| Failed to import Flash Attention, using ESPnet default: No module named 'flash_attn' |
| /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/vl107only_shared_frozen --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/vl107only_shared_frozen/config.resolved.yaml --lid_model_file /home/qingzhengw/espnet/.geolid-reproduction/round2-native/vl107only_shared_frozen/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 01:01:14,625 (abs_task:2514) INFO: config file: /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/models/vl107only_shared_frozen/config.resolved.yaml |
|
Loading weights: 0%| | 0/806 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 806/806 [00:00<00:00, 46075.55it/s] |
| [transformers] [1mWav2Vec2Model LOAD REPORT[0m from: facebook/mms-1b |
| Key | Status | | |
| -----------------------------+------------+--+- |
| quantizer.weight_proj.weight | UNEXPECTED | | |
| project_q.bias | UNEXPECTED | | |
| project_hid.bias | UNEXPECTED | | |
| project_q.weight | UNEXPECTED | | |
| project_hid.weight | UNEXPECTED | | |
| quantizer.codevectors | 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 01:01:23,590 (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: 977.11 M |
| Number of trainable parameters: 976.73 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 01:01:23,598 (lid_trainer:121) INFO: [Rank 0] Resume: 0 utterances found in /home/qingzhengw/espnet/.geolid-reproduction/full-dev-unified-301372e4/models/vl107only_shared_frozen/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 01:01:41,226 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 0 |
| [host-10-0-93-146] 2026-10-23 01:01:57,575 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 1 |
| [host-10-0-93-146] 2026-10-23 01:02:12,983 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 2 |
| [host-10-0-93-146] 2026-10-23 01:02:28,077 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 3 |
| [host-10-0-93-146] 2026-10-23 01:02:42,178 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 4 |
| [host-10-0-93-146] 2026-10-23 01:02:56,631 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 5 |
| [host-10-0-93-146] 2026-10-23 01:03:11,273 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 6 |
| [host-10-0-93-146] 2026-10-23 01:03:25,914 (lid_trainer:250) INFO: [Rank 0] Saved 200 utts at step 7 |
| [host-10-0-93-146] 2026-10-23 01:03:26,865 (lid_inference:274) INFO: args.save_embd_per_utt: False |
| [host-10-0-93-146] 2026-10-23 01:03:26,866 (lid_inference:305) INFO: args.save_tsne_plot: False |
| |
