Upload 43 files (#1)

- Upload 43 files (297c44b8ce3b4be589aa860e75f4371ef3a928b6)


Co-authored-by: Axel Chiu <[email protected]>

ASR-model/TransformerLM_seg_char/env.log

@@ -0,0 +1,195 @@
+SpeechBrain system description
+==============================
+Python version:
+3.8.10 (default, Jun  2 2021, 10:49:15) 
+[GCC 9.4.0]
+==============================
+Installed Python packages:
+appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
+joblib==1.0.1
+jsonpatch==1.22
+jsonpointer==2.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.12
+jupyter-console==6.4.0
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keyring==18.0.1
+kiwisolver==1.3.1
+language-selector==0.1
+launchpadlib==1.10.13
+lazr.restfulclient==0.14.2
+lazr.uri==1.0.3
+macaroonbakery==1.3.1
+MarkupSafe==1.1.0
+matplotlib==3.4.2
+matplotlib-inline==0.1.2
+mccabe==0.6.1
+mistune==0.8.4
+more-itertools==4.2.0
+multiprocess==0.70.12.2
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+netifaces==0.10.4
+nodeenv==1.6.0
+notebook==6.4.0
+numpy==1.21.2
+nvidia-ml-py3==7.352.0
+oauthlib==3.1.0
+packaging==21.0
+pandas==1.3.0
+pandocfilters==1.4.3
+parso==0.8.2
+pathspec==0.9.0
+pexpect==4.6.0
+pickleshare==0.7.5
+Pillow==8.3.1
+platformdirs==2.0.2
+pluggy==0.13.1
+pre-commit==2.15.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.6.1
+psutil==5.8.0
+ptyprocess==0.7.0
+py==1.10.0
+pyarrow==5.0.0
+pyasn1==0.4.2
+pyasn1-modules==0.2.1
+pycairo==1.16.2
+pycodestyle==2.5.0
+pycparser==2.20
+pycups==1.9.73
+pyflakes==2.1.1
+Pygments==2.9.0
+PyGObject==3.36.0
+PyHamcrest==1.9.0
+PyJWT==1.7.1
+pymacaroons==0.13.0
+PyMySQL==1.0.2
+PyNaCl==1.3.0
+pyOpenSSL==19.0.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.15.5
+pyserial==3.4
+pytest==5.4.1
+python-apt==2.0.0+ubuntu0.20.4.5
+python-dateutil==2.8.2
+python-debian===0.1.36ubuntu1
+pytube==10.9.3
+pytz==2019.3
+PyYAML==5.3.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.22.0
+requests-unixsocket==0.2.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scipy==1.7.1
+screen-resolution-extra==0.0.0
+SecretStorage==2.3.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+service-identity==18.1.0
+simplejson==3.16.0
+six==1.14.0
+sos==4.1
+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
+terminado==0.10.1
+testpath==0.5.0
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+Twisted==18.9.0
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
+urllib3==1.25.8
+virtualenv==20.6.0
+wadllib==1.3.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+==============================
+Git revision:
+e3e51338
+==============================
+Cuda version:
+10.2

ASR-model/TransformerLM_seg_char/hyperparams.yaml

@@ -0,0 +1,95 @@
+# Generated 2021-10-05 from:
+# /mnt/md0/user_wayne/speechbrain/recipes/MATBN/LM/hparams/TransformerLM_seg_char.yaml
+# yamllint disable
+output_folder: results/TransformerLM_seg_char
+save_folder: results/TransformerLM_seg_char/save
+train_log: results/TransformerLM_seg_char/train_log.txt
+num_workers: 4
+
+data_folder: results/prepare_seg
+
+tokenizer_file: results/tokenizer_seg_bpe5k_char/5000_char.model
+
+tokenizer: &id001 !new:sentencepiece.SentencePieceProcessor
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+  collect_in: results/TransformerLM_seg_char/tokenizer
+  loadables:
+    tokenizer: *id001
+  paths:
+    tokenizer: results/tokenizer_seg_bpe5k_char/5000_char.model
+
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/TransformerLM_seg_char/train_log.txt
+
+# Training parameters
+number_of_epochs: 20
+batch_size: 64
+lr: 1
+accumulation_steps: 2
+ckpt_interval_minutes: 15
+
+epoch_counter: &id004 !new:speechbrain.utils.epoch_loop.EpochCounter
+
+  limit: 20
+
+# Dataloader options
+train_dataloader_opts:
+  batch_size: 64
+  num_workers: 4
+  shuffle: true
+  pin_memory: true
+
+valid_dataloader_opts:
+  batch_size: 64
+  num_workers: 4
+
+test_dataloader_opts:
+  batch_size: 64
+  num_workers: 4
+
+# Model parameters
+d_model: 576
+
+# Outputs
+output_neurons: 5000
+blank_index: 0
+bos_index: 1
+eos_index: 2
+unk_index: 0
+pad_index: 0
+
+model: &id002 !new:speechbrain.lobes.models.transformer.TransformerLM.TransformerLM
+
+  vocab: 5000
+  d_model: 576
+  nhead: 6
+  num_encoder_layers: 6
+  num_decoder_layers: 0
+  d_ffn: 1538
+  dropout: 0.2
+  activation: !name:torch.nn.GELU
+  normalize_before: false
+
+modules:
+  model: *id002
+lr_annealing: &id003 !new:speechbrain.nnet.schedulers.NoamScheduler
+  lr_initial: 1
+  n_warmup_steps: 1000
+  model_size: 576
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/TransformerLM_seg_char/save
+  recoverables:
+    model: *id002
+    scheduler: *id003
+    counter: *id004
+log_softmax: !new:speechbrain.nnet.activations.Softmax
+  apply_log: true
+
+optimizer: !name:torch.optim.Adam
+  lr: 0
+  betas: (0.9, 0.98)
+  eps: 0.000000001
+
+compute_cost: !name:speechbrain.nnet.losses.nll_loss

ASR-model/TransformerLM_seg_char/log.txt

@@ -0,0 +1,276 @@
+2021-10-05 19:59:24,051 - speechbrain.core - INFO - Beginning experiment!
+2021-10-05 19:59:24,052 - speechbrain.core - INFO - Experiment folder: results/TransformerLM_seg_char
+2021-10-05 19:59:24,902 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
+joblib==1.0.1
+jsonpatch==1.22
+jsonpointer==2.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.12
+jupyter-console==6.4.0
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keyring==18.0.1
+kiwisolver==1.3.1
+language-selector==0.1
+launchpadlib==1.10.13
+lazr.restfulclient==0.14.2
+lazr.uri==1.0.3
+macaroonbakery==1.3.1
+MarkupSafe==1.1.0
+matplotlib==3.4.2
+matplotlib-inline==0.1.2
+mccabe==0.6.1
+mistune==0.8.4
+more-itertools==4.2.0
+multiprocess==0.70.12.2
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+netifaces==0.10.4
+nodeenv==1.6.0
+notebook==6.4.0
+numpy==1.21.2
+nvidia-ml-py3==7.352.0
+oauthlib==3.1.0
+packaging==21.0
+pandas==1.3.0
+pandocfilters==1.4.3
+parso==0.8.2
+pathspec==0.9.0
+pexpect==4.6.0
+pickleshare==0.7.5
+Pillow==8.3.1
+platformdirs==2.0.2
+pluggy==0.13.1
+pre-commit==2.15.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.6.1
+psutil==5.8.0
+ptyprocess==0.7.0
+py==1.10.0
+pyarrow==5.0.0
+pyasn1==0.4.2
+pyasn1-modules==0.2.1
+pycairo==1.16.2
+pycodestyle==2.5.0
+pycparser==2.20
+pycups==1.9.73
+pyflakes==2.1.1
+Pygments==2.9.0
+PyGObject==3.36.0
+PyHamcrest==1.9.0
+PyJWT==1.7.1
+pymacaroons==0.13.0
+PyMySQL==1.0.2
+PyNaCl==1.3.0
+pyOpenSSL==19.0.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.15.5
+pyserial==3.4
+pytest==5.4.1
+python-apt==2.0.0+ubuntu0.20.4.5
+python-dateutil==2.8.2
+python-debian===0.1.36ubuntu1
+pytube==10.9.3
+pytz==2019.3
+PyYAML==5.3.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.22.0
+requests-unixsocket==0.2.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scipy==1.7.1
+screen-resolution-extra==0.0.0
+SecretStorage==2.3.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+service-identity==18.1.0
+simplejson==3.16.0
+six==1.14.0
+sos==4.1
+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
+terminado==0.10.1
+testpath==0.5.0
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+Twisted==18.9.0
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
+urllib3==1.25.8
+virtualenv==20.6.0
+wadllib==1.3.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+
+
+2021-10-05 19:59:24,907 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-10-05 19:59:24,936 - speechbrain.utils.parameter_transfer - DEBUG - Collecting files (or symlinks) for pretraining in results/TransformerLM_seg_char/tokenizer.
+2021-10-05 19:59:24,937 - speechbrain.pretrained.fetching - INFO - Fetch 5000_char.model: Linking to local file in /mnt/md0/user_wayne/speechbrain/recipes/MATBN/results/tokenizer_seg_bpe5k_char/5000_char.model.
+2021-10-05 19:59:24,937 - speechbrain.utils.parameter_transfer - INFO - Loading pretrained files for: tokenizer
+2021-10-05 19:59:25,190 - speechbrain.core - INFO - Info: ckpt_interval_minutes arg from hparam file is used
+2021-10-05 19:59:27,052 - speechbrain.core - INFO - 24.7M trainable parameters in LM
+2021-10-05 19:59:27,053 - speechbrain.utils.checkpoints - INFO - Would load a checkpoint here, but none found yet.
+2021-10-05 19:59:27,053 - speechbrain.utils.epoch_loop - INFO - Going into epoch 1
+2021-10-05 20:03:07,476 - speechbrain.utils.train_logger - INFO - epoch: 1, lr: 4.99e-04 - train loss: 4.23 - valid loss: 3.19
+2021-10-05 20:03:07,790 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-03-07+00
+2021-10-05 20:03:07,792 - speechbrain.utils.epoch_loop - INFO - Going into epoch 2
+2021-10-05 20:06:50,852 - speechbrain.utils.train_logger - INFO - epoch: 2, lr: 1.00e-03 - train loss: 2.95 - valid loss: 2.77
+2021-10-05 20:06:51,157 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-06-50+00
+2021-10-05 20:06:51,186 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-03-07+00
+2021-10-05 20:06:51,186 - speechbrain.utils.epoch_loop - INFO - Going into epoch 3
+2021-10-05 20:10:34,626 - speechbrain.utils.train_logger - INFO - epoch: 3, lr: 1.23e-03 - train loss: 2.73 - valid loss: 2.64
+2021-10-05 20:10:34,936 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-10-34+00
+2021-10-05 20:10:34,967 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-06-50+00
+2021-10-05 20:10:34,967 - speechbrain.utils.epoch_loop - INFO - Going into epoch 4
+2021-10-05 20:14:18,480 - speechbrain.utils.train_logger - INFO - epoch: 4, lr: 1.07e-03 - train loss: 2.60 - valid loss: 2.54
+2021-10-05 20:14:18,787 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-14-18+00
+2021-10-05 20:14:18,820 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-10-34+00
+2021-10-05 20:14:18,821 - speechbrain.utils.epoch_loop - INFO - Going into epoch 5
+2021-10-05 20:18:02,905 - speechbrain.utils.train_logger - INFO - epoch: 5, lr: 9.56e-04 - train loss: 2.49 - valid loss: 2.48
+2021-10-05 20:18:03,213 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-18-02+00
+2021-10-05 20:18:03,247 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-14-18+00
+2021-10-05 20:18:03,247 - speechbrain.utils.epoch_loop - INFO - Going into epoch 6
+2021-10-05 20:21:47,703 - speechbrain.utils.train_logger - INFO - epoch: 6, lr: 8.73e-04 - train loss: 2.41 - valid loss: 2.42
+2021-10-05 20:21:48,011 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-21-47+00
+2021-10-05 20:21:48,048 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-18-02+00
+2021-10-05 20:21:48,048 - speechbrain.utils.epoch_loop - INFO - Going into epoch 7
+2021-10-05 20:25:32,303 - speechbrain.utils.train_logger - INFO - epoch: 7, lr: 8.08e-04 - train loss: 2.35 - valid loss: 2.38
+2021-10-05 20:25:32,612 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-25-32+00
+2021-10-05 20:25:32,650 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-21-47+00
+2021-10-05 20:25:32,651 - speechbrain.utils.epoch_loop - INFO - Going into epoch 8
+2021-10-05 20:29:17,400 - speechbrain.utils.train_logger - INFO - epoch: 8, lr: 7.56e-04 - train loss: 2.29 - valid loss: 2.35
+2021-10-05 20:29:17,706 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-29-17+00
+2021-10-05 20:29:17,745 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-25-32+00
+2021-10-05 20:29:17,746 - speechbrain.utils.epoch_loop - INFO - Going into epoch 9
+2021-10-05 20:33:02,633 - speechbrain.utils.train_logger - INFO - epoch: 9, lr: 7.13e-04 - train loss: 2.24 - valid loss: 2.33
+2021-10-05 20:33:02,943 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-33-02+00
+2021-10-05 20:33:02,985 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-29-17+00
+2021-10-05 20:33:02,985 - speechbrain.utils.epoch_loop - INFO - Going into epoch 10
+2021-10-05 20:36:47,808 - speechbrain.utils.train_logger - INFO - epoch: 10, lr: 6.76e-04 - train loss: 2.19 - valid loss: 2.32
+2021-10-05 20:36:48,118 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-36-47+00
+2021-10-05 20:36:48,161 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-33-02+00
+2021-10-05 20:36:48,161 - speechbrain.utils.epoch_loop - INFO - Going into epoch 11
+2021-10-05 20:40:33,763 - speechbrain.utils.train_logger - INFO - epoch: 11, lr: 6.45e-04 - train loss: 2.15 - valid loss: 2.30
+2021-10-05 20:40:34,072 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-40-33+00
+2021-10-05 20:40:34,117 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-36-47+00
+2021-10-05 20:40:34,117 - speechbrain.utils.epoch_loop - INFO - Going into epoch 12
+2021-10-05 20:44:19,873 - speechbrain.utils.train_logger - INFO - epoch: 12, lr: 6.17e-04 - train loss: 2.11 - valid loss: 2.30
+2021-10-05 20:44:20,181 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-44-19+00
+2021-10-05 20:44:20,228 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-40-33+00
+2021-10-05 20:44:20,228 - speechbrain.utils.epoch_loop - INFO - Going into epoch 13
+2021-10-05 20:48:05,429 - speechbrain.utils.train_logger - INFO - epoch: 13, lr: 5.93e-04 - train loss: 2.08 - valid loss: 2.29
+2021-10-05 20:48:05,739 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-48-05+00
+2021-10-05 20:48:05,788 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-44-19+00
+2021-10-05 20:48:05,789 - speechbrain.utils.epoch_loop - INFO - Going into epoch 14
+2021-10-05 20:51:51,235 - speechbrain.utils.train_logger - INFO - epoch: 14, lr: 5.71e-04 - train loss: 2.04 - valid loss: 2.29
+2021-10-05 20:51:51,545 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-51-51+00
+2021-10-05 20:51:51,596 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-48-05+00
+2021-10-05 20:51:51,596 - speechbrain.utils.epoch_loop - INFO - Going into epoch 15
+2021-10-05 20:55:37,264 - speechbrain.utils.train_logger - INFO - epoch: 15, lr: 5.52e-04 - train loss: 2.01 - valid loss: 2.28
+2021-10-05 20:55:37,574 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00
+2021-10-05 20:55:37,626 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-51-51+00
+2021-10-05 20:55:37,627 - speechbrain.utils.epoch_loop - INFO - Going into epoch 16
+2021-10-05 20:59:22,956 - speechbrain.utils.train_logger - INFO - epoch: 16, lr: 5.34e-04 - train loss: 1.98 - valid loss: 2.28
+2021-10-05 20:59:23,263 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-59-22+00
+2021-10-05 20:59:23,293 - speechbrain.utils.epoch_loop - INFO - Going into epoch 17
+2021-10-05 21:03:08,924 - speechbrain.utils.train_logger - INFO - epoch: 17, lr: 5.18e-04 - train loss: 1.95 - valid loss: 2.29
+2021-10-05 21:03:09,230 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-03-08+00
+2021-10-05 21:03:09,301 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-59-22+00
+2021-10-05 21:03:09,301 - speechbrain.utils.epoch_loop - INFO - Going into epoch 18
+2021-10-05 21:06:55,028 - speechbrain.utils.train_logger - INFO - epoch: 18, lr: 5.04e-04 - train loss: 1.92 - valid loss: 2.29
+2021-10-05 21:06:55,335 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-06-55+00
+2021-10-05 21:06:55,408 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-03-08+00
+2021-10-05 21:06:55,409 - speechbrain.utils.epoch_loop - INFO - Going into epoch 19
+2021-10-05 21:10:40,930 - speechbrain.utils.train_logger - INFO - epoch: 19, lr: 4.90e-04 - train loss: 1.90 - valid loss: 2.30
+2021-10-05 21:10:41,237 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-10-40+00
+2021-10-05 21:10:41,312 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-06-55+00
+2021-10-05 21:10:41,312 - speechbrain.utils.epoch_loop - INFO - Going into epoch 20
+2021-10-05 21:14:27,278 - speechbrain.utils.train_logger - INFO - epoch: 20, lr: 4.78e-04 - train loss: 1.87 - valid loss: 2.30
+2021-10-05 21:14:27,586 - speechbrain.utils.checkpoints - INFO - Saved an end-of-epoch checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-14-27+00
+2021-10-05 21:14:27,663 - speechbrain.utils.checkpoints - INFO - Deleted checkpoint in results/TransformerLM_seg_char/save/CKPT+2021-10-05+21-10-40+00
+2021-10-05 21:14:27,664 - speechbrain.utils.checkpoints - INFO - Loading a checkpoint from results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00
+2021-10-05 21:14:27,706 - root - DEBUG - SaveableDataLoader was requested to load a checkpoint, but the DataLoader has already been iterated. The DataLoader file will be ignored. This is normal in evaluation, when a checkpoint is loaded just to retrieve the best model.
+2021-10-05 21:14:31,760 - speechbrain.utils.train_logger - INFO - Epoch loaded: 15 - test loss: 2.68

ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/CKPT.yaml

@@ -0,0 +1,4 @@
+# yamllint disable
+end-of-epoch: true
+loss: 2.282208064707314
+unixtime: 1633438537.264693

ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/brain.ckpt

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ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/dataloader-TRAIN.ckpt

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ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/scheduler.ckpt

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ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+21-14-27+00/CKPT.yaml

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+end-of-epoch: true
+loss: 2.3031107332648304
+unixtime: 1633439667.279289

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ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+21-14-27+00/optimizer.ckpt

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ASR-model/TransformerLM_seg_char/save/CKPT+2021-10-05+21-14-27+00/scheduler.ckpt

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ASR-model/TransformerLM_seg_char/train.py

@@ -0,0 +1,150 @@
+import sys
+
+import torch
+import speechbrain as sb
+from speechbrain.dataio import dataset
+from speechbrain.utils.distributed import run_on_main
+from hyperpyyaml import load_hyperpyyaml
+
+
+class LM(sb.core.Brain):
+    def compute_forward(self, batch, stage):
+        batch = batch.to(self.device)
+        tokens_bos, _ = batch.tokens_bos
+        logits = self.hparams.model(tokens_bos)
+        pred = self.hparams.log_softmax(logits)
+        return pred
+
+    def compute_objectives(self, predictions, batch, stage):
+        batch = batch.to(self.device)
+        tokens_eos, tokens_len = batch.tokens_eos
+        loss = self.hparams.compute_cost(
+            predictions, tokens_eos, length=tokens_len
+        )
+        return loss
+
+    def fit_batch(self, batch):
+        predictions = self.compute_forward(batch, sb.Stage.TRAIN)
+        loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
+
+        (loss / self.hparams.accumulation_steps).backward()
+
+        if self.step % self.hparams.accumulation_steps == 0:
+            self.check_gradients(loss)
+
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+
+            if isinstance(
+                self.hparams.lr_annealing, sb.nnet.schedulers.NoamScheduler
+            ) or isinstance(
+                self.hparams.lr_annealing,
+                sb.nnet.schedulers.CyclicCosineScheduler,
+            ):
+                self.hparams.lr_annealing(self.optimizer)
+
+        return loss
+
+    def on_stage_end(self, stage, stage_loss, epoch):
+        stage_stats = {"loss": stage_loss}
+        if stage == sb.Stage.TRAIN:
+            self.train_stats = stage_stats
+
+        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+            if not (
+                isinstance(
+                    self.hparams.lr_annealing, sb.nnet.schedulers.NoamScheduler
+                )
+                or isinstance(
+                    self.hparams.lr_annealing,
+                    sb.nnet.schedulers.CyclicCosineScheduler,
+                )
+            ):
+                old_lr, new_lr = self.hparams.lr_annealing(stage_loss)
+                sb.nnet.schedulers.update_learning_rate(self.optimizer, new_lr)
+            else:
+                old_lr = self.hparams.lr_annealing.current_lr
+
+            self.hparams.train_logger.log_stats(
+                stats_meta={"epoch": epoch, "lr": old_lr},
+                train_stats=self.train_stats,
+                valid_stats=stage_stats,
+            )
+            self.checkpointer.save_and_keep_only(
+                meta=stage_stats, min_keys=["loss"],
+            )
+
+        if stage == sb.Stage.TEST and sb.utils.distributed.if_main_process():
+            self.hparams.train_logger.log_stats(
+                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
+                test_stats=stage_stats,
+            )
+
+
+def dataio_prepare(hparams):
+    @sb.utils.data_pipeline.takes("transcription")
+    @sb.utils.data_pipeline.provides(
+        "transcription", "tokens_bos", "tokens_eos"
+    )
+    def transcription_pipline(transcription):
+        yield transcription
+        tokens_list = hparams["tokenizer"].encode_as_ids(transcription)
+        tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
+        yield tokens_bos
+        tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
+        yield tokens_eos
+
+    data_folder = hparams["data_folder"]
+    datasets = {}
+    for dataset_name in ["train", "dev", "test"]:
+        json_path = f"{data_folder}/{dataset_name}.json"
+        datasets[dataset_name] = dataset.DynamicItemDataset.from_json(
+            json_path=json_path,
+            replacements={"data_root": data_folder},
+            dynamic_items=[transcription_pipline],
+            output_keys=["transcription", "tokens_bos", "tokens_eos"],
+        )
+
+    return datasets
+
+
+if __name__ == "__main__":
+    hparams_file_path, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
+    with open(hparams_file_path) as hparams_file:
+        hparams = load_hyperpyyaml(hparams_file, overrides)
+
+    sb.utils.distributed.ddp_init_group(run_opts)
+
+    sb.create_experiment_directory(
+        experiment_directory=hparams["output_folder"],
+        hyperparams_to_save=hparams_file_path,
+        overrides=overrides,
+    )
+
+    run_on_main(hparams["pretrainer"].collect_files)
+    hparams["pretrainer"].load_collected(device=run_opts["device"])
+
+    datasets = dataio_prepare(hparams)
+
+    lm_brain = LM(
+        modules=hparams["modules"],
+        opt_class=hparams["optimizer"],
+        hparams=hparams,
+        run_opts=run_opts,
+        checkpointer=hparams["checkpointer"],
+    )
+
+    lm_brain.fit(
+        lm_brain.hparams.epoch_counter,
+        datasets["train"],
+        datasets["dev"],
+        train_loader_kwargs=hparams["train_dataloader_opts"],
+        valid_loader_kwargs=hparams["valid_dataloader_opts"],
+    )
+
+    # evaluation
+    lm_brain.evaluate(
+        datasets["test"],
+        min_key="loss",
+        test_loader_kwargs=hparams["test_dataloader_opts"],
+    )

ASR-model/TransformerLM_seg_char/train_log.txt

@@ -0,0 +1,21 @@
+epoch: 1, lr: 4.99e-04 - train loss: 4.23 - valid loss: 3.19
+epoch: 2, lr: 1.00e-03 - train loss: 2.95 - valid loss: 2.77
+epoch: 3, lr: 1.23e-03 - train loss: 2.73 - valid loss: 2.64
+epoch: 4, lr: 1.07e-03 - train loss: 2.60 - valid loss: 2.54
+epoch: 5, lr: 9.56e-04 - train loss: 2.49 - valid loss: 2.48
+epoch: 6, lr: 8.73e-04 - train loss: 2.41 - valid loss: 2.42
+epoch: 7, lr: 8.08e-04 - train loss: 2.35 - valid loss: 2.38
+epoch: 8, lr: 7.56e-04 - train loss: 2.29 - valid loss: 2.35
+epoch: 9, lr: 7.13e-04 - train loss: 2.24 - valid loss: 2.33
+epoch: 10, lr: 6.76e-04 - train loss: 2.19 - valid loss: 2.32
+epoch: 11, lr: 6.45e-04 - train loss: 2.15 - valid loss: 2.30
+epoch: 12, lr: 6.17e-04 - train loss: 2.11 - valid loss: 2.30
+epoch: 13, lr: 5.93e-04 - train loss: 2.08 - valid loss: 2.29
+epoch: 14, lr: 5.71e-04 - train loss: 2.04 - valid loss: 2.29
+epoch: 15, lr: 5.52e-04 - train loss: 2.01 - valid loss: 2.28
+epoch: 16, lr: 5.34e-04 - train loss: 1.98 - valid loss: 2.28
+epoch: 17, lr: 5.18e-04 - train loss: 1.95 - valid loss: 2.29
+epoch: 18, lr: 5.04e-04 - train loss: 1.92 - valid loss: 2.29
+epoch: 19, lr: 4.90e-04 - train loss: 1.90 - valid loss: 2.30
+epoch: 20, lr: 4.78e-04 - train loss: 1.87 - valid loss: 2.30
+Epoch loaded: 15 - test loss: 2.68

