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2026-01-14 00:19:21 | INFO | espnet3 | === ESPnet3 run started: 2026-01-14T00:19:21.364830 ===
2026-01-14 00:19:21 | INFO | espnet3 | Command: /data/user_data/msomeki/espnet3/.venv/bin/python3 run.py --stages create_dataset train_tokenizer collect_stats train infer measure --train_config conf/train.yaml --infer_config conf/infer.yaml --measure_config conf/measure.yaml
2026-01-14 00:19:21 | INFO | espnet3 | Python: 3.11.13 (main, Aug 18 2025, 19:19:13) [Clang 20.1.4 ]
2026-01-14 00:19:21 | INFO | espnet3 | Working directory: /home/msomeki/00_systems/espnet3/egs3/mini_an4/asr
2026-01-14 00:19:21 | INFO | espnet3 | train config: /home/msomeki/00_systems/espnet3/egs3/mini_an4/asr/conf/train_asr_rnn_data_aug_debug.yaml
2026-01-14 00:19:21 | INFO | espnet3 | infer config: /home/msomeki/00_systems/espnet3/egs3/mini_an4/asr/conf/infer.yaml
2026-01-14 00:19:21 | INFO | espnet3 | measure config: /home/msomeki/00_systems/espnet3/egs3/mini_an4/asr/conf/measure.yaml
2026-01-14 00:19:21 | INFO | espnet3 | Git: commit=8509faad9811b58d5024f29fb9d68ffb026b5e73, short_commit=8509faad9, branch=espnet3/recipe/asr_ls100, worktree=dirty
2026-01-14 00:19:21 | INFO | espnet3 | Cluster env:
OMPI_MCA_plm_slurm_args=--external-launcher
SLURM_CLUSTER_NAME=babel
SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
SLURM_CPUS_ON_NODE=1
SLURM_CPUS_PER_TASK=1
SLURM_CPU_BIND=quiet,mask_cpu:0x0000000000010000
SLURM_CPU_BIND_LIST=0x0000000000010000
SLURM_CPU_BIND_TYPE=mask_cpu:
SLURM_CPU_BIND_VERBOSE=quiet
SLURM_DISTRIBUTION=cyclic,pack
SLURM_GTIDS=0
SLURM_JOBID=6122041
SLURM_JOB_ACCOUNT=swatanab
SLURM_JOB_CPUS_PER_NODE=1
SLURM_JOB_END_TIME=1768401875
SLURM_JOB_GID=2709140
SLURM_JOB_GROUP=msomeki
SLURM_JOB_ID=6122041
SLURM_JOB_NAME=bash
SLURM_JOB_NODELIST=babel-o9-16
SLURM_JOB_NUM_NODES=1
SLURM_JOB_PARTITION=debug
SLURM_JOB_QOS=debug_qos
SLURM_JOB_START_TIME=1768358675
SLURM_JOB_UID=2709140
SLURM_JOB_USER=msomeki
SLURM_LAUNCH_NODE_IPADDR=172.16.1.2
SLURM_LOCALID=0
SLURM_MEM_PER_NODE=4096
SLURM_NNODES=1
SLURM_NODEID=0
SLURM_NODELIST=babel-o9-16
SLURM_NPROCS=1
SLURM_NTASKS=1
SLURM_NTASKS_PER_NODE=1
SLURM_PRIO_PROCESS=0
SLURM_PROCID=0
SLURM_PTY_PORT=40465
SLURM_PTY_WIN_COL=112
SLURM_PTY_WIN_ROW=61
SLURM_SCRIPT_CONTEXT=prolog_task
SLURM_SRUN_COMM_HOST=172.16.1.2
SLURM_SRUN_COMM_PORT=33789
SLURM_STEPID=0
SLURM_STEP_ID=0
SLURM_STEP_LAUNCHER_PORT=33789
SLURM_STEP_NODELIST=babel-o9-16
SLURM_STEP_NUM_NODES=1
SLURM_STEP_NUM_TASKS=1
SLURM_STEP_TASKS_PER_NODE=1
SLURM_SUBMIT_DIR=/home/msomeki/00_systems/espnet3
SLURM_SUBMIT_HOST=login1
SLURM_TASKS_PER_NODE=1
SLURM_TASK_PID=3334910
SLURM_TOPOLOGY_ADDR=babel-o9-16
SLURM_TOPOLOGY_ADDR_PATTERN=node
SLURM_TRES_PER_TASK=cpu=1
SLURM_UMASK=0027
2026-01-14 00:19:21 | INFO | espnet3 | Runtime env:
LD_LIBRARY_PATH=/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:/home/msomeki/00_systems/espnet3/tools/espeak-ng/lib:/home/msomeki/00_systems/espnet3/tools/lib:/home/msomeki/00_systems/espnet3/tools/lib64:
