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# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel data/cy_token_list/bpe_unigram150/bpe.model --token_type bpe --token_list data/cy_token_list/bpe_unigram150/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_cy/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_cy/text,text,text --valid_shape_file exp/asr_stats_raw_cy_bpe150_sp/valid/speech_shape --valid_shape_file exp/asr_stats_raw_cy_bpe150_sp/valid/text_shape.bpe --resume true --init_param --ignore_init_mismatch false --fold_length 80000 --fold_length 150 --output_dir exp/asr_oxford_frontend_raw_cy_bpe150_sp --config conf/tuning/oxford_frontend.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_cy_sp/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_cy_sp/text,text,text --train_shape_file exp/asr_stats_raw_cy_bpe150_sp/train/speech_shape --train_shape_file exp/asr_stats_raw_cy_bpe150_sp/train/text_shape.bpe --ngpu 3 --multiprocessing_distributed True
# Started at Mon Jun 6 13:43:54 EDT 2022
#
/usr/bin/python3 /project/ocean/junweih/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel data/cy_token_list/bpe_unigram150/bpe.model --token_type bpe --token_list data/cy_token_list/bpe_unigram150/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_cy/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_cy/text,text,text --valid_shape_file exp/asr_stats_raw_cy_bpe150_sp/valid/speech_shape --valid_shape_file exp/asr_stats_raw_cy_bpe150_sp/valid/text_shape.bpe --resume true --init_param --ignore_init_mismatch false --fold_length 80000 --fold_length 150 --output_dir exp/asr_oxford_frontend_raw_cy_bpe150_sp --config conf/tuning/oxford_frontend.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_cy_sp/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_cy_sp/text,text,text --train_shape_file exp/asr_stats_raw_cy_bpe150_sp/train/speech_shape --train_shape_file exp/asr_stats_raw_cy_bpe150_sp/train/text_shape.bpe --ngpu 3 --multiprocessing_distributed True
[islpc50:0/3] 2022-06-06 13:44:05,310 (distributed_c10d:217) INFO: Added key: store_based_barrier_key:1 to store for rank: 0
[islpc50:0/3] 2022-06-06 13:44:05,310 (distributed_c10d:251) INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 3 nodes.
[islpc50:0/3] 2022-06-06 13:44:05,357 (asr:411) INFO: Vocabulary size: 150
[islpc50:0/3] 2022-06-06 13:44:05,863 (filelock:274) INFO: Lock 139812231996992 acquired on ./hub/s3prl_cache/1c76d6e88090f01736036b28dc995fef583f47f42662d55286332557f957609f.lock
[islpc50:0/3] 2022-06-06 13:44:05,864 (filelock:318) INFO: Lock 139812231996992 released on ./hub/s3prl_cache/1c76d6e88090f01736036b28dc995fef583f47f42662d55286332557f957609f.lock
[Featurizer] - The selected feature last_hidden_state's downsample rate is 320
[islpc50:0/3] 2022-06-06 13:44:20,588 (s3prl:159) INFO: Pretrained S3PRL frontend model parameters reloaded!