ASR-model/asr_transformer_seg_char_ctc0.3/env.log

@@ -0,0 +1,195 @@
+SpeechBrain system description
+==============================
+Python version:
+3.8.10 (default, Sep 28 2021, 16:10:42) 
+[GCC 9.3.0]
+==============================
+Installed Python packages:
+appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
+joblib==1.0.1
+jsonpatch==1.22
+jsonpointer==2.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.12
+jupyter-console==6.4.0
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keyring==18.0.1
+kiwisolver==1.3.1
+language-selector==0.1
+launchpadlib==1.10.13
+lazr.restfulclient==0.14.2
+lazr.uri==1.0.3
+macaroonbakery==1.3.1
+MarkupSafe==1.1.0
+matplotlib==3.4.2
+matplotlib-inline==0.1.2
+mccabe==0.6.1
+mistune==0.8.4
+more-itertools==4.2.0
+multiprocess==0.70.12.2
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+netifaces==0.10.4
+nodeenv==1.6.0
+notebook==6.4.0
+numpy==1.21.2
+nvidia-ml-py3==7.352.0
+oauthlib==3.1.0
+packaging==21.0
+pandas==1.3.0
+pandocfilters==1.4.3
+parso==0.8.2
+pathspec==0.9.0
+pexpect==4.6.0
+pickleshare==0.7.5
+Pillow==8.3.1
+platformdirs==2.0.2
+pluggy==0.13.1
+pre-commit==2.15.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.6.1
+psutil==5.8.0
+ptyprocess==0.7.0
+py==1.10.0
+pyarrow==5.0.0
+pyasn1==0.4.2
+pyasn1-modules==0.2.1
+pycairo==1.16.2
+pycodestyle==2.5.0
+pycparser==2.20
+pycups==1.9.73
+pyflakes==2.1.1
+Pygments==2.9.0
+PyGObject==3.36.0
+PyHamcrest==1.9.0
+PyJWT==1.7.1
+pymacaroons==0.13.0
+PyMySQL==1.0.2
+PyNaCl==1.3.0
+pyOpenSSL==19.0.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.15.5
+pyserial==3.4
+pytest==5.4.1
+python-apt==2.0.0+ubuntu0.20.4.5
+python-dateutil==2.8.2
+python-debian===0.1.36ubuntu1
+pytube==10.9.3
+pytz==2019.3
+PyYAML==5.3.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.22.0
+requests-unixsocket==0.2.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scipy==1.7.1
+screen-resolution-extra==0.0.0
+SecretStorage==2.3.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+service-identity==18.1.0
+simplejson==3.16.0
+six==1.14.0
+sos==4.1
+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
+terminado==0.10.1
+testpath==0.5.0
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+Twisted==18.9.0
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
+urllib3==1.25.8
+virtualenv==20.6.0
+wadllib==1.3.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+==============================
+Git revision:
+e3e51338
+==============================
+Cuda version:
+10.2

ASR-model/asr_transformer_seg_char_ctc0.3/hyperparams.yaml

@@ -0,0 +1,241 @@
+# Generated 2021-10-11 from:
+# /mnt/md0/user_wayne/speechbrain/recipes/MATBN/ASR/hparams/transformer_seg_LM_char_decode.yaml
+# yamllint disable
+output_folder: results/asr_transformer_seg_char
+cer_file: results/asr_transformer_seg_char/cer.txt
+train_log: results/asr_transformer_seg_char/train_log.txt
+save_folder: results/asr_transformer_seg_char/save
+ckpt_interval_minutes: 15
+num_workers: 4
+
+data_folder: results/prepare_seg
+tokenizer_file: results/tokenizer_seg_bpe5k_char/5000_char.model
+lm_file: results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/model.ckpt
+
+tokenizer: &id001 !new:sentencepiece.SentencePieceProcessor
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+  collect_in: results/asr_transformer_seg_char/save
+  loadables:
+    lm: &id003 !new:speechbrain.lobes.models.transformer.TransformerLM.TransformerLM
+                                                                        # yamllint disable-line rule:line-length
+      vocab: 5000
+      d_model: 576
+      nhead: 6
+      num_encoder_layers: 6
+      num_decoder_layers: 0
+      d_ffn: 1538
+      dropout: 0.2
+      activation: !name:torch.nn.GELU
+      normalize_before: false
+
+    tokenizer: *id001
+  paths:
+    lm: results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/model.ckpt
+    tokenizer: results/tokenizer_seg_bpe5k_char/5000_char.model
+
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/asr_transformer_seg_char/train_log.txt
+
+# Feature parameters
+sample_rate: 16000
+n_fft: 400
+n_mels: 80
+hop_length: 10
+
+compute_features: !new:speechbrain.lobes.features.Fbank
+  sample_rate: 16000
+  n_fft: 400
+  n_mels: 80
+  hop_length: 10
+
+speed_perturb: !new:speechbrain.processing.speech_augmentation.SpeedPerturb
+  orig_freq: 16000
+  speeds: [90, 100, 110]
+
+# Training parameters
+number_of_epochs: 60
+batch_size: 1
+ctc_weight: 0.3
+gradient_accumulation: 32
+gradient_clipping: 5.0
+loss_reduction: batchmean
+sorting: random
+
+# stages related parameters
+stage_one_epochs: 40
+lr_adam: 1.0
+lr_sgd: 0.000025
+
+# Dataloader options
+train_dataloader_opts:
+  batch_size: 1
+  num_workers: 4
+  shuffle: true
+
+valid_dataloader_opts:
+  batch_size: 1
+  num_workers: 4
+
+test_dataloader_opts:
+  batch_size: 1
+  num_workers: 4
+
+####################### Model parameters ###########################
+# Transformer
+d_model: 256
+nhead: 4
+num_encoder_layers: 12
+num_decoder_layers: 6
+d_ffn: 2048
+transformer_dropout: 0.1
+activation: &id002 !name:torch.nn.GELU
+output_neurons: 5000
+vocab_size: 5000
+
+# Outputs
+blank_index: 0
+label_smoothing: 0.1
+pad_index: 0
+bos_index: 1
+eos_index: 2
+unk_index: 0
+
+# Decoding parameters
+min_decode_ratio: 0.0
+max_decode_ratio: 1.0 # 1.0
+valid_search_interval: 10
+valid_beam_size: 10
+test_beam_size: 10
+ctc_weight_decode: 0.30
+lm_weight: 0.9
+
+############################## models ################################
+
+CNN: &id004 !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
+  input_shape: (8, 10, 80)
+  num_blocks: 2
+  num_layers_per_block: 1
+  out_channels: (256, 256)
+  kernel_sizes: (3, 3)
+  strides: (2, 2)
+  residuals: (False, False)
+
+Transformer: &id005 !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR
+                                                                          # yamllint disable-line rule:line-length
+  input_size: 5120
+  tgt_vocab: 5000
+  d_model: 256
+  nhead: 4
+  num_encoder_layers: 12
+  num_decoder_layers: 6
+  d_ffn: 2048
+  dropout: 0.1
+  activation: *id002
+  normalize_before: true
+
+lm_model: *id003
+ctc_lin: &id007 !new:speechbrain.nnet.linear.Linear
+  input_size: 256
+  n_neurons: 5000
+
+seq_lin: &id006 !new:speechbrain.nnet.linear.Linear
+  input_size: 256
+  n_neurons: 5000
+
+modules:
+  CNN: *id004
+  Transformer: *id005
+  seq_lin: *id006
+  ctc_lin: *id007
+  normalize: &id010 !new:speechbrain.processing.features.InputNormalization
+
+    norm_type: global
+    update_until_epoch: 4
+
+model: &id008 !new:torch.nn.ModuleList
+- [*id004, *id005, *id006, *id007]
+Adam: !name:torch.optim.Adam
+  lr: 0
+  betas: (0.9, 0.98)
+  eps: 0.000000001
+
+SGD: !name:torch.optim.SGD
+  lr: 0.000025
+  momentum: 0.99
+  nesterov: true
+
+valid_search: !new:speechbrain.decoders.S2STransformerBeamSearch
+  modules: [*id005, *id006, *id007]
+  bos_index: 1
+  eos_index: 2
+  blank_index: 0
+  min_decode_ratio: 0.0
+  max_decode_ratio: 1.0
+  beam_size: 10
+  ctc_weight: 0.30
+  using_eos_threshold: false
+  length_normalization: true
+
+test_search: !new:speechbrain.decoders.S2STransformerBeamSearch
+  modules: [*id005, *id006, *id007]
+  bos_index: 1
+  eos_index: 2
+  blank_index: 0
+  min_decode_ratio: 0.0
+  max_decode_ratio: 1.0
+  beam_size: 10
+  ctc_weight: 0.30
+  lm_weight: 0.9
+  lm_modules: *id003
+  temperature: 1.15
+  temperature_lm: 1.15
+  using_eos_threshold: false
+  length_normalization: true
+
+log_softmax: !new:torch.nn.LogSoftmax
+  dim: -1
+
+ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
+  blank_index: 0
+  reduction: batchmean
+
+seq_cost: !name:speechbrain.nnet.losses.kldiv_loss
+  label_smoothing: 0.1
+  reduction: batchmean
+
+noam_annealing: &id009 !new:speechbrain.nnet.schedulers.NoamScheduler
+  lr_initial: 1.0
+  n_warmup_steps: 25000
+  model_size: 256
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/asr_transformer_seg_char/save
+  recoverables:
+    model: *id008
+    noam_scheduler: *id009
+    normalizer: *id010
+    counter: &id011 !new:speechbrain.utils.epoch_loop.EpochCounter
+
+      limit: 60
+
+epoch_counter: *id011
+normalize: *id010
+augmentation: !new:speechbrain.lobes.augment.SpecAugment
+  time_warp: true
+  time_warp_window: 5
+  time_warp_mode: bicubic
+  freq_mask: true
+  n_freq_mask: 2
+  time_mask: true
+  n_time_mask: 2
+  replace_with_zero: false
+  freq_mask_width: 30
+  time_mask_width: 40
+
+remove_spaces: true
+split_tokens: &id012 !apply:operator.not_ [true]
+
+cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
+  split_tokens: *id012
+acc_computer: !name:speechbrain.utils.Accuracy.AccuracyStats

ASR-model/asr_transformer_seg_char_ctc0.3/results/asr_transformer_seg_char/env.log

@@ -0,0 +1,109 @@
+SpeechBrain system description
+==============================
+Python version:
+3.8.16 (default, Jun 12 2023, 18:09:05) 
+[GCC 11.2.0]
+==============================
+Installed Python packages:
+antlr4-python3-runtime==4.8
+anyio==3.7.0
+astroid==2.15.5
+bitarray==2.7.6
+certifi==2023.5.7
+cffi==1.15.1
+cfgv==3.3.1
+charset-normalizer==3.1.0
+click==7.1.2
+cmake==3.26.4
+colorama==0.4.6
+Cython==0.29.36
+dill==0.3.6
+distlib==0.3.6
+exceptiongroup==1.1.2
+fairseq @ git+https://github.com/facebookresearch/fairseq.git@31fba013a070eaff69dec8642e68e7134d60ab0f
+fastapi==0.98.0
+filelock==3.12.2
+flake8==6.0.0
+fsspec==2023.6.0
+h11==0.14.0
+httpcore==0.17.2
+httptools==0.1.2
+httpx==0.24.1
+huggingface-hub==0.15.1
+hydra-core==1.0.7
+HyperPyYAML==1.2.1
+identify==2.5.24
+idna==3.4
+importlib-resources==5.12.0
+iniconfig==2.0.0
+isort==5.12.0
+jieba==0.42.1
+Jinja2==3.1.2
+joblib==1.3.1
+lazy-object-proxy==1.9.0
+lit==16.0.6
+lxml==4.9.2
+MarkupSafe==2.1.3
+mccabe==0.7.0
+mock==5.0.2
+mpmath==1.3.0
+networkx==3.1
+nodeenv==1.8.0
+numpy==1.24.4
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+omegaconf==2.0.6
+packaging==23.1
+platformdirs==3.8.0
+pluggy==1.2.0
+portalocker==2.7.0
+pre-commit==3.3.3
+pycodestyle==2.10.0
+pycparser==2.21
+pydantic==1.10.9
+pyflakes==3.0.1
+pylint==2.17.4
+pytest==7.4.0
+python-multipart==0.0.6
+PyYAML==6.0
+regex==2023.6.3
+requests==2.31.0
+ruamel.yaml==0.17.28
+ruamel.yaml.clib==0.2.7
+sacrebleu==2.3.1
+scikit-learn==1.3.0
+scipy==1.10.1
+sentencepiece==0.1.99
+sniffio==1.3.0
+speechbrain==0.5.14
+starlette==0.27.0
+subword-nmt==0.3.8
+sympy==1.12
+tabulate==0.9.0
+threadpoolctl==3.1.0
+tomli==2.0.1
+tomlkit==0.11.8
+torch==2.0.1
+torchaudio==2.0.2
+tqdm==4.65.0
+triton==2.0.0
+typing_extensions==4.7.1
+urllib3==2.0.3
+uvicorn==0.11.3
+uvloop==0.17.0
+virtualenv==20.23.1
+websockets==8.1
+wrapt==1.15.0
+zipp==3.15.0
+==============================
+Could not get git revision==============================
+CUDA not available

ASR-model/asr_transformer_seg_char_ctc0.3/results/asr_transformer_seg_char/hyperparams.yaml

