetch_factor: 8
+  drop_last: True
+
+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: 0
+  shuffle: True
+  shuffle_args:
+    shuffle_size: 2500
+  filter: True
+  filter_args:
+    min_num_frames: 50
+    max_num_frames: 400
+  resample_rate: 16000
+  speed_perturb: True
+  num_frms: 300
+  aug_prob: 0.6 # prob to add reverb & noise aug per sample
+  frontend: "s3prl" # fbank, s3prl
+  s3prl_args:
+    upstream_args:
+      name: "wavlm_large"
+    download_dir: ./s3prl_hub
+    multilayer_feature: True
+    layer: -1
+    frozen: False
+    frame_shift: 20
+    frame_length: 20
+  cmvn: True
+  cmvn_args:
+    norm_mean: True
+    norm_var: False
+  spec_aug: False
+  spec_aug_args:
+    num_t_mask: 1
+    num_f_mask: 1
+    max_t: 10
+    max_f: 8
+    prob: 0.6
+
+model: ECAPA_TDNN_GLOB_c512 # ECAPA_TDNN_GLOB_c512, ECAPA_TDNN_GLOB_c1024
+model_init: null
+model_args:
+  feat_dim: -1 # equals to the output_size of the frontend (will be initialized before training)
+  embed_dim: 192
+  pooling_func: "ASTP" # the default pooling_func in ECAPA_TDNN is ASTP
+projection_args:
+  project_type: "arc_margin_intertopk_subcenter" # add_margin, arc_margin, sphere, softmax, arc_margin_intertopk_subcenter
+  scale: 32.0
+  easy_margin: False
+
+margin_scheduler: MarginScheduler
+margin_update:
+  initial_margin: 0.5
+  final_margin: 0.5
+  increase_start_epoch: 1
+  fix_start_epoch: 1
+  update_margin: True
+  increase_type: "exp" # exp, linear
+
+loss: CrossEntropyLoss
+loss_args: {}
+
+optimizer: SGD
+optimizer_args:
+  momentum: 0.9
+  nesterov: True
+  weight_decay: 0.0001
+
+scheduler: ExponentialDecrease
+scheduler_args:
+  initial_lr: 0.0001
+  final_lr: 0.000025
+  warm_up_epoch: 1
+  warm_from_zero: True

examples/voxceleb/v2/conf/ecapa_tdnn_lm.yaml

@@ -38,6 +38,7 @@ dataset_args:
   speed_perturb: True
   num_frms: 600
   aug_prob: 0.6 # prob to add reverb & noise aug per sample
+  frontend: "fbank" # fbank, s3prl
   fbank_args:
     num_mel_bins: 80
     frame_shift: 10

examples/voxceleb/v2/run.sh

@@ -11,8 +11,8 @@ stop_stage=-1
 data=data
 data_type="shard"  # shard/raw
 
-config=conf/campplus.yaml
-exp_dir=exp/CAMPPlus-TSTP-emb512-fbank80-num_frms200-aug0.6-spTrue-saFalse-ArcMargin-SGD-epoch150
+config=conf/resnet.yaml
+exp_dir=exp/ResNet34-TSTP-emb256-fbank80-num_frms200-aug0.6-spTrue-saFalse-ArcMargin-SGD-epoch150
 gpus="[0,1]"
 num_avg=10
 checkpoint=
@@ -22,7 +22,7 @@ score_norm_method="asnorm"  # asnorm/snorm
 top_n=300
 
 # setup for large margin fine-tuning
-lm_config=conf/campplus_lm.yaml
+lm_config=conf/resnet_lm.yaml
 
 . tools/parse_options.sh || exit 1
 
@@ -55,7 +55,7 @@ fi
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
   echo "Start training ..."
   num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
-  torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
+  torchrun --master_addr=localhost --master_port=29401 --nnodes=1 --nproc_per_node=$num_gpus \
     wespeaker/bin/train.py --config $config \
       --exp_dir ${exp_dir} \
       --gpus $gpus \