[islpc50:0/3] 2022-06-06 13:44:24,553 (abs_task:1157) INFO: pytorch.version=1.10.1+cu111, cuda.available=True, cudnn.version=8005, cudnn.benchmark=False, cudnn.deterministic=True
[islpc50:0/3] 2022-06-06 13:44:24,558 (abs_task:1158) INFO: Model structure:
ESPnetASRModel(
(frontend): S3prlFrontend(
(upstream): UpstreamExpert(
(model): Wav2Vec2Model(
(feature_extractor): ConvFeatureExtractionModel(
(conv_layers): ModuleList(
(0): Sequential(
(0): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
(1): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
(2): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
(3): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
(4): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
(5): Sequential(
(0): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
(6): Sequential(
(0): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU()
)
)
)
(post_extract_proj): Linear(in_features=512, out_features=1024, bias=True)
(dropout_input): Dropout(p=0.1, inplace=False)
(dropout_features): Dropout(p=0.1, inplace=False)
(quantizer): GumbelVectorQuantizer(
(weight_proj): Linear(in_features=512, out_features=640, bias=True)
)
(project_q): Linear(in_features=768, out_features=768, bias=True)
(encoder): TransformerEncoder(
(pos_conv): Sequential(
(0): Conv1d(1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)
(1): SamePad()
(2): GELU()
)
(layers): ModuleList(
(0): AdapterTransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(adapter1): Adapter(
(down_projection): Linear(in_features=1024, out_features=192, bias=True)
(up_projection): Linear(in_features=192, out_features=1024, bias=True)
)
(adapter2): Adapter(
(down_projection): Linear(in_features=1024, out_features=192, bias=True)
(up_projection): Linear(in_features=192, out_features=1024, bias=True)
)
)
(1): AdapterTransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(adapter1): Adapter(
(down_projection): Linear(in_features=1024, out_features=192, bias=True)
(up_projection): Linear(in_features=192, out_features=1024, bias=True)
)
(adapter2): Adapter(
(down_projection): Linear(in_features=1024, out_features=192, bias=True)
(up_projection): Linear(in_features=192, out_features=1024, bias=True)
)
)
(2): AdapterTransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(adapter1): Adapter(
(down_projection): Linear(in_features=1024, out_features=192, bias=True)
(up_projection): Linear(in_features=192, out_features=1024, bias=True)
)
(adapter2): Adapter(
(down_projection): Linear(in_features=1024, out_features=192, bias=True)
(up_projection): Linear(in_features=192, out_features=1024, bias=True)
)
)
(3): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(4): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(5): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(6): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(7): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(8): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(9): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(10): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(11): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(12): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(13): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(14): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(15): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(16): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(17): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(18): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(19): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(20): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(21): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(22): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(23): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.0, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(final_proj): Linear(in_features=1024, out_features=768, bias=True)
)
)
(featurizer): Featurizer()
)
(normalize): UtteranceMVN(norm_means=True, norm_vars=False)
(encoder): TransformerEncoder(
(embed): Conv2dSubsampling(
(conv): Sequential(
(0): Conv2d(1, 1024, kernel_size=(3, 3), stride=(2, 2))
(1): ReLU()
(2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2))
(3): ReLU()
)
(out): Sequential(
(0): Linear(in_features=261120, out_features=1024, bias=True)
(1): PositionalEncoding(
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(encoders): MultiSequential(