@@ -0,0 +1,244 @@
+# Generated 2023-07-05 from:
+# /mnt/md0/user_sinica_axel/MATBN/results/asr_transformer_seg_char_ctc0.3/hyperparams.yaml
+# yamllint disable
+# Generated 2021-10-11 from:
+# /mnt/md0/user_wayne/speechbrain/recipes/MATBN/ASR/hparams/transformer_seg_LM_char_decode.yaml
+# yamllint disable
+output_folder: results/asr_transformer_seg_char
+cer_file: results/asr_transformer_seg_char/cer.txt
+train_log: results/asr_transformer_seg_char/train_log.txt
+save_folder: results/asr_transformer_seg_char/save
+ckpt_interval_minutes: 15
+num_workers: 4
+
+data_folder: results/prepare_seg
+tokenizer_file: results/tokenizer_seg_bpe5k_char/5000_char.model
+lm_file: results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/model.ckpt
+
+tokenizer: &id001 !new:sentencepiece.SentencePieceProcessor
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+  collect_in: results/asr_transformer_seg_char/save
+  loadables:
+    lm: &id003 !new:speechbrain.lobes.models.transformer.TransformerLM.TransformerLM
+                                                                        # yamllint disable-line rule:line-length
+      vocab: 5000
+      d_model: 576
+      nhead: 6
+      num_encoder_layers: 6
+      num_decoder_layers: 0
+      d_ffn: 1538
+      dropout: 0.2
+      activation: !name:torch.nn.GELU
+      normalize_before: false
+
+    tokenizer: *id001
+  paths:
+    lm: results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00/model.ckpt
+    tokenizer: results/tokenizer_seg_bpe5k_char/5000_char.model
+
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/asr_transformer_seg_char/train_log.txt
+
+# Feature parameters
+sample_rate: 16000
+n_fft: 400
+n_mels: 80
+hop_length: 10
+
+compute_features: !new:speechbrain.lobes.features.Fbank
+  sample_rate: 16000
+  n_fft: 400
+  n_mels: 80
+  hop_length: 10
+
+speed_perturb: !new:speechbrain.processing.speech_augmentation.SpeedPerturb
+  orig_freq: 16000
+  speeds: [90, 100, 110]
+
+# Training parameters
+number_of_epochs: 60
+batch_size: 1
+ctc_weight: 0.3
+gradient_accumulation: 32
+gradient_clipping: 5.0
+loss_reduction: batchmean
+sorting: random
+
+# stages related parameters
+stage_one_epochs: 40
+lr_adam: 1.0
+lr_sgd: 0.000025
+
+# Dataloader options
+train_dataloader_opts:
+  batch_size: 1
+  num_workers: 4
+  shuffle: true
+
+valid_dataloader_opts:
+  batch_size: 1
+  num_workers: 4
+
+test_dataloader_opts:
+  batch_size: 1
+  num_workers: 4
+
+####################### Model parameters ###########################
+# Transformer
+d_model: 256
+nhead: 4
+num_encoder_layers: 12
+num_decoder_layers: 6
+d_ffn: 2048
+transformer_dropout: 0.1
+activation: &id002 !name:torch.nn.GELU
+output_neurons: 5000
+vocab_size: 5000
+
+# Outputs
+blank_index: 0
+label_smoothing: 0.1
+pad_index: 0
+bos_index: 1
+eos_index: 2
+unk_index: 0
+
+# Decoding parameters
+min_decode_ratio: 0.0
+max_decode_ratio: 1.0 # 1.0
+valid_search_interval: 10
+valid_beam_size: 10
+test_beam_size: 10
+ctc_weight_decode: 0.30
+lm_weight: 0.9
+
+############################## models ################################
+
+CNN: &id004 !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd
+  input_shape: (8, 10, 80)
+  num_blocks: 2
+  num_layers_per_block: 1
+  out_channels: (256, 256)
+  kernel_sizes: (3, 3)
+  strides: (2, 2)
+  residuals: (False, False)
+
+Transformer: &id005 !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR
+                                                                          # yamllint disable-line rule:line-length
+  input_size: 5120
+  tgt_vocab: 5000
+  d_model: 256
+  nhead: 4
+  num_encoder_layers: 12
+  num_decoder_layers: 6
+  d_ffn: 2048
+  dropout: 0.1
+  activation: *id002
+  normalize_before: true
+
+lm_model: *id003
+ctc_lin: &id007 !new:speechbrain.nnet.linear.Linear
+  input_size: 256
+  n_neurons: 5000
+
+seq_lin: &id006 !new:speechbrain.nnet.linear.Linear
+  input_size: 256
+  n_neurons: 5000
+
+modules:
+  CNN: *id004
+  Transformer: *id005
+  seq_lin: *id006
+  ctc_lin: *id007
+  normalize: &id010 !new:speechbrain.processing.features.InputNormalization
+
+    norm_type: global
+    update_until_epoch: 4
+
+model: &id008 !new:torch.nn.ModuleList
+- [*id004, *id005, *id006, *id007]
+Adam: !name:torch.optim.Adam
+  lr: 0
+  betas: (0.9, 0.98)
+  eps: 0.000000001
+
+SGD: !name:torch.optim.SGD
+  lr: 0.000025
+  momentum: 0.99
+  nesterov: true
+
+valid_search: !new:speechbrain.decoders.S2STransformerBeamSearch
+  modules: [*id005, *id006, *id007]
+  bos_index: 1
+  eos_index: 2
+  blank_index: 0
+  min_decode_ratio: 0.0
+  max_decode_ratio: 1.0
+  beam_size: 10
+  ctc_weight: 0.30
+  using_eos_threshold: false
+  length_normalization: true
+
+test_search: !new:speechbrain.decoders.S2STransformerBeamSearch
+  modules: [*id005, *id006, *id007]
+  bos_index: 1
+  eos_index: 2
+  blank_index: 0
+  min_decode_ratio: 0.0
+  max_decode_ratio: 1.0
+  beam_size: 10
+  ctc_weight: 0.30
+  lm_weight: 0.9
+  lm_modules: *id003
+  temperature: 1.15
+  temperature_lm: 1.15
+  using_eos_threshold: false
+  length_normalization: true
+
+log_softmax: !new:torch.nn.LogSoftmax
+  dim: -1
+
+ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
+  blank_index: 0
+  reduction: batchmean
+
+seq_cost: !name:speechbrain.nnet.losses.kldiv_loss
+  label_smoothing: 0.1
+  reduction: batchmean
+
+noam_annealing: &id009 !new:speechbrain.nnet.schedulers.NoamScheduler
+  lr_initial: 1.0
+  n_warmup_steps: 25000
+  model_size: 256
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/asr_transformer_seg_char/save
+  recoverables:
+    model: *id008
+    noam_scheduler: *id009
+    normalizer: *id010
+    counter: &id011 !new:speechbrain.utils.epoch_loop.EpochCounter
+
+      limit: 60
+
+epoch_counter: *id011
+normalize: *id010
+augmentation: !new:speechbrain.lobes.augment.SpecAugment
+  time_warp: true
+  time_warp_window: 5
+  time_warp_mode: bicubic
+  freq_mask: true
+  n_freq_mask: 2
+  time_mask: true
+  n_time_mask: 2
+  replace_with_zero: false
+  freq_mask_width: 30
+  time_mask_width: 40
+
+remove_spaces: true
+split_tokens: &id012 !apply:operator.not_ [true]
+
+cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
+  split_tokens: *id012
+acc_computer: !name:speechbrain.utils.Accuracy.AccuracyStats

ASR-model/asr_transformer_seg_char_ctc0.3/results/asr_transformer_seg_char/log.txt

@@ -0,0 +1,120 @@
+2023-07-05 14:57:01,987 - speechbrain.core - INFO - Beginning experiment!
+2023-07-05 14:57:01,987 - speechbrain.core - INFO - Experiment folder: results/asr_transformer_seg_char
+2023-07-05 14:57:02,208 - speechbrain.utils.superpowers - DEBUG - antlr4-python3-runtime==4.8
+anyio==3.7.0
+astroid==2.15.5
+bitarray==2.7.6
+certifi==2023.5.7
+cffi==1.15.1
+cfgv==3.3.1
+charset-normalizer==3.1.0
+click==7.1.2
+cmake==3.26.4
+colorama==0.4.6
+Cython==0.29.36
+dill==0.3.6
+distlib==0.3.6
+exceptiongroup==1.1.2
+fairseq @ git+https://github.com/facebookresearch/fairseq.git@31fba013a070eaff69dec8642e68e7134d60ab0f
+fastapi==0.98.0
+filelock==3.12.2
+flake8==6.0.0
+fsspec==2023.6.0
+h11==0.14.0
+httpcore==0.17.2
+httptools==0.1.2
+httpx==0.24.1
+huggingface-hub==0.15.1
+hydra-core==1.0.7
+HyperPyYAML==1.2.1
+identify==2.5.24
+idna==3.4
+importlib-resources==5.12.0
+iniconfig==2.0.0
+isort==5.12.0
+jieba==0.42.1
+Jinja2==3.1.2
+joblib==1.3.1
+lazy-object-proxy==1.9.0
+lit==16.0.6
+lxml==4.9.2
+MarkupSafe==2.1.3
+mccabe==0.7.0
+mock==5.0.2
+mpmath==1.3.0
+networkx==3.1
+nodeenv==1.8.0
+numpy==1.24.4
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+omegaconf==2.0.6
+packaging==23.1
+platformdirs==3.8.0
+pluggy==1.2.0
+portalocker==2.7.0
+pre-commit==3.3.3
+pycodestyle==2.10.0
+pycparser==2.21
+pydantic==1.10.9
+pyflakes==3.0.1
+pylint==2.17.4
+pytest==7.4.0
+python-multipart==0.0.6
+PyYAML==6.0
+regex==2023.6.3
+requests==2.31.0
+ruamel.yaml==0.17.28
+ruamel.yaml.clib==0.2.7
+sacrebleu==2.3.1
+scikit-learn==1.3.0
+scipy==1.10.1
+sentencepiece==0.1.99
+sniffio==1.3.0
+speechbrain==0.5.14
+starlette==0.27.0
+subword-nmt==0.3.8
+sympy==1.12
+tabulate==0.9.0
+threadpoolctl==3.1.0
+tomli==2.0.1
+tomlkit==0.11.8
+torch==2.0.1
+torchaudio==2.0.2
+tqdm==4.65.0
+triton==2.0.0
+typing_extensions==4.7.1
+urllib3==2.0.3
+uvicorn==0.11.3
+uvloop==0.17.0
+virtualenv==20.23.1
+websockets==8.1
+wrapt==1.15.0
+zipp==3.15.0
+
+
+2023-07-05 14:57:02,231 - speechbrain.utils.parameter_transfer - DEBUG - Collecting files (or symlinks) for pretraining in results/asr_transformer_seg_char/save.
+2023-07-05 14:57:02,231 - speechbrain.pretrained.fetching - INFO - Fetch model.ckpt: Delegating to Huggingface hub, source results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00.
+2023-07-05 14:57:02,290 - speechbrain.core - ERROR - Exception:
+Traceback (most recent call last):
+  File "test.py", line 308, in <module>
+    run_on_main(hparams["pretrainer"].collect_files)
+  File "/home/sinica_axel/miniconda3/envs/asr_api/lib/python3.8/site-packages/speechbrain/utils/distributed.py", line 61, in run_on_main
+    func(*args, **kwargs)
+  File "/home/sinica_axel/miniconda3/envs/asr_api/lib/python3.8/site-packages/speechbrain/utils/parameter_transfer.py", line 202, in collect_files
+    path = fetch(
+  File "/home/sinica_axel/miniconda3/envs/asr_api/lib/python3.8/site-packages/speechbrain/pretrained/fetching.py", line 120, in fetch
+    fetched_file = huggingface_hub.hf_hub_download(
+  File "/home/sinica_axel/miniconda3/envs/asr_api/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 110, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/home/sinica_axel/miniconda3/envs/asr_api/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 158, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'results/TransformerLM_seg_char/save/CKPT+2021-10-05+20-55-37+00'. Use `repo_type` argument if needed.