PATH=/home/msomeki/00_systems/espnet3/tools/ffmpeg-release:/home/msomeki/00_systems/espnet3/tools/festival/bin:/home/msomeki/00_systems/espnet3/tools/MBROLA/Bin:/home/msomeki/00_systems/espnet3/tools/espeak-ng/bin:/home/msomeki/00_systems/espnet3/tools/BeamformIt:/home/msomeki/00_systems/espnet3/tools/kenlm/build/bin:/home/msomeki/00_systems/espnet3/tools/PESQ/P862_annex_A_2005_CD/source:/home/msomeki/00_systems/espnet3/tools/nkf/nkf-2.1.4:/home/msomeki/00_systems/espnet3/tools/moses/scripts/tokenizer:/home/msomeki/00_systems/espnet3/tools/moses/scripts/generic:/home/msomeki/00_systems/espnet3/tools/tools/moses/scripts/recaser:/home/msomeki/00_systems/espnet3/tools/moses/scripts/training:/home/msomeki/00_systems/espnet3/tools/mwerSegmenter:/home/msomeki/00_systems/espnet3/tools/sctk/bin:/home/msomeki/00_systems/espnet3/tools/sph2pipe:/home/msomeki/00_systems/espnet3/tools/sentencepiece_commands:/data/user_data/msomeki/espnet3/.venv/bin:/home/msomeki/.pixi/bin:/home/msomeki/local/bin:/home/msomeki/utils:/usr/share/Modules/bin:/home/msomeki/00_systems/espnet3/tools/ffmpeg-release:/home/msomeki/00_systems/espnet3/tools/festival/bin:/home/msomeki/00_systems/espnet3/tools/MBROLA/Bin:/home/msomeki/00_systems/espnet3/tools/espeak-ng/bin:/home/msomeki/00_systems/espnet3/tools/BeamformIt:/home/msomeki/00_systems/espnet3/tools/kenlm/build/bin:/home/msomeki/00_systems/espnet3/tools/PESQ/P862_annex_A_2005_CD/source:/home/msomeki/00_systems/espnet3/tools/nkf/nkf-2.1.4:/home/msomeki/00_systems/espnet3/tools/moses/scripts/tokenizer:/home/msomeki/00_systems/espnet3/tools/moses/scripts/generic:/home/msomeki/00_systems/espnet3/tools/tools/moses/scripts/recaser:/home/msomeki/00_systems/espnet3/tools/moses/scripts/training:/home/msomeki/00_systems/espnet3/tools/mwerSegmenter:/home/msomeki/00_systems/espnet3/tools/sctk/bin:/home/msomeki/00_systems/espnet3/tools/sph2pipe:/home/msomeki/00_systems/espnet3/tools/sentencepiece_commands:/home/msomeki/00_systems/espnet3/tools/ffmpeg-release:/home/msomeki/00_systems/espnet3/tools/festival/bin:/home/msomeki/00_systems/espnet3/tools/MBROLA/Bin:/home/msomeki/00_systems/espnet3/tools/espeak-ng/bin:/home/msomeki/00_systems/espnet3/tools/BeamformIt:/home/msomeki/00_systems/espnet3/tools/kenlm/build/bin:/home/msomeki/00_systems/espnet3/tools/PESQ/P862_annex_A_2005_CD/source:/home/msomeki/00_systems/espnet3/tools/nkf/nkf-2.1.4:/home/msomeki/00_systems/espnet3/tools/moses/scripts/tokenizer:/home/msomeki/00_systems/espnet3/tools/moses/scripts/generic:/home/msomeki/00_systems/espnet3/tools/tools/moses/scripts/recaser:/home/msomeki/00_systems/espnet3/tools/moses/scripts/training:/home/msomeki/00_systems/espnet3/tools/mwerSegmenter:/home/msomeki/00_systems/espnet3/tools/sctk/bin:/home/msomeki/00_systems/espnet3/tools/sph2pipe:/home/msomeki/00_systems/espnet3/tools/sentencepiece_commands:/home/msomeki/.pixi/bin:/home/msomeki/local/bin:/home/msomeki/utils:/home/msomeki/.local/bin:/home/msomeki/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin
PYTHONPATH=/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:../../../:../../TEMPLATE/asr:/home/msomeki/00_systems/espnet3/egs3/mini_an4/asr:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3:/home/msomeki/00_systems/espnet3/tools/RawNet/python/RawNet3/models:
2026-01-14 00:19:21 | INFO | espnet3 | Train config content:
num_device: 1
num_nodes: 1
task: espnet3.systems.asr.task.ASRTask
recipe_dir: .