(0): EncoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=1024, out_features=1024, bias=True)
(linear_k): Linear(in_features=1024, out_features=1024, bias=True)
(linear_v): Linear(in_features=1024, out_features=1024, bias=True)
(linear_out): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=1024, out_features=2048, bias=True)
(w_2): Linear(in_features=2048, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(activation): ReLU()
)
(norm1): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(1): EncoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=1024, out_features=1024, bias=True)
(linear_k): Linear(in_features=1024, out_features=1024, bias=True)
(linear_v): Linear(in_features=1024, out_features=1024, bias=True)
(linear_out): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=1024, out_features=2048, bias=True)
(w_2): Linear(in_features=2048, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(activation): ReLU()
)
(norm1): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(2): EncoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=1024, out_features=1024, bias=True)
(linear_k): Linear(in_features=1024, out_features=1024, bias=True)
(linear_v): Linear(in_features=1024, out_features=1024, bias=True)
(linear_out): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=1024, out_features=2048, bias=True)
(w_2): Linear(in_features=2048, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(activation): ReLU()
)
(norm1): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(3): EncoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=1024, out_features=1024, bias=True)
(linear_k): Linear(in_features=1024, out_features=1024, bias=True)
(linear_v): Linear(in_features=1024, out_features=1024, bias=True)
(linear_out): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=1024, out_features=2048, bias=True)
(w_2): Linear(in_features=2048, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(activation): ReLU()
)
(norm1): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(4): EncoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=1024, out_features=1024, bias=True)
(linear_k): Linear(in_features=1024, out_features=1024, bias=True)
(linear_v): Linear(in_features=1024, out_features=1024, bias=True)
(linear_out): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=1024, out_features=2048, bias=True)
(w_2): Linear(in_features=2048, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(activation): ReLU()
)
(norm1): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(5): EncoderLayer(
(self_attn): MultiHeadedAttention(
(linear_q): Linear(in_features=1024, out_features=1024, bias=True)
(linear_k): Linear(in_features=1024, out_features=1024, bias=True)
(linear_v): Linear(in_features=1024, out_features=1024, bias=True)
(linear_out): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(feed_forward): PositionwiseFeedForward(
(w_1): Linear(in_features=1024, out_features=2048, bias=True)
(w_2): Linear(in_features=2048, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(activation): ReLU()
)
(norm1): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(after_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
)
(criterion_att): LabelSmoothingLoss(
(criterion): KLDivLoss()
)
(ctc): CTC(
(ctc_lo): Linear(in_features=1024, out_features=150, bias=True)
(ctc_loss): CTCLoss()
)
)
Model summary:
Class Name: ESPnetASRModel
Total Number of model parameters: 647.15 M
Number of trainable parameters: 329.76 M (51.0%)
Size: 1.32 GB
Type: torch.float32
[islpc50:0/3] 2022-06-06 13:44:24,559 (abs_task:1161) INFO: Optimizer:
Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
initial_lr: 0.00027
lr: 6.749999999999999e-09
weight_decay: 0
)
[islpc50:0/3] 2022-06-06 13:44:24,559 (abs_task:1162) INFO: Scheduler: WarmupLR(warmup_steps=40000)
[islpc50:0/3] 2022-06-06 13:44:24,564 (abs_task:1171) INFO: Saving the configuration in exp/asr_oxford_frontend_raw_cy_bpe150_sp/config.yaml
[islpc50:0/3] 2022-06-06 13:44:26,790 (abs_task:1525) INFO: [train] dataset:
ESPnetDataset(
speech: {"path": "dump/raw/train_cy_sp/wav.scp", "type": "sound"}
text: {"path": "dump/raw/train_cy_sp/text", "type": "text"}
preprocess: <espnet2.train.preprocessor.CommonPreprocessor object at 0x7f28098a80a0>)
[islpc50:0/3] 2022-06-06 13:44:26,790 (abs_task:1526) INFO: [train] Batch sampler: FoldedBatchSampler(N-batch=58673, batch_size=1, shape_files=['exp/asr_stats_raw_cy_bpe150_sp/train/speech_shape', 'exp/asr_stats_raw_cy_bpe150_sp/train/text_shape.bpe'], sort_in_batch=descending, sort_batch=descending)
