From faa8ad377aa728818635074a74ee9176fed88fc5 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期四, 09 三月 2023 16:28:47 +0800
Subject: [PATCH] modify unit test for speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch

---
 egs/callhome/diarization/sond/sond.yaml       | 2738 ++++++++++++++++++++++++++++
 funasr/models/encoder/resnet34_encoder.py     |  227 ++
 egs/callhome/diarization/sond/sond_fbank.yaml | 2738 ++++++++++++++++++++++++++++
 funasr/tasks/diar.py                          |    4 
 egs/callhome/diarization/sond/unit_test.py    |   97 +
 5 files changed, 5,803 insertions(+), 1 deletions(-)

diff --git a/egs/callhome/diarization/sond/sond.yaml b/egs/callhome/diarization/sond/sond.yaml
new file mode 100644
index 0000000..9e6640b
--- /dev/null
+++ b/egs/callhome/diarization/sond/sond.yaml
@@ -0,0 +1,2738 @@
+config: finetune.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/sond
+ngpu: 1
+seed: 0
+num_workers: 16
+num_att_plot: 0
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: 0
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: true
+distributed: false
+unused_parameters: true
+sharded_ddp: false
+ddp_backend: pytorch_ddp
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: false
+write_collected_feats: false
+max_epoch: 50
+patience: null
+val_scheduler_criterion:
+- valid
+- acc
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - acc
+    - max
+keep_nbest_models: 10
+nbest_averaging_interval: 0
+grad_clip: 5
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: true
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: false
+use_tensorboard: true
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+use_pai: true
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: null
+batch_size: 20
+valid_batch_size: null
+batch_bins: 10000
+valid_batch_bins: null
+train_shape_file:
+- /data/volume1/youyan/aishell/ark/train/speech_shape.1
+- /data/volume1/youyan/aishell/ark/train/text_shape.1
+valid_shape_file:
+- /data/volume1/youyan/aishell/ark/dev/speech_shape.1
+- /data/volume1/youyan/aishell/ark/dev/text_shape.1
+batch_type: length
+valid_batch_type: null
+fold_length:
+- 512
+- 150
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - /data/volume1/youyan/aishell/ark/train/data.scp
+    - speech
+    - kaldi_ark
+-   - /data/volume1/youyan/aishell/ark/train/data.text.1
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - /data/volume1/youyan/aishell/ark/dev/data.scp
+    - speech
+    - kaldi_ark
+-   - /data/volume1/youyan/aishell/ark/dev/data.text.1
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+    warmup_steps: 30000
+token_list:
+- '0'
+- '1'
+- '2'
+- '3'
+- '4'
+- '5'
+- '6'
+- '7'
+- '8'
+- '9'
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+init: null
+input_size: null
+cmvn_file: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+specaug: null
+specaug_conf: {}
+normalize: null
+normalize_conf: {}
+label_aggregator: null
+label_aggregator_conf: {}
+model: sond
+model_conf:
+    lsm_weight: 0.1
+    length_normalized_loss: true
+    max_spk_num: 16
+    normlize_speech_speaker: true
+# speech encoder
+encoder: resnet34_sp_l2reg
+encoder_conf:
+    # pass by model, equal to feature dim
+    # input_size: 80
+    pooling_type: "window_shift"
+    pool_size: 20
+    stride: 1
+    tf2torch_tensor_name_prefix_torch: encoder
+    tf2torch_tensor_name_prefix_tf: EAND/speech_encoder
+speaker_encoder: null
+speaker_encoder_conf: {}
+ci_scorer: conv
+ci_scorer_conf:
+    input_units: 512
+    num_layers: 3
+    num_units: 512
+    kernel_size: 1
+    dropout_rate: 0.0
+    position_encoder: null
+    out_units: 1
+    out_norm: false
+    auxiliary_states: false
+    tf2torch_tensor_name_prefix_torch: ci_scorer
+    tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer/ci_scorer
+cd_scorer: san
+cd_scorer_conf:
+    input_size: 512
+    output_size: 512
+    out_units: 1
+    attention_heads: 4
+    linear_units: 1024
+    num_blocks: 4
+    dropout_rate: 0.0
+    positional_dropout_rate: 0.0
+    attention_dropout_rate: 0.0
+    # use string "null" to remove input layer
+    input_layer: "null"
+    pos_enc_class: null
+    normalize_before: true
+    tf2torch_tensor_name_prefix_torch: cd_scorer