ASR-model/asr_transformer_seg_char_ctc0.3/results/asr_transformer_seg_char/test.py

@@ -0,0 +1,331 @@
+import sys
+
+import torch
+import speechbrain as sb
+from speechbrain.utils.distributed import run_on_main
+from hyperpyyaml import load_hyperpyyaml
+
+
+class ASR(sb.core.Brain):
+    def compute_forward(self, batch, stage):
+        batch = batch.to(self.device)
+        wavs, wavs_len = batch.sig
+        tokens_bos, _ = batch.tokens_bos
+
+        feats = self.hparams.compute_features(wavs)
+        current_epoch = self.hparams.epoch_counter.current
+        feats = self.hparams.normalize(feats, wavs_len, epoch=current_epoch)
+
+        # if stage == sb.Stage.TRAIN:
+        #     if hasattr(self.modules, "augmentation"):
+        #         feats = self.hparams.augmentation(feats)
+
+        src = self.modules.CNN(feats)
+        enc_out, pred = self.modules.Transformer(
+            src, tokens_bos, wavs_len, pad_idx=self.hparams.pad_index
+        )
+
+        logits = self.modules.ctc_lin(enc_out)
+        p_ctc = self.hparams.log_softmax(logits)
+
+        pred = self.hparams.seq_lin(pred)
+        p_seq = self.hparams.log_softmax(pred)
+
+        hyps = None
+        if stage == sb.Stage.TRAIN:
+            hyps = None
+        elif stage == sb.Stage.VALID:
+            hyps = None
+            current_epoch = self.hparams.epoch_counter.current
+            if current_epoch % self.hparams.valid_search_interval == 0:
+                # for the sake of efficiency, we only perform beamsearch with limited capacity
+                # and no LM to give user some idea of how the AM is doing
+                hyps, _ = self.hparams.valid_search(enc_out.detach(), wavs_len)
+        elif stage == sb.Stage.TEST:
+            hyps, _ = self.hparams.test_search(enc_out.detach(), wavs_len)
+
+        return p_ctc, p_seq, wavs_len, hyps
+
+    def compute_objectives(self, predictions, batch, stage):
+
+        (p_ctc, p_seq, wavs_len, hyps,) = predictions
+
+        ids = batch.id
+        tokens_eos, tokens_eos_len = batch.tokens_eos
+        tokens, tokens_len = batch.tokens
+
+        attention_loss = self.hparams.seq_cost(
+            p_seq, tokens_eos, tokens_eos_len
+        )
+        ctc_loss = self.hparams.ctc_cost(p_ctc, tokens, wavs_len, tokens_len)
+        loss = (
+            self.hparams.ctc_weight * ctc_loss
+            + (1 - self.hparams.ctc_weight) * attention_loss
+        )
+
+        if stage != sb.Stage.TRAIN:
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+
+            if current_epoch % valid_search_interval == 0 or (
+                stage == sb.Stage.TEST
+            ):
+                predictions = [
+                    self.hparams.tokenizer.decode_ids(utt_seq).split(" ")
+                    for utt_seq in hyps
+                ]
+                targets = [
+                    transcription.split(" ")
+                    for transcription in batch.transcription
+                ]
+                if self.hparams.remove_spaces:
+                    predictions = [
+                        "".join(prediction_words)
+                        for prediction_words in predictions
+                    ]
+                    targets = [
+                        "".join(target_words) for target_words in targets
+                    ]
+                    self.cer_metric.append(ids, predictions, targets)
+
+            self.acc_metric.append(p_seq, tokens_eos, tokens_eos_len)
+
+        return loss
+
+    def fit_batch(self, batch):
+        self.check_and_reset_optimizer()
+
+        predictions = self.compute_forward(batch, sb.Stage.TRAIN)
+        loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
+
+        (loss / self.hparams.gradient_accumulation).backward()
+
+        if self.step % self.hparams.gradient_accumulation == 0:
+            self.check_gradients(loss)
+
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+
+            self.hparams.noam_annealing(self.optimizer)
+
+        return loss.detach()
+
+    def evaluate_batch(self, batch, stage):
+        with torch.no_grad():
+            predictions = self.compute_forward(batch, stage=stage)
+            loss = self.compute_objectives(predictions, batch, stage=stage)
+        # origin function is call loss.detach().cpu()
+        return loss.detach()
+
+    def on_stage_start(self, stage, epoch):
+        if stage != sb.Stage.TRAIN:
+            self.acc_metric = self.hparams.acc_computer()
+            self.cer_metric = self.hparams.cer_computer()
+
+    def on_stage_end(self, stage, stage_loss, epoch):
+        stage_stats = {"loss": stage_loss}
+        if stage == sb.Stage.TRAIN:
+            self.train_stats = stage_stats
+        else:
+            stage_stats["ACC"] = self.acc_metric.summarize()
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+            if (
+                current_epoch % valid_search_interval == 0
+                or stage == sb.Stage.TEST
+            ):
+                stage_stats["CER"] = self.cer_metric.summarize("error_rate")
+
+        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+
+            current_epoch = self.hparams.epoch_counter.current
+            if current_epoch <= self.hparams.stage_one_epochs:
+                lr = self.hparams.noam_annealing.current_lr
+                steps = self.hparams.noam_annealing.n_steps
+                optimizer = self.optimizer.__class__.__name__
+            else:
+                lr = self.hparams.lr_sgd
+                steps = -1
+                optimizer = self.optimizer.__class__.__name__
+
+            epoch_stats = {
+                "epoch": epoch,
+                "lr": lr,
+                "steps": steps,
+                "optimizer": optimizer,
+            }
+            self.hparams.train_logger.log_stats(
+                stats_meta=epoch_stats,
+                train_stats=self.train_stats,
+                valid_stats=stage_stats,
+            )
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": stage_stats["ACC"], "epoch": epoch},
+                max_keys=["ACC"],
+                num_to_keep=10,
+            )
+
+        elif stage == sb.Stage.TEST:
+            self.hparams.train_logger.log_stats(
+                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
+                test_stats=stage_stats,
+            )
+            with open(self.hparams.cer_file, "w") as cer_file:
+                self.cer_metric.write_stats(cer_file)
+
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": 1.1, "epoch": epoch},
+                max_keys=["ACC"],
+                num_to_keep=1,
+            )
+
+    def check_and_reset_optimizer(self):
+        current_epoch = self.hparams.epoch_counter.current
+        if not hasattr(self, "switched"):
+            self.switched = False
+            if isinstance(self.optimizer, torch.optim.SGD):
+                self.switched = True
+
+        if self.switched is True:
+            return
+
+        if current_epoch > self.hparams.stage_one_epochs:
+            self.optimizer = self.hparams.SGD(self.modules.parameters())
+
+            if self.checkpointer is not None:
+                self.checkpointer.add_recoverable("optimizer", self.optimizer)
+
+            self.switched = True
+
+    def on_fit_start(self):
+        """Initialize the right optimizer on the training start"""
+        super().on_fit_start()
+
+        current_epoch = self.hparams.epoch_counter.current
+        current_optimizer = self.optimizer
+        if current_epoch > self.hparams.stage_one_epochs:
+            del self.optimizer
+            self.optimizer = self.hparams.SGD(self.modules.parameters())
+
+            if self.checkpointer is not None:
+                group = current_optimizer.param_groups[0]
+                if "momentum" not in group:
+                    return
+                self.checkpointer.recover_if_possible(
+                    device=torch.device(self.device)
+                )
+
+    def on_evaluate_start(self, max_key=None, min_key=None):
+        super().on_evaluate_start()
+
+        checkpointer = self.checkpointer.find_checkpoints(
+            max_key=max_key, min_key=min_key
+        )
+        checkpointer = sb.utils.checkpoints.average_checkpoints(
+            checkpointer, recoverable_name="model", device=self.device
+        )
+
+        self.hparams.model.load_state_dict(checkpointer, strict=True)
+        self.hparams.model.eval()
+
+
+def dataio_prepare(hparams):
+    @sb.utils.data_pipeline.takes("transcription")
+    @sb.utils.data_pipeline.provides(
+        "transcription", "tokens_bos", "tokens_eos", "tokens"
+    )
+    def transcription_pipline(transcription):
+        yield transcription
+        tokens_list = hparams["tokenizer"].encode_as_ids(transcription)
+        tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
+        yield tokens_bos
+        tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
+        yield tokens_eos
+        tokens = torch.LongTensor(tokens_list)
+        yield tokens
+
+    @sb.utils.data_pipeline.takes("wav")
+    @sb.utils.data_pipeline.provides("sig")
+    def audio_pipline(wav):
+        sig = sb.dataio.dataio.read_audio(wav)
+        return sig
+
+    @sb.utils.data_pipeline.takes("wav")
+    @sb.utils.data_pipeline.provides("sig")
+    def sp_audio_pipline(wav):
+        sig = sb.dataio.dataio.read_audio(wav)
+        sig = sig.unsqueeze(0)
+        sig = hparams["speed_perturb"](sig)
+        sig = sig.squeeze(0)
+        return sig
+
+    datasets = {}
+    data_folder = hparams["data_folder"]
+    output_keys = [
+        "transcription",
+        "tokens_bos",
+        "tokens_eos",
+        "tokens",
+        "sig",
+        "id",
+    ]
+    default_dynamic_items = [transcription_pipline, audio_pipline]
+    train_dynamic_item = [transcription_pipline, sp_audio_pipline]
+
+    for dataset_name in ["train", "dev", "test"]:
+        if dataset_name == "train":
+            dynamic_items = train_dynamic_item
+        else:
+            dynamic_items = default_dynamic_items
+
+        json_path = f"{data_folder}/{dataset_name}.json"
+        datasets[dataset_name] = sb.dataio.dataset.DynamicItemDataset.from_json(
+            json_path=json_path,
+            replacements={"data_root": data_folder},
+            dynamic_items=dynamic_items,
+            output_keys=output_keys,
+        )
+
+    return datasets
+
+
+if __name__ == "__main__":
+    #hparams_file_path, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
+    hparams_file_path = "hyperparams.yaml"
+    run_opts = {"device": "cuda", "distributed_launch": False}
+    overrides = None
+    with open(hparams_file_path) as hparams_file:
+        hparams = load_hyperpyyaml(hparams_file, overrides)
+
+    sb.utils.distributed.ddp_init_group(run_opts)
+
+    sb.create_experiment_directory(
+        experiment_directory=hparams["output_folder"],
+        hyperparams_to_save=hparams_file_path,
+        overrides=overrides,
+    )
+
+    run_on_main(hparams["pretrainer"].collect_files)
+    hparams["pretrainer"].load_collected(device=run_opts["device"])
+
+    datasets = dataio_prepare(hparams)
+
+    asr_brain = ASR(
+        modules=hparams["modules"],
+        opt_class=hparams["Adam"],
+        hparams=hparams,
+        run_opts=run_opts,
+        checkpointer=hparams["checkpointer"],
+    )
+
+    # asr_brain.fit(
+    #     asr_brain.hparams.epoch_counter,
+    #     datasets["train"],
+    #     datasets["dev"],
+    #     train_loader_kwargs=hparams["train_dataloader_opts"],
+    #     valid_loader_kwargs=hparams["valid_dataloader_opts"],
+    # )
+
+    asr_brain.evaluate(
+        datasets["test"], max_key="ACC", test_loader_kwargs=hparams["test_dataloader_opts"]
+    )

ASR-model/asr_transformer_seg_char_ctc0.3/save/CKPT+2021-10-11+14-03-15+00/CKPT.yaml

@@ -0,0 +1,5 @@
+# yamllint disable
+ACC: 1.1
+end-of-epoch: true
+epoch: null
+unixtime: 1633932195.1636646

ASR-model/asr_transformer_seg_char_ctc0.3/save/CKPT+2021-10-11+14-03-15+00/brain.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22e854e25aa2ad065885cf277f6017bfa7ab555eb84131dfa2da35605cb8fb14
+size 32

ASR-model/asr_transformer_seg_char_ctc0.3/save/CKPT+2021-10-11+14-03-15+00/counter.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39fa9ec190eee7b6f4dff1100d6343e10918d044c75eac8f9e9a2596173f80c9
+size 2

ASR-model/asr_transformer_seg_char_ctc0.3/save/CKPT+2021-10-11+14-03-15+00/model.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f325dfc2ecffc1332882e84cac08a9f05e500253413c2346a20f35baaeed705
+size 126713830

ASR-model/asr_transformer_seg_char_ctc0.3/save/CKPT+2021-10-11+14-03-15+00/noam_scheduler.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35ce303ccbe8d1abb2b87a01a29e01ed6a7b345253ab6db9111cbd5eefbf503c
+size 431

ASR-model/asr_transformer_seg_char_ctc0.3/save/CKPT+2021-10-11+14-03-15+00/normalizer.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c4aa56041d2ead93019a7781f786d29cc577aaf24f4766574c47867a2078f80
+size 1782