data_dir: ./data
exp_tag: train_asr_rnn_data_aug_debug
exp_dir: ./exp/train_asr_rnn_data_aug_debug
stats_dir: ./exp/stats
decode_dir: ./exp/train_asr_rnn_data_aug_debug/decode
dataset_dir: ./data/mini_an4
create_dataset:
func: src.create_dataset.create_dataset
dataset_dir: ./data/mini_an4
archive_path: ./../../egs2/mini_an4/asr1/downloads.tar.gz
dataset:
_target_: espnet3.components.data.data_organizer.DataOrganizer
train:
- name: train_nodev
dataset:
_target_: src.dataset.MiniAN4Dataset
manifest_path: ./data/mini_an4/manifest/train_nodev.tsv
valid:
- name: train_dev
dataset:
_target_: src.dataset.MiniAN4Dataset
manifest_path: ./data/mini_an4/manifest/train_dev.tsv
preprocessor:
_target_: espnet2.train.preprocessor.CommonPreprocessor
_convert_: all
fs: 16000
train: true
data_aug_effects:
- - 0.1
- contrast
- enhancement_amount: 75.0
- - 0.1
- highpass
- cutoff_freq: 5000
Q: 0.707
- - 0.1
- equalization
- center_freq: 1000
gain: 0
Q: 0.707
- - 0.1
- - - 0.3
- speed_perturb
- factor: 0.9
- - 0.3
- speed_perturb
- factor: 1.1
- - 0.3
- speed_perturb
- factor: 1.3
data_aug_num:
- 1
- 4
data_aug_prob: 1.0
token_type: bpe
token_list: ./data/bpe_30/tokens.txt
bpemodel: ./data/bpe_30/bpe.model
parallel:
env: local
n_workers: 1
options: {}
dataloader:
collate_fn:
_target_: espnet2.train.collate_fn.CommonCollateFn
int_pad_value: -1
train:
multiple_iterator: false
num_shards: 1
iter_factory:
_target_: espnet2.iterators.sequence_iter_factory.SequenceIterFactory
shuffle: true
collate_fn:
_target_: espnet2.train.collate_fn.CommonCollateFn
int_pad_value: -1
num_workers: 0
batches:
type: sorted
shape_files:
- ./exp/stats/train/feats_shape
batch_size: 2
batch_bins: 200000
valid:
multiple_iterator: false
num_shards: 1
iter_factory:
_target_: espnet2.iterators.sequence_iter_factory.SequenceIterFactory
shuffle: false
collate_fn:
_target_: espnet2.train.collate_fn.CommonCollateFn
int_pad_value: -1
batches:
type: sorted
shape_files:
- ./exp/stats/valid/feats_shape
batch_size: 2
batch_bins: 200000
optim:
_target_: torch.optim.Adam
lr: 0.001
weight_decay: 0.0
scheduler:
_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
mode: min
factor: 0.5
patience: 1
val_scheduler_criterion: valid/loss
best_model_criterion:
- - valid/acc
- 1
- max
trainer:
devices: 1
num_nodes: 1
accumulate_grad_batches: 1
check_val_every_n_epoch: 1
gradient_clip_val: 1.0
log_every_n_steps: 1
max_epochs: 1
limit_train_batches: 1
limit_val_batches: 1
precision: 32
reload_dataloaders_every_n_epochs: 1
use_distributed_sampler: false
tokenizer:
vocab_size: 30
character_coverage: 1.0
model_type: bpe
save_path: ./data/bpe_30
text_builder:
func: src.tokenizer.gather_training_text
manifest_path: ./data/mini_an4/manifest/train_nodev.tsv
model:
vocab_size: 30
token_list: ./data/bpe_30/tokens.txt
encoder: vgg_rnn
encoder_conf:
num_layers: 1
hidden_size: 2
output_size: 2
decoder: rnn
decoder_conf:
hidden_size: 2
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
2026-01-14 00:19:21 | INFO | espnet3 | Infer config content:
num_device: 1
num_nodes: 1
recipe_dir: .