+    tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer/cd_scorer
+# post net
+decoder: fsmn
+decoder_conf:
+    in_units: 32
+    out_units: 2517
+    filter_size: 31
+    fsmn_num_layers: 6
+    dnn_num_layers: 1
+    num_memory_units: 16
+    ffn_inner_dim: 512
+    dropout_rate: 0.0
+    tf2torch_tensor_name_prefix_torch: decoder
+    tf2torch_tensor_name_prefix_tf: EAND/post_net
+frontend: wav_frontend
+frontend_conf:
+    fs: 8000
+    window: povey
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    filter_length_min: -1
+    filter_length_max: -1
+    lfr_m: 1
+    lfr_n: 1
+    dither: 0.0
+    snip_edges: false
+    upsacle_samples: false
+num_worker_count: 1
+required:
+- output_dir
+- token_list
+oss_bucket: 'null'
+version: 0.1.4
diff --git a/egs/callhome/diarization/sond/sond_fbank.yaml b/egs/callhome/diarization/sond/sond_fbank.yaml
new file mode 100644
index 0000000..9ef036d
--- /dev/null
+++ b/egs/callhome/diarization/sond/sond_fbank.yaml
@@ -0,0 +1,2738 @@
+config: finetune.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/sond
+ngpu: 1
+seed: 0
+num_workers: 16
+num_att_plot: 0
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: 0
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: true
+distributed: false
+unused_parameters: true
+sharded_ddp: false
+ddp_backend: pytorch_ddp
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: false
+write_collected_feats: false
+max_epoch: 50
+patience: null
+val_scheduler_criterion:
+- valid
+- acc
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - acc
+    - max
+keep_nbest_models: 10
+nbest_averaging_interval: 0
+grad_clip: 5
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: true
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: false
+use_tensorboard: true
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+use_pai: true
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: null
+batch_size: 20
+valid_batch_size: null
+batch_bins: 10000
+valid_batch_bins: null
+train_shape_file:
+- /data/volume1/youyan/aishell/ark/train/speech_shape.1
+- /data/volume1/youyan/aishell/ark/train/text_shape.1
+valid_shape_file:
+- /data/volume1/youyan/aishell/ark/dev/speech_shape.1
+- /data/volume1/youyan/aishell/ark/dev/text_shape.1
+batch_type: length
+valid_batch_type: null
+fold_length:
+- 512
+- 150
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - /data/volume1/youyan/aishell/ark/train/data.scp
+    - speech
+    - kaldi_ark
+-   - /data/volume1/youyan/aishell/ark/train/data.text.1
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - /data/volume1/youyan/aishell/ark/dev/data.scp
+    - speech
+    - kaldi_ark
+-   - /data/volume1/youyan/aishell/ark/dev/data.text.1
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+    warmup_steps: 30000
+token_list:
+- '0'
+- '1'
+- '2'
+- '3'
+- '4'
+- '5'
+- '6'
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+init: null
+input_size: 80
+cmvn_file: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+specaug: null
+specaug_conf: {}
+normalize: null
+normalize_conf: {}
+label_aggregator: null
+label_aggregator_conf: {}
+model: sond
+model_conf:
+    lsm_weight: 0.1
+    length_normalized_loss: true
+    max_spk_num: 16
+    normlize_speech_speaker: true
+# speech encoder
+encoder: resnet34_sp_l2reg
+encoder_conf:
+    # pass by model, equal to feature dim
+    # input_size: 80
+    pooling_type: "window_shift"
+    pool_size: 20
+    stride: 1
+    tf2torch_tensor_name_prefix_torch: encoder
+    tf2torch_tensor_name_prefix_tf: EAND/speech_encoder
+speaker_encoder: null
+speaker_encoder_conf: {}
+ci_scorer: conv
+ci_scorer_conf:
+    input_units: 512
+    num_layers: 3
+    num_units: 512
+    kernel_size: 1
+    dropout_rate: 0.0
+    position_encoder: null
+    out_units: 1
+    out_norm: false
+    auxiliary_states: false
+    tf2torch_tensor_name_prefix_torch: ci_scorer
+    tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer/ci_scorer
+cd_scorer: san
+cd_scorer_conf:
+    input_size: 512
+    output_size: 512
+    out_units: 1
+    attention_heads: 4
+    linear_units: 1024
+    num_blocks: 4
+    dropout_rate: 0.0
+    positional_dropout_rate: 0.0
+    attention_dropout_rate: 0.0
+    # use string "null" to remove input layer