ASR-model/asr_transformer_seg_char_ctc0.3/test.py

@@ -0,0 +1,181 @@
+import sys
+
+import torch
+import speechbrain as sb
+from speechbrain.utils.distributed import run_on_main
+from hyperpyyaml import load_hyperpyyaml
+
+
+class ASR(sb.core.Brain):
+    def compute_forward(self, batch, stage):
+        batch = batch.to(self.device)
+        wavs, wavs_len = batch.sig
+        tokens_bos, _ = batch.tokens_bos
+
+        feats = self.hparams.compute_features(wavs)
+        current_epoch = self.hparams.epoch_counter.current
+        feats = self.hparams.normalize(feats, wavs_len, epoch=current_epoch)
+
+        # if stage == sb.Stage.TRAIN:
+        #     if hasattr(self.modules, "augmentation"):
+        #         feats = self.hparams.augmentation(feats)
+
+        src = self.modules.CNN(feats)
+        enc_out, pred = self.modules.Transformer(
+            src, tokens_bos, wavs_len, pad_idx=self.hparams.pad_index
+        )
+
+        logits = self.modules.ctc_lin(enc_out)
+        p_ctc = self.hparams.log_softmax(logits)
+
+        pred = self.hparams.seq_lin(pred)
+        p_seq = self.hparams.log_softmax(pred)
+
+        hyps = None
+        if stage == sb.Stage.TRAIN:
+            hyps = None
+        elif stage == sb.Stage.VALID:
+            hyps = None
+            current_epoch = self.hparams.epoch_counter.current
+            if current_epoch % self.hparams.valid_search_interval == 0:
+                # for the sake of efficiency, we only perform beamsearch with limited capacity
+                # and no LM to give user some idea of how the AM is doing
+                hyps, _ = self.hparams.valid_search(enc_out.detach(), wavs_len)
+        elif stage == sb.Stage.TEST:
+            hyps, _ = self.hparams.test_search(enc_out.detach(), wavs_len)
+
+        return p_ctc, p_seq, wavs_len, hyps
+
+    def evaluate_batch(self, batch, stage):
+        with torch.no_grad():
+            predictions = self.compute_forward(batch, stage=stage)
+            loss = self.compute_objectives(predictions, batch, stage=stage)
+        # origin function is call loss.detach().cpu()
+        return loss.detach()
+
+    def on_stage_start(self, stage, epoch):
+        if stage != sb.Stage.TRAIN:
+            self.acc_metric = self.hparams.acc_computer()
+            self.cer_metric = self.hparams.cer_computer()
+
+    def on_stage_end(self, stage, stage_loss, epoch):
+        stage_stats = {"loss": stage_loss}
+        if stage == sb.Stage.TRAIN:
+            self.train_stats = stage_stats
+        else:
+            stage_stats["ACC"] = self.acc_metric.summarize()
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+            if (
+                current_epoch % valid_search_interval == 0
+                or stage == sb.Stage.TEST
+            ):
+                stage_stats["CER"] = self.cer_metric.summarize("error_rate")
+
+        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+
+            current_epoch = self.hparams.epoch_counter.current
+            if current_epoch <= self.hparams.stage_one_epochs:
+                lr = self.hparams.noam_annealing.current_lr
+                steps = self.hparams.noam_annealing.n_steps
+                optimizer = self.optimizer.__class__.__name__
+            else:
+                lr = self.hparams.lr_sgd
+                steps = -1
+                optimizer = self.optimizer.__class__.__name__
+
+            epoch_stats = {
+                "epoch": epoch,
+                "lr": lr,
+                "steps": steps,
+                "optimizer": optimizer,
+            }
+            self.hparams.train_logger.log_stats(
+                stats_meta=epoch_stats,
+                train_stats=self.train_stats,
+                valid_stats=stage_stats,
+            )
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": stage_stats["ACC"], "epoch": epoch},
+                max_keys=["ACC"],
+                num_to_keep=10,
+            )
+
+        elif stage == sb.Stage.TEST:
+            self.hparams.train_logger.log_stats(
+                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
+                test_stats=stage_stats,
+            )
+            with open(self.hparams.cer_file, "w") as cer_file:
+                self.cer_metric.write_stats(cer_file)
+
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": 1.1, "epoch": epoch},
+                max_keys=["ACC"],
+                num_to_keep=1,
+            )
+
+    def check_and_reset_optimizer(self):
+        current_epoch = self.hparams.epoch_counter.current
+        if not hasattr(self, "switched"):
+            self.switched = False
+            if isinstance(self.optimizer, torch.optim.SGD):
+                self.switched = True
+
+        if self.switched is True:
+            return
+
+        if current_epoch > self.hparams.stage_one_epochs:
+            self.optimizer = self.hparams.SGD(self.modules.parameters())
+
+            if self.checkpointer is not None:
+                self.checkpointer.add_recoverable("optimizer", self.optimizer)
+
+            self.switched = True
+
+    def on_fit_start(self):
+        """Initialize the right optimizer on the training start"""
+        super().on_fit_start()
+
+        current_epoch = self.hparams.epoch_counter.current
+        current_optimizer = self.optimizer
+        if current_epoch > self.hparams.stage_one_epochs:
+            del self.optimizer
+            self.optimizer = self.hparams.SGD(self.modules.parameters())
+
+            if self.checkpointer is not None:
+                group = current_optimizer.param_groups[0]
+                if "momentum" not in group:
+                    return
+                self.checkpointer.recover_if_possible(
+                    device=torch.device(self.device)
+                )
+
+    def on_evaluate_start(self, max_key=None, min_key=None):
+        super().on_evaluate_start()
+
+        checkpointer = self.checkpointer.find_checkpoints(
+            max_key=max_key, min_key=min_key
+        )
+        checkpointer = sb.utils.checkpoints.average_checkpoints(
+            checkpointer, recoverable_name="model", device=self.device
+        )
+
+        self.hparams.model.load_state_dict(checkpointer, strict=True)
+        self.hparams.model.eval()
+
+
+if __name__ == "__main__":
+    hparams_file_path = "hyperparams.yaml"
+    run_opts = {"device": "cuda", "distributed_launch": False}
+    with open(hparams_file_path) as hparams_file:
+        hparams = load_hyperpyyaml(hparams_file)
+
+    asr_brain = ASR(
+        modules=hparams["modules"],
+        opt_class=hparams["Adam"],
+        hparams=hparams,
+        run_opts=run_opts,
+        checkpointer=hparams["checkpointer"],
+    )
+

ASR-model/asr_transformer_seg_char_ctc0.3/train.py

@@ -0,0 +1,322 @@
+import sys
+
+import torch
+import speechbrain as sb
+from speechbrain.utils.distributed import run_on_main
+from hyperpyyaml import load_hyperpyyaml
+
+
+class ASR(sb.core.Brain):
+    def compute_forward(self, batch, stage):
+        batch = batch.to(self.device)
+        wavs, wavs_len = batch.sig
+        tokens_bos, _ = batch.tokens_bos
+        feats = self.hparams.compute_features(wavs)
+        current_epoch = self.hparams.epoch_counter.current
+        feats = self.modules.normalize(feats, wavs_len, epoch=current_epoch)
+
+        src = self.modules.CNN(feats)
+        enc_out, pred = self.modules.Transformer(
+            src, tokens_bos, wavs_len, pad_idx=self.hparams.pad_index
+        )
+
+        logits = self.modules.ctc_lin(enc_out)
+        p_ctc = self.hparams.log_softmax(logits)
+
+        pred = self.modules.seq_lin(pred)
+        p_seq = self.hparams.log_softmax(pred)
+
+        hyps = None
+        if stage == sb.Stage.TRAIN:
+            hyps = None
+        elif stage == sb.Stage.VALID:
+            hyps = None
+            current_epoch = self.hparams.epoch_counter.current
+            if current_epoch % self.hparams.valid_search_interval == 0:
+                hyps, _ = self.hparams.valid_search(enc_out.detach(), wavs_len)
+        elif stage == sb.Stage.TEST:
+            hyps, _ = self.hparams.test_search(enc_out.detach(), wavs_len)
+
+        return p_ctc, p_seq, wavs_len, hyps
+
+    def compute_objectives(self, predictions, batch, stage):
+
+        (p_ctc, p_seq, wavs_len, hyps,) = predictions
+
+        ids = batch.id
+        tokens_eos, tokens_eos_len = batch.tokens_eos
+        tokens, tokens_len = batch.tokens
+
+        attention_loss = self.hparams.seq_cost(
+            p_seq, tokens_eos, length=tokens_eos_len
+        )
+        ctc_loss = self.hparams.ctc_cost(p_ctc, tokens, wavs_len, tokens_len)
+        loss = (
+            self.hparams.ctc_weight * ctc_loss
+            + (1 - self.hparams.ctc_weight) * attention_loss
+        )
+
+        if stage != sb.Stage.TRAIN:
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+
+            if current_epoch % valid_search_interval == 0 or (
+                stage == sb.Stage.TEST
+            ):
+                predictions = [
+                    hparams["tokenizer"].decode_ids(utt_seq).split(" ")
+                    for utt_seq in hyps
+                ]
+                targets = [
+                    transcription.split(" ")
+                    for transcription in batch.transcription
+                ]
+                if self.hparams.remove_spaces:
+                    predictions = [
+                        "".join(prediction_words)
+                        for prediction_words in predictions
+                    ]
+                    targets = [
+                        "".join(target_words) for target_words in targets
+                    ]
+                    self.cer_metric.append(ids, predictions, targets)
+
+            self.acc_metric.append(p_seq, tokens_eos, tokens_eos_len)
+
+        return loss
+
+    def fit_batch(self, batch):
+        self.check_and_reset_optimizer()
+
+        predictions = self.compute_forward(batch, sb.Stage.TRAIN)
+        loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
+
+        (loss / self.hparams.gradient_accumulation).backward()
+
+        if self.step % self.hparams.gradient_accumulation == 0:
+            self.check_gradients(loss)
+
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+
+            self.hparams.noam_annealing(self.optimizer)
+
+        return loss.detach()
+
+    def evaluate_batch(self, batch, stage):
+        with torch.no_grad():
+            predictions = self.compute_forward(batch, stage=stage)
+            loss = self.compute_objectives(predictions, batch, stage=stage)
+        # origin function is call loss.detach().cpu()
+        return loss.detach()
+
+    def on_stage_start(self, stage, epoch):
+        if stage != sb.Stage.TRAIN:
+            self.acc_metric = self.hparams.acc_computer()
+            self.cer_metric = self.hparams.cer_computer()
+
+    def on_stage_end(self, stage, stage_loss, epoch):
+        stage_stats = {"loss": stage_loss}
+        if stage == sb.Stage.TRAIN:
+            self.train_stats = stage_stats
+        else:
+            stage_stats["ACC"] = self.acc_metric.summarize()
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+            if (
+                current_epoch % valid_search_interval == 0
+                or stage == sb.Stage.TEST
+            ):
+                stage_stats["CER"] = self.cer_metric.summarize("error_rate")
+
+        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+
+            current_epoch = self.hparams.epoch_counter.current
+            if current_epoch <= self.hparams.stage_one_epochs:
+                lr = self.hparams.noam_annealing.current_lr
+                steps = self.hparams.noam_annealing.n_steps
+                optimizer = self.optimizer.__class__.__name__
+            else:
+                lr = self.hparams.lr_sgd
+                steps = -1
+                optimizer = self.optimizer.__class__.__name__
+
+            epoch_stats = {
+                "epoch": epoch,
+                "lr": lr,
+                "steps": steps,
+                "optimizer": optimizer,
+            }
+            self.hparams.train_logger.log_stats(
+                stats_meta=epoch_stats,
+                train_stats=self.train_stats,
+                valid_stats=stage_stats,
+            )
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": stage_stats["ACC"], "epoch": epoch},
+                max_keys=["ACC"],
+                num_to_keep=10,
+            )
+
+        elif stage == sb.Stage.TEST:
+            self.hparams.train_logger.log_stats(
+                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
+                test_stats=stage_stats,
+            )
+            with open(self.hparams.cer_file, "w") as cer_file:
+                self.cer_metric.write_stats(cer_file)
+
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": 1.1, "epoch": epoch},
+                max_keys=["ACC"],
+                num_to_keep=1,
+            )
+
+    def check_and_reset_optimizer(self):
+        current_epoch = self.hparams.epoch_counter.current
+        if not hasattr(self, "switched"):
+            self.switched = False
+            if isinstance(self.optimizer, torch.optim.SGD):
+                self.switched = True
+
+        if self.switched is True:
+            return
+
+        if current_epoch > self.hparams.stage_one_epochs:
+            self.optimizer = self.hparams.SGD(self.modules.parameters())
+
+            if self.checkpointer is not None:
+                self.checkpointer.add_recoverable("optimizer", self.optimizer)
+
+            self.switched = True
+
+    def on_fit_start(self):
+        """Initialize the right optimizer on the training start"""
+        super().on_fit_start()
+
+        current_epoch = self.hparams.epoch_counter.current
+        current_optimizer = self.optimizer
+        if current_epoch > self.hparams.stage_one_epochs:
+            del self.optimizer
+            self.optimizer = self.hparams.SGD(self.modules.parameters())
+
+            if self.checkpointer is not None:
+                group = current_optimizer.param_groups[0]
+                if "momentum" not in group:
+                    return
+                self.checkpointer.recover_if_possible(
+                    device=torch.device(self.device)
+                )
+
+    def on_evaluate_start(self, max_key=None, min_key=None):
+        super().on_evaluate_start()
+
+        checkpointers = self.checkpointer.find_checkpoints(
+            max_key=max_key, min_key=min_key
+        )
+        checkpointer = sb.utils.checkpoints.average_checkpoints(
+            checkpointers, recoverable_name="model", device=self.device
+        )
+
+        self.hparams.model.load_state_dict(checkpointer, strict=True)
+        self.hparams.model.eval()
+
+
+def dataio_prepare(hparams):
+    @sb.utils.data_pipeline.takes("transcription")
+    @sb.utils.data_pipeline.provides(
+        "transcription", "tokens_bos", "tokens_eos", "tokens"
+    )
+    def transcription_pipline(transcription):
+        yield transcription
+        tokens_list = hparams["tokenizer"].encode_as_ids(transcription)
+        tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
+        yield tokens_bos
+        tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
+        yield tokens_eos
+        tokens = torch.LongTensor(tokens_list)
+        yield tokens
+
+    @sb.utils.data_pipeline.takes("wav")
+    @sb.utils.data_pipeline.provides("sig")
+    def audio_pipline(wav):
+        sig = sb.dataio.dataio.read_audio(wav)
+        return sig
+
+    @sb.utils.data_pipeline.takes("wav")
+    @sb.utils.data_pipeline.provides("sig")
+    def sp_audio_pipline(wav):
+        sig = sb.dataio.dataio.read_audio(wav)
+        sig = sig.unsqueeze(0)
+        sig = hparams["speed_perturb"](sig)
+        sig = sig.squeeze(0)
+        return sig
+
+    datasets = {}
+    data_folder = hparams["data_folder"]
+    output_keys = [
+        "transcription",
+        "tokens_bos",
+        "tokens_eos",
+        "tokens",
+        "sig",
+        "id",
+    ]
+    default_dynamic_items = [transcription_pipline, audio_pipline]
+    train_dynamic_item = [transcription_pipline, sp_audio_pipline]
+
+    for dataset_name in ["train", "dev", "test"]:
+        if dataset_name == "train":
+            dynamic_items = train_dynamic_item
+        else:
+            dynamic_items = default_dynamic_items
+
+        json_path = f"{data_folder}/{dataset_name}.json"
+        datasets[dataset_name] = sb.dataio.dataset.DynamicItemDataset.from_json(
+            json_path=json_path,
+            replacements={"data_root": data_folder},
+            dynamic_items=dynamic_items,
+            output_keys=output_keys,
+        )
+
+    return datasets
+
+
+if __name__ == "__main__":
+    hparams_file_path, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
+
+    sb.utils.distributed.ddp_init_group(run_opts)
+
+    with open(hparams_file_path) as hparams_file:
+        hparams = load_hyperpyyaml(hparams_file, overrides)
+
+    sb.create_experiment_directory(
+        experiment_directory=hparams["output_folder"],
+        hyperparams_to_save=hparams_file_path,
+        overrides=overrides,
+    )
+
+    run_on_main(hparams["pretrainer"].collect_files)
+    hparams["pretrainer"].load_collected(device=run_opts["device"])
+
+    datasets = dataio_prepare(hparams)
+
+    asr_brain = ASR(
+        modules=hparams["modules"],
+        opt_class=hparams["Adam"],
+        hparams=hparams,
+        run_opts=run_opts,
+        checkpointer=hparams["checkpointer"],
+    )
+
+    asr_brain.fit(
+        asr_brain.hparams.epoch_counter,
+        datasets["train"],
+        datasets["dev"],
+        train_loader_kwargs=hparams["train_dataloader_opts"],
+        valid_loader_kwargs=hparams["valid_dataloader_opts"],
+    )
+
+    # asr_brain.evaluate(
+    #     datasets["test"],max_key="ACC", test_loader_kwargs=hparams["test_dataloader_opts"]
+    # )