data_dir: ./data
exp_tag: train_asr_rnn_data_aug_debug
exp_dir: ./exp/train_asr_rnn_data_aug_debug
stats_dir: ./exp/stats
decode_dir: ./exp/train_asr_rnn_data_aug_debug/decode
dataset_dir: ./data/mini_an4
dataset:
_target_: espnet3.components.data.data_organizer.DataOrganizer
test:
- name: test
dataset:
_target_: src.dataset.MiniAN4Dataset
manifest_path: ./data/mini_an4/manifest/test.tsv
parallel:
env: local
n_workers: 1
model:
_target_: espnet2.bin.asr_inference.Speech2Text
asr_train_config: ./exp/train_asr_rnn_data_aug_debug/config.yaml
asr_model_file: ./exp/train_asr_rnn_data_aug_debug/last.ckpt
beam_size: 1
ctc_weight: 0.3
tokenizer:
vocab_size: 30
character_coverage: 1.0
model_type: bpe
save_path: ./data/bpe_30
2026-01-14 00:19:21 | INFO | espnet3 | Measure config content:
recipe_dir: .
data_dir: ./data
exp_tag: train_asr_rnn_data_aug_debug
exp_dir: ./exp/train_asr_rnn_data_aug_debug
stats_dir: ./exp/stats
decode_dir: ./exp/train_asr_rnn_data_aug_debug/decode
dataset_dir: ./data/mini_an4
dataset:
_target_: espnet3.components.data.data_organizer.DataOrganizer
test:
- name: test
dataset:
_target_: src.dataset.MiniAN4Dataset
manifest_path: ./data/mini_an4/manifest/test.tsv
metrics:
- metric:
_target_: espnet3.systems.asr.metrics.wer.WER
clean_types: null
- metric:
_target_: espnet3.systems.asr.metrics.cer.CER
clean_types: null
2026-01-14 00:19:21 | INFO | espnet3 | === [START] stage: train ===
2026-01-14 00:19:21 | INFO | espnet3.systems.asr.system | ASRSystem.train(): starting training process
2026-01-14 00:19:21 | INFO | espnet3.systems.base.system | Training start | exp_dir=./exp/train_asr_rnn_data_aug_debug model=<unknown>
2026-01-14 00:19:22 | INFO | root | Vocabulary size: 30
2026-01-14 00:19:22 | INFO | espnet3.systems.base.train | Model:
ESPnetASRModel(
(frontend): DefaultFrontend(
(stft): Stft(n_fft=512, win_length=400, hop_length=160, center=True, normalized=False, onesided=True)
(frontend): Frontend()
(logmel): LogMel(sr=16000, n_fft=512, n_mels=80, fmin=0, fmax=8000.0, htk=False)
)
(normalize): UtteranceMVN(norm_means=True, norm_vars=False)
(encoder): VGGRNNEncoder(
(enc): ModuleList(
(0): VGG2L(
(conv1_1): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2_1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): RNNP(
(birnn0): LSTM(2560, 2, batch_first=True, bidirectional=True)
(bt0): Linear(in_features=4, out_features=2, bias=True)
)
)
)
(decoder): RNNDecoder(
(embed): Embedding(30, 2)
(dropout_emb): Dropout(p=0.0, inplace=False)
(decoder): ModuleList(
(0): LSTMCell(4, 2)
)
(dropout_dec): ModuleList(
(0): Dropout(p=0.0, inplace=False)
)
(output): Linear(in_features=2, out_features=30, bias=True)
(att_list): ModuleList(
(0): AttLoc(
(mlp_enc): Linear(in_features=2, out_features=320, bias=True)
(mlp_dec): Linear(in_features=2, out_features=320, bias=False)
(mlp_att): Linear(in_features=10, out_features=320, bias=False)
(loc_conv): Conv2d(1, 10, kernel_size=(1, 201), stride=(1, 1), padding=(0, 100), bias=False)
(gvec): Linear(in_features=320, out_features=1, bias=True)
)
)
)
(criterion_att): LabelSmoothingLoss(
(criterion): KLDivLoss()
)
(ctc): CTC(
(ctc_lo): Linear(in_features=2, out_features=30, bias=True)
(ctc_loss): CTCLoss()
)
)
2026-01-14 00:19:22 | WARNING | py.warnings | /data/user_data/msomeki/espnet3/.venv/lib/python3.11/site-packages/lightning/fabric/plugins/environments/slurm.py:204: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python3 run.py --stages create_dataset train_tokenizer coll ...