+    input_layer: "null"
+    pos_enc_class: null
+    normalize_before: true
+    tf2torch_tensor_name_prefix_torch: cd_scorer
+    tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer/cd_scorer
+# post net
+decoder: fsmn
+decoder_conf:
+    in_units: 32
+    out_units: 2517
+    filter_size: 31
+    fsmn_num_layers: 6
+    dnn_num_layers: 1
+    num_memory_units: 16
+    ffn_inner_dim: 512
+    dropout_rate: 0.0
+    tf2torch_tensor_name_prefix_torch: decoder
+    tf2torch_tensor_name_prefix_tf: EAND/post_net
+frontend: null
+frontend_conf:
+    fs: 8000
+    window: povey
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    filter_length_min: -1
+    filter_length_max: -1
+    lfr_m: 1
+    lfr_n: 1
+    dither: 0.0
+    snip_edges: false
+    upsacle_samples: false
+num_worker_count: 0
+required:
+- output_dir
+- token_list
+oss_bucket: 'null'
+version: 0.1.4
diff --git a/egs/callhome/diarization/sond/unit_test.py b/egs/callhome/diarization/sond/unit_test.py
new file mode 100644
index 0000000..0aab932
--- /dev/null
+++ b/egs/callhome/diarization/sond/unit_test.py
@@ -0,0 +1,97 @@
+from funasr.bin.diar_inference_launch import inference_launch
+import os
+
+
+def test_fbank_cpu_infer():
+    diar_config_path = "sond_fbank.yaml"
+    diar_model_path = "sond.pth"
+    output_dir = "./outputs"
+    data_path_and_name_and_type = [
+        ("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
+        ("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
+    ]
+    pipeline = inference_launch(
+        mode="sond",
+        diar_train_config=diar_config_path,
+        diar_model_file=diar_model_path,
+        output_dir=output_dir,
+        num_workers=0,
+        log_level="INFO",
+    )
+    results = pipeline(data_path_and_name_and_type)
+    print(results)
+
+
+def test_fbank_gpu_infer():
+    diar_config_path = "config_fbank.yaml"
+    diar_model_path = "sond.pth"
+    output_dir = "./outputs"
+    data_path_and_name_and_type = [
+        ("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
+        ("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
+    ]
+    pipeline = inference_launch(
+        mode="sond",
+        diar_train_config=diar_config_path,
+        diar_model_file=diar_model_path,
+        output_dir=output_dir,
+        ngpu=1,
+        num_workers=1,
+        log_level="WARNING",
+    )
+    results = pipeline(data_path_and_name_and_type)
+    print(results)
+
+
+def test_wav_gpu_infer():
+    diar_config_path = "config.yaml"
+    diar_model_path = "sond.pth"
+    output_dir = "./outputs"
+    data_path_and_name_and_type = [
+        ("data/unit_test/test_wav.scp", "speech", "sound"),
+        ("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
+    ]
+    pipeline = inference_launch(
+        mode="sond",
+        diar_train_config=diar_config_path,
+        diar_model_file=diar_model_path,
+        output_dir=output_dir,
+        ngpu=1,
+        num_workers=1,
+        log_level="WARNING",
+    )
+    results = pipeline(data_path_and_name_and_type)
+    print(results)
+
+
+def test_without_profile_gpu_infer():
+    diar_config_path = "config.yaml"
+    diar_model_path = "sond.pth"
+    output_dir = "./outputs"
+    raw_inputs = [[
+        "data/unit_test/raw_inputs/record.wav",
+        "data/unit_test/raw_inputs/spk1.wav",
+        "data/unit_test/raw_inputs/spk2.wav",
+        "data/unit_test/raw_inputs/spk3.wav",
+        "data/unit_test/raw_inputs/spk4.wav"
+    ]]
+    pipeline = inference_launch(
+        mode="sond_demo",
+        diar_train_config=diar_config_path,
+        diar_model_file=diar_model_path,
+        output_dir=output_dir,
+        ngpu=1,
+        num_workers=1,
+        log_level="WARNING",
+        param_dict={},
+    )
+    results = pipeline(raw_inputs=raw_inputs)
+    print(results)
+
+
+if __name__ == '__main__':
+    os.environ["CUDA_VISIBLE_DEVICES"] = "7"
+    test_fbank_cpu_infer()
+    # test_fbank_gpu_infer()
+    # test_wav_gpu_infer()
+    # test_without_profile_gpu_infer()
diff --git a/funasr/models/encoder/resnet34_encoder.py b/funasr/models/encoder/resnet34_encoder.py
index 930f7e0..91c5dc0 100644
--- a/funasr/models/encoder/resnet34_encoder.py
+++ b/funasr/models/encoder/resnet34_encoder.py