ASR-model/asr_transformer_seg_char_ctc0.3/train_log.txt

@@ -0,0 +1,70 @@
+epoch: 1, lr: 2.40e-05, steps: 1520, optimizer: Adam - train loss: 3.41e+02 - valid loss: 2.52e+02, valid ACC: 4.07e-01
+epoch: 2, lr: 4.81e-05, steps: 3040, optimizer: Adam - train loss: 2.36e+02 - valid loss: 2.26e+02, valid ACC: 4.36e-01
+epoch: 3, lr: 7.21e-05, steps: 4560, optimizer: Adam - train loss: 2.17e+02 - valid loss: 2.12e+02, valid ACC: 4.66e-01
+epoch: 4, lr: 9.61e-05, steps: 6080, optimizer: Adam - train loss: 2.05e+02 - valid loss: 2.02e+02, valid ACC: 4.86e-01
+epoch: 5, lr: 1.20e-04, steps: 7600, optimizer: Adam - train loss: 1.96e+02 - valid loss: 1.92e+02, valid ACC: 4.98e-01
+epoch: 6, lr: 1.44e-04, steps: 9120, optimizer: Adam - train loss: 1.70e+02 - valid loss: 1.51e+02, valid ACC: 5.30e-01
+epoch: 7, lr: 1.68e-04, steps: 10640, optimizer: Adam - train loss: 1.37e+02 - valid loss: 1.14e+02, valid ACC: 6.31e-01
+epoch: 8, lr: 1.92e-04, steps: 12160, optimizer: Adam - train loss: 1.10e+02 - valid loss: 88.56, valid ACC: 7.21e-01
+epoch: 9, lr: 2.16e-04, steps: 13680, optimizer: Adam - train loss: 86.93 - valid loss: 67.36, valid ACC: 8.03e-01
+epoch: 10, lr: 2.40e-04, steps: 15200, optimizer: Adam - train loss: 64.60 - valid loss: 51.05, valid ACC: 8.61e-01, valid CER: 22.14
+epoch: 11, lr: 2.64e-04, steps: 16720, optimizer: Adam - train loss: 51.94 - valid loss: 42.96, valid ACC: 8.86e-01
+epoch: 12, lr: 2.88e-04, steps: 18240, optimizer: Adam - train loss: 44.10 - valid loss: 37.56, valid ACC: 8.99e-01
+epoch: 13, lr: 3.12e-04, steps: 19760, optimizer: Adam - train loss: 38.66 - valid loss: 33.39, valid ACC: 9.09e-01
+epoch: 14, lr: 3.36e-04, steps: 21280, optimizer: Adam - train loss: 34.66 - valid loss: 31.04, valid ACC: 9.16e-01
+epoch: 15, lr: 3.60e-04, steps: 22800, optimizer: Adam - train loss: 31.69 - valid loss: 29.01, valid ACC: 9.19e-01
+epoch: 16, lr: 3.85e-04, steps: 24320, optimizer: Adam - train loss: 29.34 - valid loss: 27.39, valid ACC: 9.24e-01
+epoch: 17, lr: 3.89e-04, steps: 25840, optimizer: Adam - train loss: 27.37 - valid loss: 25.86, valid ACC: 9.28e-01
+epoch: 18, lr: 3.78e-04, steps: 27360, optimizer: Adam - train loss: 25.12 - valid loss: 24.43, valid ACC: 9.32e-01
+epoch: 19, lr: 3.68e-04, steps: 28880, optimizer: Adam - train loss: 23.16 - valid loss: 23.26, valid ACC: 9.35e-01
+epoch: 20, lr: 3.58e-04, steps: 30400, optimizer: Adam - train loss: 21.53 - valid loss: 22.58, valid ACC: 9.37e-01, valid CER: 10.20
+epoch: 21, lr: 3.50e-04, steps: 31920, optimizer: Adam - train loss: 20.06 - valid loss: 21.66, valid ACC: 9.39e-01
+epoch: 22, lr: 3.42e-04, steps: 33440, optimizer: Adam - train loss: 18.83 - valid loss: 21.18, valid ACC: 9.41e-01
+epoch: 23, lr: 3.34e-04, steps: 34960, optimizer: Adam - train loss: 17.77 - valid loss: 20.92, valid ACC: 9.42e-01
+epoch: 24, lr: 3.27e-04, steps: 36480, optimizer: Adam - train loss: 16.80 - valid loss: 20.30, valid ACC: 9.43e-01
+epoch: 25, lr: 3.21e-04, steps: 38000, optimizer: Adam - train loss: 15.91 - valid loss: 19.68, valid ACC: 9.44e-01
+epoch: 26, lr: 3.14e-04, steps: 39520, optimizer: Adam - train loss: 15.12 - valid loss: 19.73, valid ACC: 9.45e-01
+epoch: 27, lr: 3.09e-04, steps: 41040, optimizer: Adam - train loss: 14.41 - valid loss: 19.30, valid ACC: 9.46e-01
+epoch: 28, lr: 3.03e-04, steps: 42560, optimizer: Adam - train loss: 13.79 - valid loss: 18.82, valid ACC: 9.47e-01
+epoch: 29, lr: 2.98e-04, steps: 44080, optimizer: Adam - train loss: 13.15 - valid loss: 19.04, valid ACC: 9.47e-01
+epoch: 30, lr: 2.93e-04, steps: 45600, optimizer: Adam - train loss: 12.66 - valid loss: 18.89, valid ACC: 9.47e-01, valid CER: 8.39
+epoch: 31, lr: 2.88e-04, steps: 47120, optimizer: Adam - train loss: 12.13 - valid loss: 18.66, valid ACC: 9.48e-01
+epoch: 32, lr: 2.83e-04, steps: 48640, optimizer: Adam - train loss: 11.64 - valid loss: 18.51, valid ACC: 9.48e-01
+epoch: 33, lr: 2.79e-04, steps: 50160, optimizer: Adam - train loss: 11.21 - valid loss: 18.36, valid ACC: 9.48e-01
+epoch: 34, lr: 2.75e-04, steps: 51680, optimizer: Adam - train loss: 10.79 - valid loss: 18.65, valid ACC: 9.48e-01
+epoch: 35, lr: 2.71e-04, steps: 53200, optimizer: Adam - train loss: 10.43 - valid loss: 18.40, valid ACC: 9.49e-01
+epoch: 36, lr: 2.67e-04, steps: 54720, optimizer: Adam - train loss: 10.11 - valid loss: 18.29, valid ACC: 9.49e-01
+epoch: 37, lr: 2.64e-04, steps: 56240, optimizer: Adam - train loss: 9.78 - valid loss: 18.32, valid ACC: 9.50e-01
+epoch: 38, lr: 2.60e-04, steps: 57760, optimizer: Adam - train loss: 9.46 - valid loss: 18.12, valid ACC: 9.50e-01
+epoch: 39, lr: 2.57e-04, steps: 59280, optimizer: Adam - train loss: 9.17 - valid loss: 18.36, valid ACC: 9.49e-01
+epoch: 40, lr: 2.53e-04, steps: 60800, optimizer: Adam - train loss: 8.88 - valid loss: 18.29, valid ACC: 9.49e-01, valid CER: 7.93
+epoch: 41, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 7.01 - valid loss: 17.34, valid ACC: 9.54e-01
+epoch: 42, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 6.32 - valid loss: 17.22, valid ACC: 9.54e-01
+epoch: 43, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.97 - valid loss: 17.15, valid ACC: 9.54e-01
+epoch: 44, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.76 - valid loss: 17.14, valid ACC: 9.55e-01
+epoch: 45, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.58 - valid loss: 17.19, valid ACC: 9.54e-01
+epoch: 46, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.45 - valid loss: 17.07, valid ACC: 9.55e-01
+epoch: 47, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.33 - valid loss: 17.07, valid ACC: 9.55e-01
+epoch: 48, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.22 - valid loss: 17.17, valid ACC: 9.55e-01
+epoch: 49, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.13 - valid loss: 17.24, valid ACC: 9.55e-01
+epoch: 50, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 5.04 - valid loss: 17.10, valid ACC: 9.55e-01, valid CER: 7.10
+epoch: 51, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.97 - valid loss: 17.26, valid ACC: 9.55e-01
+epoch: 52, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.88 - valid loss: 17.23, valid ACC: 9.55e-01
+epoch: 53, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.82 - valid loss: 17.17, valid ACC: 9.55e-01
+epoch: 54, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.76 - valid loss: 17.31, valid ACC: 9.55e-01
+epoch: 55, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.71 - valid loss: 17.37, valid ACC: 9.55e-01
+epoch: 56, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.66 - valid loss: 17.26, valid ACC: 9.55e-01
+epoch: 57, lr: 2.50e-05, steps: -1, optimizer: SGD - train loss: 4.60 - valid loss: 17.43, valid ACC: 9.55e-01
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ASR-model/tokenizer_seg_bpe5k_char/env.log

@@ -0,0 +1,195 @@
+SpeechBrain system description
+==============================
+Python version:
+3.8.10 (default, Jun  2 2021, 10:49:15) 
+[GCC 9.4.0]
+==============================
+Installed Python packages:
+appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
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+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
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+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
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+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+==============================
+Git revision:
+e3e51338
+==============================
+Cuda version:
+10.2

ASR-model/tokenizer_seg_bpe5k_char/hyperparams.yaml

@@ -0,0 +1,31 @@
+# Generated 2021-10-04 from:
+# /mnt/md0/user_wayne/speechbrain/recipes/MATBN/Tokenizer/hparams/tokenizer_seg_bpe5k_char.yaml
+# yamllint disable
+dataset_folder: /home/wayne/CORPUS/MATBN_SEG
+prepare_folder: results/prepare_seg
+output_folder: results/tokenizer_seg_bpe5k_char
+keep_unk: false
+
+token_type: char # ["unigram", "bpe", "char"]
+token_output: 5000 # index(blank/eos/bos/unk) = 0
+character_coverage: 1.0
+annotation_read: transcription
+
+train_json: results/prepare_seg/train.json
+dev_json: results/prepare_seg/dev.json
+eval_json: results/prepare_seg/eval.json
+test_json: results/prepare_seg/test.json
+
+
+tokenizer: !name:speechbrain.tokenizers.SentencePiece.SentencePiece
+  model_dir: results/tokenizer_seg_bpe5k_char
+  vocab_size: 5000
+  annotation_train: results/prepare_seg/train.json
+  annotation_read: transcription
+  model_type: char              # ["unigram", "bpe", "char"]
+  character_coverage: 1.0
+  annotation_list_to_check: [results/prepare_seg/dev.json, results/prepare_seg/eval.json,
+    results/prepare_seg/test.json]
+  annotation_format: json
+  bos_id: 1
+  eos_id: 2