[islpc50:0/3] 2022-06-06 13:44:26,797 (abs_task:1527) INFO: [train] mini-batch sizes summary: N-batch=58673, mean=3.0, min=3, max=3
[islpc50:0/3] 2022-06-06 13:44:26,935 (abs_task:1525) INFO: [valid] dataset:
ESPnetDataset(
speech: {"path": "dump/raw/dev_cy/wav.scp", "type": "sound"}
text: {"path": "dump/raw/dev_cy/text", "type": "text"}
preprocess: <espnet2.train.preprocessor.CommonPreprocessor object at 0x7f28098a83d0>)
[islpc50:0/3] 2022-06-06 13:44:26,935 (abs_task:1526) INFO: [valid] Batch sampler: FoldedBatchSampler(N-batch=977, batch_size=1, shape_files=['exp/asr_stats_raw_cy_bpe150_sp/valid/speech_shape', 'exp/asr_stats_raw_cy_bpe150_sp/valid/text_shape.bpe'], sort_in_batch=descending, sort_batch=descending)
[islpc50:0/3] 2022-06-06 13:44:26,935 (abs_task:1527) INFO: [valid] mini-batch sizes summary: N-batch=977, mean=3.0, min=3, max=4
[islpc50:0/3] 2022-06-06 13:44:26,972 (abs_task:1525) INFO: [plot_att] dataset:
ESPnetDataset(
speech: {"path": "dump/raw/dev_cy/wav.scp", "type": "sound"}
text: {"path": "dump/raw/dev_cy/text", "type": "text"}
preprocess: <espnet2.train.preprocessor.CommonPreprocessor object at 0x7f27e17ac970>)
[islpc50:0/3] 2022-06-06 13:44:26,972 (abs_task:1526) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=2933, batch_size=1, key_file=exp/asr_stats_raw_cy_bpe150_sp/valid/speech_shape,
[islpc50:0/3] 2022-06-06 13:44:26,972 (abs_task:1527) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1
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islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 0(=19000) and dev 1(=1a000)
islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 1(=1a000) and dev 0(=19000)
islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 0(=19000) and dev 1(=1a000)
islpc50:1025747:1025833 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0
islpc50:1025747:1025833 [1] NCCL INFO Setting affinity for GPU 1 to ffffff
islpc50:1025748:1025834 [2] NCCL INFO Trees [0] -1/-1/-1->2->1 [1] -1/-1/-1->2->1
islpc50:1025746:1025832 [0] NCCL INFO Channel 00/02 : 0 1 2
islpc50:1025748:1025834 [2] NCCL INFO Setting affinity for GPU 2 to ffffff
islpc50:1025746:1025832 [0] NCCL INFO Channel 01/02 : 0 1 2
islpc50:1025746:1025832 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1
islpc50:1025746:1025832 [0] NCCL INFO Setting affinity for GPU 0 to ffffff
islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 1(=1a000) and dev 0(=19000)
islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 1(=1a000) and dev 0(=19000)
islpc50:1025747:1025833 [1] NCCL INFO Channel 00 : 1[1a000] -> 2[67000] via direct shared memory
islpc50:1025747:1025833 [1] NCCL INFO Channel 01 : 1[1a000] -> 2[67000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Could not enable P2P between dev 0(=19000) and dev 1(=1a000)
islpc50:1025748:1025834 [2] NCCL INFO Channel 00 : 2[67000] -> 0[19000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Channel 00 : 0[19000] -> 1[1a000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Could not enable P2P between dev 0(=19000) and dev 1(=1a000)
islpc50:1025748:1025834 [2] NCCL INFO Channel 01 : 2[67000] -> 0[19000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Channel 01 : 0[19000] -> 1[1a000] via direct shared memory
islpc50:1025748:1025834 [2] NCCL INFO Connected all rings
islpc50:1025747:1025833 [1] NCCL INFO Connected all rings
islpc50:1025746:1025832 [0] NCCL INFO Connected all rings
islpc50:1025748:1025834 [2] NCCL INFO Channel 00 : 2[67000] -> 1[1a000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Could not enable P2P between dev 0(=19000) and dev 1(=1a000)
islpc50:1025748:1025834 [2] NCCL INFO Channel 01 : 2[67000] -> 1[1a000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Could not enable P2P between dev 0(=19000) and dev 1(=1a000)
islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 1(=1a000) and dev 0(=19000)
islpc50:1025747:1025833 [1] NCCL INFO Channel 00 : 1[1a000] -> 0[19000] via direct shared memory
islpc50:1025747:1025833 [1] NCCL INFO Could not enable P2P between dev 1(=1a000) and dev 0(=19000)
islpc50:1025747:1025833 [1] NCCL INFO Channel 01 : 1[1a000] -> 0[19000] via direct shared memory
islpc50:1025746:1025832 [0] NCCL INFO Connected all trees
islpc50:1025746:1025832 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 8/8/512
islpc50:1025746:1025832 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