@@ -612,3 +612,230 @@
                     logging.warning("{} is missed from tf checkpoint".format(name))
 
         return var_dict_torch_update
+
+
+class ResNet34SpL2RegDiar(ResNet34_SP_L2Reg):
+    def __init__(
+            self,
+            input_size,
+            embedding_node="resnet1_dense",
+            use_head_conv=True,
+            batchnorm_momentum=0.5,
+            use_head_maxpool=False,
+            num_nodes_pooling_layer=256,
+            layers_in_block=(3, 4, 6, 3),
+            filters_in_block=(32, 64, 128, 256),
+            num_nodes_resnet1=256,
+            num_nodes_last_layer=256,
+            pooling_type="window_shift",
+            pool_size=20,
+            stride=1,
+            tf2torch_tensor_name_prefix_torch="encoder",
+            tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder"
+    ):
+        super(ResNet34SpL2RegDiar, self).__init__(
+            input_size,
+            use_head_conv=use_head_conv,
+            batchnorm_momentum=batchnorm_momentum,
+            use_head_maxpool=use_head_maxpool,
+            num_nodes_pooling_layer=num_nodes_pooling_layer,
+            layers_in_block=layers_in_block,
+            filters_in_block=filters_in_block,
+        )
+
+        self.embedding_node = embedding_node
+        self.num_nodes_resnet1 = num_nodes_resnet1
+        self.num_nodes_last_layer = num_nodes_last_layer
+        self.pooling_type = pooling_type
+        self.pool_size = pool_size
+        self.stride = stride
+        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+
+        self.resnet1_dense = torch.nn.Linear(num_nodes_pooling_layer * 2, num_nodes_resnet1)
+        self.resnet1_bn = torch.nn.BatchNorm1d(num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum)
+
+        self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer)
+        self.resnet2_bn = torch.nn.BatchNorm1d(num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum)
+
+    def output_size(self) -> int:
+        if self.embedding_node.startswith("resnet1"):
+            return self.num_nodes_resnet1
+        elif self.embedding_node.startswith("resnet2"):
+            return self.num_nodes_last_layer
+
+        return self.num_nodes_pooling_layer
+
+    def forward(
+            self,
+            xs_pad: torch.Tensor,
+            ilens: torch.Tensor,
+            prev_states: torch.Tensor = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+
+        endpoints = OrderedDict()
+        res_out, ilens = super().forward(xs_pad, ilens)
+        endpoints["resnet0_bn"] = res_out
+        if self.pooling_type == "frame_gsp":
+            features = statistic_pooling(res_out, ilens, (2, ))
+        else:
+            features, ilens = windowed_statistic_pooling(res_out, ilens, (2, ), self.pool_size, self.stride)
+        features = features.transpose(1, 2)
+        endpoints["pooling"] = features
+
+        features = self.resnet1_dense(features)
+        endpoints["resnet1_dense"] = features
+        features = F.relu(features)
+        endpoints["resnet1_relu"] = features
+        features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2)
+        endpoints["resnet1_bn"] = features
+
+        features = self.resnet2_dense(features)
+        endpoints["resnet2_dense"] = features
+        features = F.relu(features)
+        endpoints["resnet2_relu"] = features
+        features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2)
+        endpoints["resnet2_bn"] = features
+
+        return endpoints[self.embedding_node], ilens, None
+
+    def gen_tf2torch_map_dict(self):
+        tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
+        tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
+        train_steps = 720000
+        map_dict_local = {
+            # torch: conv1d.weight in "out_channel in_channel kernel_size"
+            # tf   : conv1d.weight in "kernel_size in_channel out_channel"
+            # torch: linear.weight in "out_channel in_channel"
+            # tf   :  dense.weight in "in_channel out_channel"
+            "{}.pre_conv.weight".format(tensor_name_prefix_torch):
+                {"name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf),
+                 "squeeze": None,
