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/home/qingzhengw/espnet/.geolid-reproduction/venv/bin/python /home/qingzhengw/espnet/.geolid-reproduction/review-code/espnet2/bin/lid_inference.py --output_dir /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/models/combined_shared_trainable --ngpu 1 --dtype float32 --data_path_and_name_and_type /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/data/voxlingua107-dev/wav.scp,speech,sound --valid_batch_size 1 --lid_train_config /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/models/combined_shared_trainable/config.resolved.yaml --lid_model_file /home/qingzhengw/espnet/.geolid-reproduction/round2-native/combined_shared_trainable/model.pth --use_preprocessor false --fix_duration false --num_workers 8 --extract_embd false --save_embd_per_utt false --save_embd_avg_lang false --save_tsne_plot false --checkpoint_interval 200
[host-10-0-93-146] 2026-10-23 00:53:32,012 (abs_task:2514) INFO: config file: /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/models/combined_shared_trainable/config.resolved.yaml
Loading weights: 0%| | 0/806 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 806/806 [00:00<00:00, 33237.07it/s]
[transformers] [1mWav2Vec2Model LOAD REPORT[0m from: facebook/mms-1b
Key | Status | |
-----------------------------+------------+--+-
project_q.weight | UNEXPECTED | |
quantizer.codevectors | UNEXPECTED | |
project_hid.weight | UNEXPECTED | |
quantizer.weight_proj.weight | UNEXPECTED | |
project_q.bias | UNEXPECTED | |
project_hid.bias | UNEXPECTED | |
quantizer.weight_proj.bias | UNEXPECTED | |
Notes:
- UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch.
[host-10-0-93-146] 2026-10-23 00:53:43,296 (lid_inference:111) INFO: Model structure:
ESPnetLIDModel(
(frontend): S3prlFrontend(
(upstream): S3PRLUpstream(
(upstream): UpstreamExpert(
(model): Wav2Vec2Model(
(feature_extractor): Wav2Vec2FeatureEncoder(
(conv_layers): ModuleList(
(0): Wav2Vec2LayerNormConvLayer(
(conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(activation): GELUActivation()
)
(1-4): 4 x Wav2Vec2LayerNormConvLayer(
(conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(activation): GELUActivation()
)
(5-6): 2 x Wav2Vec2LayerNormConvLayer(
(conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(activation): GELUActivation()
)
)
)
(feature_projection): Wav2Vec2FeatureProjection(
(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(projection): Linear(in_features=512, out_features=1280, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): Wav2Vec2EncoderStableLayerNorm(
(pos_conv_embed): Wav2Vec2PositionalConvEmbedding(
(conv): ParametrizedConv1d(
1280, 1280, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
(parametrizations): ModuleDict(
(weight): ParametrizationList(
(0): _WeightNorm()
)
)
)
(padding): Wav2Vec2SamePadLayer()
(activation): GELUActivation()
)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
(layers): ModuleList(
(0-31): 32 x Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(32): Lang2VecConditionedLayer(
(layer): Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(encoder): IdentityEncoder()
(pooling): ChnAttnStatPooling(
(attention): Sequential(
(0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
(1): ReLU()
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
)
(softmax): Softmax(dim=2)
)
(projector): RawNet3Projector(
(bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc): Linear(in_features=2560, out_features=192, bias=True)
)
(prediction): Linear(in_features=192, out_features=299, bias=True)
(conditioning_layer): Linear(in_features=299, out_features=1280, bias=True)
)
(33-35): 3 x Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(36): Lang2VecConditionedLayer(
(layer): Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(encoder): IdentityEncoder()
(pooling): ChnAttnStatPooling(
(attention): Sequential(
(0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
(1): ReLU()
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
)
(softmax): Softmax(dim=2)
)
(projector): RawNet3Projector(
(bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc): Linear(in_features=2560, out_features=192, bias=True)
)
(prediction): Linear(in_features=192, out_features=299, bias=True)
(conditioning_layer): Linear(in_features=299, out_features=1280, bias=True)
)
(37-39): 3 x Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(40): Lang2VecConditionedLayer(
(layer): Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(encoder): IdentityEncoder()
(pooling): ChnAttnStatPooling(
(attention): Sequential(
(0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
(1): ReLU()
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
)
(softmax): Softmax(dim=2)
)
(projector): RawNet3Projector(
(bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc): Linear(in_features=2560, out_features=192, bias=True)
)
(prediction): Linear(in_features=192, out_features=299, bias=True)
(conditioning_layer): Linear(in_features=299, out_features=1280, bias=True)
)
(41-43): 3 x Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(44): Lang2VecConditionedLayer(
(layer): Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(encoder): IdentityEncoder()
(pooling): ChnAttnStatPooling(
(attention): Sequential(
(0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
(1): ReLU()
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
)
(softmax): Softmax(dim=2)
)
(projector): RawNet3Projector(
(bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc): Linear(in_features=2560, out_features=192, bias=True)
)
(prediction): Linear(in_features=192, out_features=299, bias=True)
(conditioning_layer): Linear(in_features=299, out_features=1280, bias=True)
)
(45-47): 3 x Wav2Vec2EncoderLayerStableLayerNorm(
(attention): Wav2Vec2Attention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=True)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(dropout): Dropout(p=0.1, inplace=False)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(feed_forward): Wav2Vec2FeedForward(
(intermediate_dropout): Dropout(p=0.0, inplace=False)
(intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
(intermediate_act_fn): GELUActivation()
(output_dense): Linear(in_features=5120, out_features=1280, bias=True)
(output_dropout): Dropout(p=0.1, inplace=False)
)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
)
)
)
)
)
(featurizer): Featurizer()
)
(normalize): UtteranceMVN(norm_means=True, norm_vars=False)
(encoder): EcapaTdnnEncoder(
(conv): Conv1d(1280, 512, kernel_size=(5,), stride=(1,), padding=(2,))
(relu): ReLU()
(bn): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(layer1): EcapaBlock(
(conv1): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(convs): ModuleList(
(0-6): 7 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
)
(bns): ModuleList(
(0-6): 7 x BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU()
(se): SEModule(
(se): Sequential(
(0): AdaptiveAvgPool1d(output_size=1)
(1): Conv1d(512, 128, kernel_size=(1,), stride=(1,))
(2): ReLU()
(3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): Conv1d(128, 512, kernel_size=(1,), stride=(1,))
(5): Sigmoid()
)
)
)
(layer2): EcapaBlock(
(conv1): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(convs): ModuleList(
(0-6): 7 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
)
(bns): ModuleList(
(0-6): 7 x BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU()
(se): SEModule(
(se): Sequential(
(0): AdaptiveAvgPool1d(output_size=1)
(1): Conv1d(512, 128, kernel_size=(1,), stride=(1,))
(2): ReLU()
(3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): Conv1d(128, 512, kernel_size=(1,), stride=(1,))
(5): Sigmoid()
)
)
)
(layer3): EcapaBlock(
(conv1): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(convs): ModuleList(
(0-6): 7 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(4,), dilation=(4,))
)
(bns): ModuleList(
(0-6): 7 x BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU()
(se): SEModule(
(se): Sequential(
(0): AdaptiveAvgPool1d(output_size=1)
(1): Conv1d(512, 128, kernel_size=(1,), stride=(1,))
(2): ReLU()
(3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): Conv1d(128, 512, kernel_size=(1,), stride=(1,))
(5): Sigmoid()
)
)
)
(extra_layers): ModuleList()
(layer4): Conv1d(1536, 1536, kernel_size=(1,), stride=(1,))
(mp3): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
)
(pooling): ChnAttnStatPooling(
(attention): Sequential(
(0): Conv1d(4608, 128, kernel_size=(1,), stride=(1,))
(1): ReLU()
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Conv1d(128, 1536, kernel_size=(1,), stride=(1,))
)
(softmax): Softmax(dim=2)
)
(projector): RawNet3Projector(
(bn): BatchNorm1d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc): Linear(in_features=3072, out_features=192, bias=True)
)
(loss): ArcMarginProduct_intertopk_subcenter(
(ce): CrossEntropyLoss()
)
(lang2vec_head): Linear(in_features=192, out_features=299, bias=True)
(lang2vec_loss): MSELoss()
)
Model summary:
Class Name: ESPnetLIDModel
Total Number of model parameters: 977.14 M
Number of trainable parameters: 977.14 M (100.0%)
Size: 3.91 GB
Type: torch.float32
/home/qingzhengw/espnet/.geolid-reproduction/venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py:424: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
self.check_worker_number_rationality()
/home/qingzhengw/espnet/.geolid-reproduction/review-code/espnet2/train/reporter.py:320: UserWarning: The stats of the previous epoch=-1doesn't exist.
warnings.warn(
[host-10-0-93-146] 2026-10-23 00:53:43,304 (lid_trainer:121) INFO: [Rank 0] Resume: 0 utterances found in /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/models/combined_shared_trainable/lids0
/home/qingzhengw/espnet/.geolid-reproduction/venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py:432: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
self.check_worker_number_rationality()
[host-10-0-93-146] 2026-10-23 00:54:01,839 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 0
[host-10-0-93-146] 2026-10-23 00:54:18,771 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 1
[host-10-0-93-146] 2026-10-23 00:54:34,599 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 2
[host-10-0-93-146] 2026-10-23 00:54:50,150 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 3
[host-10-0-93-146] 2026-10-23 00:55:04,729 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 4
[host-10-0-93-146] 2026-10-23 00:55:19,633 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 5
[host-10-0-93-146] 2026-10-23 00:55:34,813 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 6
[host-10-0-93-146] 2026-10-23 00:55:49,924 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 7
[host-10-0-93-146] 2026-10-23 00:55:50,841 (lid_inference:274) INFO: args.save_embd_per_utt: False
[host-10-0-93-146] 2026-10-23 00:55:50,842 (lid_inference:305) INFO: args.save_tsne_plot: False
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