islpc50:1025747:1025833 [1] NCCL INFO Connected all trees
islpc50:1025747:1025833 [1] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 8/8/512
islpc50:1025747:1025833 [1] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
islpc50:1025748:1025834 [2] NCCL INFO Connected all trees
islpc50:1025748:1025834 [2] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 8/8/512
islpc50:1025748:1025834 [2] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
islpc50:1025746:1025832 [0] NCCL INFO comm 0x7f27d0002fb0 rank 0 nranks 3 cudaDev 0 busId 19000 - Init COMPLETE
islpc50:1025747:1025833 [1] NCCL INFO comm 0x7f6e2c002fb0 rank 1 nranks 3 cudaDev 1 busId 1a000 - Init COMPLETE
islpc50:1025748:1025834 [2] NCCL INFO comm 0x7f6680002fb0 rank 2 nranks 3 cudaDev 2 busId 67000 - Init COMPLETE
islpc50:1025746:1025746 [0] NCCL INFO Launch mode Parallel
[islpc50:0/3] 2022-06-06 13:44:27,683 (trainer:280) INFO: 1/18epoch started
[s3prl.upstream.experts] Warning: can not import s3prl.upstream.byol_a.expert: No module named 'easydict'. Pass.
[s3prl.hub] Warning: can not import s3prl.upstream.byol_a.hubconf: No module named 'easydict'. Please see upstream/byol_a/README.md
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.quesst14_dtw.expert: No module named 'dtw'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.separation_stft.expert: No module named 'asteroid'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.enhancement_stft.expert: No module named 'asteroid'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.speech_commands.expert: No module named 'catalyst'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.a2a-vc-vctk.expert: No module named 'resemblyzer'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.voxceleb2_ge2e.expert: No module named 'sox'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.sv_voxceleb1.expert: No module named 'sox'. Pass.
Using cache found in ./hub/s3prl_cache/1c76d6e88090f01736036b28dc995fef583f47f42662d55286332557f957609f
for https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt
>> inserted adapters to the following layers: 0, 1, 2
* original model weights: 317,390,592
* new model weights - all: 319,757,184
* new model weights - trainable: 2,366,592 ( 0.75% of original model)
[s3prl.upstream.experts] Warning: can not import s3prl.upstream.byol_a.expert: No module named 'easydict'. Pass.
[s3prl.hub] Warning: can not import s3prl.upstream.byol_a.hubconf: No module named 'easydict'. Please see upstream/byol_a/README.md
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.quesst14_dtw.expert: No module named 'dtw'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.separation_stft.expert: No module named 'asteroid'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.enhancement_stft.expert: No module named 'asteroid'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.speech_commands.expert: No module named 'catalyst'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.a2a-vc-vctk.expert: No module named 'resemblyzer'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.voxceleb2_ge2e.expert: No module named 'sox'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.sv_voxceleb1.expert: No module named 'sox'. Pass.
Using cache found in ./hub/s3prl_cache/1c76d6e88090f01736036b28dc995fef583f47f42662d55286332557f957609f
for https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt
>> inserted adapters to the following layers: 0, 1, 2
* original model weights: 317,390,592
* new model weights - all: 319,757,184
* new model weights - trainable: 2,366,592 ( 0.75% of original model)
[s3prl.upstream.experts] Warning: can not import s3prl.upstream.byol_a.expert: No module named 'easydict'. Pass.
[s3prl.hub] Warning: can not import s3prl.upstream.byol_a.hubconf: No module named 'easydict'. Please see upstream/byol_a/README.md
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.quesst14_dtw.expert: No module named 'dtw'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.separation_stft.expert: No module named 'asteroid'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.enhancement_stft.expert: No module named 'asteroid'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.speech_commands.expert: No module named 'catalyst'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.a2a-vc-vctk.expert: No module named 'resemblyzer'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.voxceleb2_ge2e.expert: No module named 'sox'. Pass.