+                 "transpose": (3, 2, 0, 1),
+                 },
+            "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch):
+                {"name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf),
+                 "squeeze": None,
+                 "transpose": None,
+                 },
+            "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch):
+                {"name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf),
+                 "squeeze": None,
+                 "transpose": None,
+                 },
+            "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch):
+                {"name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf),
+                 "squeeze": None,
+                 "transpose": None,
+                 },
+            "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch):
+                {"name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf),
+                 "squeeze": None,
+                 "transpose": None,
+                 },
+            "{}.pre_conv_bn.num_batches_tracked".format(tensor_name_prefix_torch): train_steps
+        }
+        for layer_idx in range(3):
+            map_dict_local.update({
+                "{}.resnet{}_dense.weight".format(tensor_name_prefix_torch, layer_idx):
+                    {"name": "{}/resnet{}_dense/kernel".format(tensor_name_prefix_tf, layer_idx),
+                     "squeeze": None,
+                     "transpose": (3, 2, 0, 1) if layer_idx == 0 else (1, 0),
+                     },
+                "{}.resnet{}_dense.bias".format(tensor_name_prefix_torch, layer_idx):
+                    {"name": "{}/resnet{}_dense/bias".format(tensor_name_prefix_tf, layer_idx),
+                     "squeeze": None,
+                     "transpose": None,
+                     },
+                "{}.resnet{}_bn.weight".format(tensor_name_prefix_torch, layer_idx):
+                    {"name": "{}/resnet{}_bn/gamma".format(tensor_name_prefix_tf, layer_idx),
+                     "squeeze": None,
+                     "transpose": None,
+                     },
+                "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx):
+                    {"name": "{}/resnet{}_bn/beta".format(tensor_name_prefix_tf, layer_idx),
+                     "squeeze": None,
+                     "transpose": None,
+                     },
+                "{}.resnet{}_bn.running_mean".format(tensor_name_prefix_torch, layer_idx):
+                    {"name": "{}/resnet{}_bn/moving_mean".format(tensor_name_prefix_tf, layer_idx),
+                     "squeeze": None,
+                     "transpose": None,
+                     },
+                "{}.resnet{}_bn.running_var".format(tensor_name_prefix_torch, layer_idx):
+                    {"name": "{}/resnet{}_bn/moving_variance".format(tensor_name_prefix_tf, layer_idx),
+                     "squeeze": None,
+                     "transpose": None,
+                     },
+                "{}.resnet{}_bn.num_batches_tracked".format(tensor_name_prefix_torch, layer_idx): train_steps
+            })
+
+        for block_idx in range(len(self.layers_in_block)):
+            for layer_idx in range(self.layers_in_block[block_idx]):
+                for i in ["1", "2", "_sc"]:
+                    map_dict_local.update({
+                        "{}.block_{}.layer_{}.conv{}.weight".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+                            {"name": "{}/block_{}/layer_{}/conv{}/kernel".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+                             "squeeze": None,
+                             "transpose": (3, 2, 0, 1),
+                             },
+                        "{}.block_{}.layer_{}.bn{}.weight".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+                            {"name": "{}/block_{}/layer_{}/bn{}/gamma".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+                             "squeeze": None,
+                             "transpose": None,
+                             },
+                        "{}.block_{}.layer_{}.bn{}.bias".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+                            {"name": "{}/block_{}/layer_{}/bn{}/beta".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+                             "squeeze": None,