2026-01-14 00:19:22 | INFO | lightning.pytorch.utilities.rank_zero | GPU available: False, used: False
2026-01-14 00:19:22 | INFO | lightning.pytorch.utilities.rank_zero | TPU available: False, using: 0 TPU cores
2026-01-14 00:19:22 | INFO | lightning.pytorch.utilities.rank_zero | `Trainer(limit_train_batches=1)` was configured so 1 batch per epoch will be used.
2026-01-14 00:19:22 | INFO | lightning.pytorch.utilities.rank_zero | `Trainer(limit_val_batches=1)` was configured so 1 batch will be used.
2026-01-14 00:19:22 | WARNING | py.warnings | /data/user_data/msomeki/espnet3/.venv/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:881: Checkpoint directory /home/msomeki/00_systems/espnet3/egs3/mini_an4/asr/exp/train_asr_rnn_data_aug_debug exists and is not empty.
2026-01-14 00:19:22 | INFO | lightning.pytorch.callbacks.model_summary |
| Name | Type | Params | Mode | FLOPs
---------------------------------------------------------
0 | model | ESPnetASRModel | 307 K | train | 0
---------------------------------------------------------
307 K Trainable params
0 Non-trainable params
307 K Total params
1.230 Total estimated model params size (MB)
35 Modules in train mode
1 Modules in eval mode
0 Total Flops
2026-01-14 00:19:22 | WARNING | py.warnings | /home/msomeki/00_systems/espnet3/espnet2/asr/espnet_model.py:402: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with autocast(self.autocast_frontend, dtype=autocast_type):
2026-01-14 00:19:22 | WARNING | root | Using make_pad_mask with a list of lengths is not tracable. If you try to trace this function with type(lengths) == list, please change the type of lengths to torch.LongTensor.
2026-01-14 00:19:22 | WARNING | root | Using make_pad_mask with a list of lengths is not tracable. If you try to trace this function with type(lengths) == list, please change the type of lengths to torch.LongTensor.
2026-01-14 00:19:22 | WARNING | py.warnings | /data/user_data/msomeki/espnet3/.venv/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:534: Found 1 module(s) in eval mode at the start of training. This may lead to unexpected behavior during training. If this is intentional, you can ignore this warning.
2026-01-14 00:19:22 | WARNING | root | Using make_pad_mask with a list of lengths is not tracable. If you try to trace this function with type(lengths) == list, please change the type of lengths to torch.LongTensor.
2026-01-14 00:19:23 | WARNING | root | Using make_pad_mask with a list of lengths is not tracable. If you try to trace this function with type(lengths) == list, please change the type of lengths to torch.LongTensor.
2026-01-14 00:19:23 | WARNING | root | Using make_pad_mask with a list of lengths is not tracable. If you try to trace this function with type(lengths) == list, please change the type of lengths to torch.LongTensor.
2026-01-14 00:19:23 | WARNING | root | Using make_pad_mask with a list of lengths is not tracable. If you try to trace this function with type(lengths) == list, please change the type of lengths to torch.LongTensor.
2026-01-14 00:19:23 | INFO | lightning.pytorch.utilities.rank_zero | `Trainer.fit` stopped: `max_epochs=1` reached.
2026-01-14 00:19:23 | INFO | espnet3.systems.base.train | Training finished in 1.46s | exp_dir=./exp/train_asr_rnn_data_aug_debug model=None
2026-01-14 00:19:23 | INFO | espnet3 | === [DONE] stage: train (1.47s) ===
|