[s3prl.downstream.experts] Warning: can not import s3prl.downstream.sv_voxceleb1.expert: No module named 'sox'. Pass.
Using cache found in ./hub/s3prl_cache/1c76d6e88090f01736036b28dc995fef583f47f42662d55286332557f957609f
for https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt
>> inserted adapters to the following layers: 0, 1, 2
* original model weights: 317,390,592
* new model weights - all: 319,757,184
* new model weights - trainable: 2,366,592 ( 0.75% of original model)
Process SpawnProcess-1:
Traceback (most recent call last):
File "/usr/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/usr/lib/python3.8/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/project/ocean/junweih/espnet/espnet2/tasks/abs_task.py", line 1315, in main_worker
cls.trainer.run(
File "/project/ocean/junweih/espnet/espnet2/train/trainer.py", line 286, in run
all_steps_are_invalid = cls.train_one_epoch(
File "/project/ocean/junweih/espnet/espnet2/train/trainer.py", line 652, in train_one_epoch
optimizer.step()
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 65, in wrapper
return wrapped(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper
return func(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/adam.py", line 133, in step
F.adam(params_with_grad,
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/_functional.py", line 94, in adam
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)
RuntimeError: CUDA out of memory. Tried to allocate 1020.00 MiB (GPU 0; 10.76 GiB total capacity; 8.37 GiB already allocated; 554.56 MiB free; 8.68 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Process SpawnProcess-2:
Traceback (most recent call last):
File "/usr/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/usr/lib/python3.8/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/project/ocean/junweih/espnet/espnet2/tasks/abs_task.py", line 1315, in main_worker
cls.trainer.run(
File "/project/ocean/junweih/espnet/espnet2/train/trainer.py", line 286, in run
all_steps_are_invalid = cls.train_one_epoch(
File "/project/ocean/junweih/espnet/espnet2/train/trainer.py", line 652, in train_one_epoch
optimizer.step()
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 65, in wrapper
return wrapped(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper
return func(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/adam.py", line 133, in step
F.adam(params_with_grad,
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/_functional.py", line 94, in adam
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)
RuntimeError: CUDA out of memory. Tried to allocate 1020.00 MiB (GPU 1; 10.76 GiB total capacity; 8.37 GiB already allocated; 554.56 MiB free; 8.68 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Process SpawnProcess-3:
Traceback (most recent call last):
File "/usr/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/usr/lib/python3.8/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/project/ocean/junweih/espnet/espnet2/tasks/abs_task.py", line 1315, in main_worker
cls.trainer.run(
File "/project/ocean/junweih/espnet/espnet2/train/trainer.py", line 286, in run
all_steps_are_invalid = cls.train_one_epoch(
File "/project/ocean/junweih/espnet/espnet2/train/trainer.py", line 652, in train_one_epoch
optimizer.step()
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 65, in wrapper
return wrapped(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper
return func(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/adam.py", line 133, in step
F.adam(params_with_grad,
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/optim/_functional.py", line 94, in adam
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)
RuntimeError: CUDA out of memory. Tried to allocate 1020.00 MiB (GPU 2; 10.76 GiB total capacity; 8.37 GiB already allocated; 554.56 MiB free; 8.68 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Traceback (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/project/ocean/junweih/espnet/espnet2/bin/asr_train.py", line 23, in <module>
main()
File "/project/ocean/junweih/espnet/espnet2/bin/asr_train.py", line 19, in main
ASRTask.main(cmd=cmd)
File "/project/ocean/junweih/espnet/espnet2/tasks/abs_task.py", line 1069, in main
while not ProcessContext(processes, error_queues).join():
File "/project/ocean/junweih/espnet/tools/python_user_base/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 139, in join
raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 2 terminated with exit code 1
/usr/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 3 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
# Accounting: time=44 threads=1
# Ended (code 1) at Mon Jun 6 13:44:38 EDT 2022, elapsed time 44 seconds
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