+                             "transpose": None,
+                             },
+                        "{}.block_{}.layer_{}.bn{}.running_mean".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+                            {"name": "{}/block_{}/layer_{}/bn{}/moving_mean".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+                             "squeeze": None,
+                             "transpose": None,
+                             },
+                        "{}.block_{}.layer_{}.bn{}.running_var".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+                            {"name": "{}/block_{}/layer_{}/bn{}/moving_variance".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+                             "squeeze": None,
+                             "transpose": None,
+                             },
+                        "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format(tensor_name_prefix_torch, block_idx, layer_idx, i): train_steps,
+                    })
+
+        return map_dict_local
+
+    def convert_tf2torch(self,
+                         var_dict_tf,
+                         var_dict_torch,
+                         ):
+
+        map_dict = self.gen_tf2torch_map_dict()
+
+        var_dict_torch_update = dict()
+        for name in sorted(var_dict_torch.keys(), reverse=False):
+            if name.startswith(self.tf2torch_tensor_name_prefix_torch):
+                if name in map_dict:
+                    if "num_batches_tracked" not in name:
+                        name_tf = map_dict[name]["name"]
+                        data_tf = var_dict_tf[name_tf]
+                        if map_dict[name]["squeeze"] is not None:
+                            data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+                        if map_dict[name]["transpose"] is not None:
+                            data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+                        data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+                        assert var_dict_torch[name].size() == data_tf.size(), \
+                            "{}, {}, {} != {}".format(name, name_tf,
+                                                      var_dict_torch[name].size(), data_tf.size())
+                        var_dict_torch_update[name] = data_tf
+                        logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+                            name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+                        ))
+                    else:
+                        var_dict_torch_update[name] = torch.Tensor(map_dict[name]).type(torch.int64).to("cpu")
+                        logging.info("torch tensor: {}, manually assigning to: {}".format(
+                            name, map_dict[name]
+                        ))
+                else:
+                    logging.warning("{} is missed from tf checkpoint".format(name))
+
+        return var_dict_torch_update
diff --git a/funasr/tasks/diar.py b/funasr/tasks/diar.py
index 73c51e3..45e3d7a 100644
--- a/funasr/tasks/diar.py
+++ b/funasr/tasks/diar.py
@@ -23,7 +23,7 @@
 from funasr.layers.utterance_mvn import UtteranceMVN
 from funasr.layers.label_aggregation import LabelAggregate
 from funasr.models.ctc import CTC
-from funasr.models.encoder.resnet34_encoder import ResNet34Diar
+from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
 from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
 from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
 from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
@@ -122,6 +122,7 @@
         fsmn=FsmnEncoder,
         conv=ConvEncoder,
         resnet34=ResNet34Diar,
+        resnet34_sp_l2reg=ResNet34SpL2RegDiar,
         sanm_chunk_opt=SANMEncoderChunkOpt,
         data2vec_encoder=Data2VecEncoder,
         ecapa_tdnn=ECAPA_TDNN,
@@ -160,6 +161,7 @@
     classes=dict(
         dot=DotScorer,
         cosine=CosScorer,
+        conv=ConvEncoder,
     ),
     type_check=torch.nn.Module,
     default=None,

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