ASR-model/tokenizer_seg_bpe5k_char/log.txt

@@ -0,0 +1,1037 @@
+2021-09-16 18:20:17,572 - speechbrain.core - INFO - Beginning experiment!
+2021-09-16 18:20:17,572 - speechbrain.core - INFO - Experiment folder: results/tokenizer_seg_bpe5k_char
+2021-09-16 18:20:17,773 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==21.2.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+certifi==2021.5.30
+cffi==1.14.6
+cfgv==3.3.0
+charset-normalizer==2.0.4
+click==8.0.1
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+debugpy==1.3.0
+decorator==5.0.9
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+huggingface-hub==0.0.16
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==3.2
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+joblib==1.0.1
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+mkl-random @ file:///tmp/build/80754af9/mkl_random_1626186064646/work
+mkl-service==2.4.0
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+nodeenv==1.6.0
+notebook==6.4.0
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+olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work
+packaging==21.0
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+pydub @ file:///home/conda/feedstock_root/build_artifacts/pydub_1615612442567/work
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+scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1621370412049/work
+scipy==1.7.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+six @ file:///tmp/build/80754af9/six_1623709665295/work
+-e git+https://github.com/speechbrain/speechbrain.git@2ec4839746970875fc763aa354c44a3356685ef6#egg=speechbrain
+terminado==0.10.1
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+threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
+toml==0.10.2
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+widgetsnbextension==3.5.1
+xxhash==2.0.2
+yamllint==1.23.0
+
+
+2021-09-16 18:20:17,777 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-09-16 18:20:19,582 - speechbrain.tokenizers.SentencePiece - INFO - Train tokenizer with type:char
+2021-09-16 18:20:19,582 - speechbrain.tokenizers.SentencePiece - INFO - Extract transcription sequences from:results/prepare_seg/train.json
+2021-09-16 18:20:19,788 - speechbrain.tokenizers.SentencePiece - INFO - Text file created at: results/prepare_seg/train.txt
+2021-09-16 18:20:20,080 - speechbrain.tokenizers.SentencePiece - INFO - ==== Loading Tokenizer ===
+2021-09-16 18:20:20,080 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer path: results/tokenizer_seg_bpe5k_char/5000_char.model
+2021-09-16 18:20:20,080 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer vocab_size: 5000
+2021-09-16 18:20:20,080 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer type: char
+2021-09-16 18:20:20,082 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-16 18:20:32,793 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/dev.json
+2021-09-16 18:20:32,793 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-16 18:20:32,793 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-16 18:20:32,793 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-16 18:20:45,713 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/eval.json
+2021-09-16 18:20:45,713 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-16 18:20:45,714 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-16 18:20:45,714 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-16 18:20:58,798 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/test.json
+2021-09-16 18:20:58,798 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-16 18:20:58,798 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-16 18:57:44,327 - speechbrain.core - INFO - Beginning experiment!
+2021-09-16 18:57:44,327 - speechbrain.core - INFO - Experiment folder: results/tokenizer_seg_bpe5k_char
+2021-09-16 18:57:44,526 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==21.2.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+certifi==2021.5.30
+cffi==1.14.6
+cfgv==3.3.0
+charset-normalizer==2.0.4
+click==8.0.1
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+debugpy==1.3.0
+decorator==5.0.9
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+huggingface-hub==0.0.16
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==3.2
+ipykernel==6.0.2
+ipython==7.25.0
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+mkl-fft==1.3.0
+mkl-random @ file:///tmp/build/80754af9/mkl_random_1626186064646/work
+mkl-service==2.4.0
+more-itertools==8.9.0
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+olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work
+packaging==21.0
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+pydub @ file:///home/conda/feedstock_root/build_artifacts/pydub_1615612442567/work
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+scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1621370412049/work
+scipy==1.7.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+six @ file:///tmp/build/80754af9/six_1623709665295/work
+-e git+https://github.com/speechbrain/speechbrain.git@2ec4839746970875fc763aa354c44a3356685ef6#egg=speechbrain
+terminado==0.10.1
+testpath==0.5.0
+threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
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+widgetsnbextension==3.5.1
+xxhash==2.0.2
+yamllint==1.23.0
+
+
+2021-09-16 18:57:44,530 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-09-16 18:57:46,443 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer is already trained.
+2021-09-16 18:57:46,443 - speechbrain.tokenizers.SentencePiece - INFO - ==== Loading Tokenizer ===
+2021-09-16 18:57:46,443 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer path: results/tokenizer_seg_bpe5k_char/5000_char.model
+2021-09-16 18:57:46,443 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer vocab_size: 5000
+2021-09-16 18:57:46,444 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer type: char
+2021-09-16 18:57:46,447 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-16 18:57:59,747 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/dev.json
+2021-09-16 18:57:59,747 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-16 18:57:59,747 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-16 18:57:59,747 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-16 18:58:12,647 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/eval.json
+2021-09-16 18:58:12,647 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-16 18:58:12,647 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-16 18:58:12,647 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-16 18:58:25,444 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/test.json
+2021-09-16 18:58:25,444 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-16 18:58:25,444 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-18 01:09:55,556 - speechbrain.core - INFO - Beginning experiment!
+2021-09-18 01:09:55,576 - speechbrain.core - INFO - Experiment folder: results/tokenizer_seg_bpe5k_char
+2021-09-18 01:09:55,858 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==21.2.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+certifi==2021.5.30
+cffi==1.14.6
+cfgv==3.3.0
+charset-normalizer==2.0.4
+click==8.0.1
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+debugpy==1.3.0
+decorator==5.0.9
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+huggingface-hub==0.0.16
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==3.2
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
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+jedi==0.18.0
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+matplotlib-inline==0.1.2
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+mistune==0.8.4
+mkl-fft==1.3.0
+mkl-random @ file:///tmp/build/80754af9/mkl_random_1626186064646/work
+mkl-service==2.4.0
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+olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work
+packaging==21.0
+pandas==1.3.0
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+parso==0.8.2
+pathspec==0.9.0
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+platformdirs==2.0.2
+pluggy==0.13.1
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+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
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+py==1.10.0
+pyarrow==5.0.0
+pycodestyle==2.5.0
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+pydub @ file:///home/conda/feedstock_root/build_artifacts/pydub_1615612442567/work
+pyflakes==2.1.1
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+pytest==5.4.1
+python-dateutil==2.8.2
+pytube==10.9.3
+PyYAML==5.4.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.26.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1621370412049/work
+scipy==1.7.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+six @ file:///tmp/build/80754af9/six_1623709665295/work
+-e git+https://github.com/speechbrain/speechbrain.git@2ec4839746970875fc763aa354c44a3356685ef6#egg=speechbrain
+terminado==0.10.1
+testpath==0.5.0
+threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+urllib3==1.26.6
+virtualenv==20.6.0
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xxhash==2.0.2
+yamllint==1.23.0
+
+
+2021-09-18 01:09:55,862 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-09-18 01:09:57,778 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer is already trained.
+2021-09-18 01:09:57,778 - speechbrain.tokenizers.SentencePiece - INFO - ==== Loading Tokenizer ===
+2021-09-18 01:09:57,778 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer path: results/tokenizer_seg_bpe5k_char/5000_char.model
+2021-09-18 01:09:57,778 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer vocab_size: 5000
+2021-09-18 01:09:57,778 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer type: char
+2021-09-18 01:09:57,780 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-18 01:10:15,083 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/dev.json
+2021-09-18 01:10:15,083 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-18 01:10:15,083 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-18 01:10:15,083 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-18 01:10:32,413 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/eval.json
+2021-09-18 01:10:32,413 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-18 01:10:32,413 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-09-18 01:10:32,413 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-09-18 01:10:49,667 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/test.json
+2021-09-18 01:10:49,667 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-09-18 01:10:49,667 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-10-04 03:04:09,349 - speechbrain.core - INFO - Beginning experiment!
+2021-10-04 03:04:09,349 - speechbrain.core - INFO - Experiment folder: results/tokenizer_seg_bpe5k_char
+2021-10-04 03:04:09,986 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
+joblib==1.0.1
+jsonpatch==1.22
+jsonpointer==2.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.12
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+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keyring==18.0.1
+kiwisolver==1.3.1
+language-selector==0.1
+launchpadlib==1.10.13
+lazr.restfulclient==0.14.2
+lazr.uri==1.0.3
+macaroonbakery==1.3.1
+MarkupSafe==1.1.0
+matplotlib==3.4.2
+matplotlib-inline==0.1.2
+mccabe==0.6.1
+mistune==0.8.4
+more-itertools==4.2.0
+multiprocess==0.70.12.2
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+netifaces==0.10.4
+nodeenv==1.6.0
+notebook==6.4.0
+numpy==1.21.2
+nvidia-ml-py3==7.352.0
+oauthlib==3.1.0
+packaging==21.0
+pandas==1.3.0
+pandocfilters==1.4.3
+parso==0.8.2
+pathspec==0.9.0
+pexpect==4.6.0
+pickleshare==0.7.5
+Pillow==8.3.1
+platformdirs==2.0.2
+pluggy==0.13.1
+pre-commit==2.15.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.6.1
+psutil==5.8.0
+ptyprocess==0.7.0
+py==1.10.0
+pyarrow==5.0.0
+pyasn1==0.4.2
+pyasn1-modules==0.2.1
+pycairo==1.16.2
+pycodestyle==2.5.0
+pycparser==2.20
+pycups==1.9.73
+pyflakes==2.1.1
+Pygments==2.9.0
+PyGObject==3.36.0
+PyHamcrest==1.9.0
+PyJWT==1.7.1
+pymacaroons==0.13.0
+PyMySQL==1.0.2
+PyNaCl==1.3.0
+pyOpenSSL==19.0.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.15.5
+pyserial==3.4
+pytest==5.4.1
+python-apt==2.0.0+ubuntu0.20.4.5
+python-dateutil==2.8.2
+python-debian===0.1.36ubuntu1
+pytube==10.9.3
+pytz==2019.3
+PyYAML==5.3.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.22.0
+requests-unixsocket==0.2.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scipy==1.7.1
+screen-resolution-extra==0.0.0
+SecretStorage==2.3.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+service-identity==18.1.0
+simplejson==3.16.0
+six==1.14.0
+sos==4.1
+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
+terminado==0.10.1
+testpath==0.5.0
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+Twisted==18.9.0
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
+urllib3==1.25.8
+virtualenv==20.6.0
+wadllib==1.3.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+
+
+2021-10-04 03:04:09,989 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-10-04 03:04:10,195 - speechbrain.core - ERROR - Exception:
+Traceback (most recent call last):
+  File "Tokenizer/train.py", line 21, in <module>
+    run_on_main(
+  File "/home/wayne/speechbrain/speechbrain/utils/distributed.py", line 61, in run_on_main
+    func(*args, **kwargs)
+  File "/mnt/md0/user_wayne/speechbrain/recipes/MATBN/Tokenizer/matbn_prepare.py", line 67, in prepare_matbn
+    segments_info = extract_segments_info(segments_path)
+  File "/mnt/md0/user_wayne/speechbrain/recipes/MATBN/Tokenizer/matbn_prepare.py", line 117, in extract_segments_info
+    with open(segments_path, "r", encoding="utf-8") as segments_file:
+FileNotFoundError: [Errno 2] No such file or directory: '/home/wayne/CORPUS/MATBN_SEG/data/eval/segments'
+2021-10-04 03:04:50,119 - speechbrain.core - INFO - Beginning experiment!
+2021-10-04 03:04:50,119 - speechbrain.core - INFO - Experiment folder: results/tokenizer_seg_bpe5k_char
+2021-10-04 03:04:50,727 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
+joblib==1.0.1
+jsonpatch==1.22
+jsonpointer==2.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.12
+jupyter-console==6.4.0
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keyring==18.0.1
+kiwisolver==1.3.1
+language-selector==0.1
+launchpadlib==1.10.13
+lazr.restfulclient==0.14.2
+lazr.uri==1.0.3
+macaroonbakery==1.3.1
+MarkupSafe==1.1.0
+matplotlib==3.4.2
+matplotlib-inline==0.1.2
+mccabe==0.6.1
+mistune==0.8.4
+more-itertools==4.2.0
+multiprocess==0.70.12.2
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+netifaces==0.10.4
+nodeenv==1.6.0
+notebook==6.4.0
+numpy==1.21.2
+nvidia-ml-py3==7.352.0
+oauthlib==3.1.0
+packaging==21.0
+pandas==1.3.0
+pandocfilters==1.4.3
+parso==0.8.2
+pathspec==0.9.0
+pexpect==4.6.0
+pickleshare==0.7.5
+Pillow==8.3.1
+platformdirs==2.0.2
+pluggy==0.13.1
+pre-commit==2.15.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.6.1
+psutil==5.8.0
+ptyprocess==0.7.0
+py==1.10.0
+pyarrow==5.0.0
+pyasn1==0.4.2
+pyasn1-modules==0.2.1
+pycairo==1.16.2
+pycodestyle==2.5.0
+pycparser==2.20
+pycups==1.9.73
+pyflakes==2.1.1
+Pygments==2.9.0
+PyGObject==3.36.0
+PyHamcrest==1.9.0
+PyJWT==1.7.1
+pymacaroons==0.13.0
+PyMySQL==1.0.2
+PyNaCl==1.3.0
+pyOpenSSL==19.0.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.15.5
+pyserial==3.4
+pytest==5.4.1
+python-apt==2.0.0+ubuntu0.20.4.5
+python-dateutil==2.8.2
+python-debian===0.1.36ubuntu1
+pytube==10.9.3
+pytz==2019.3
+PyYAML==5.3.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.22.0
+requests-unixsocket==0.2.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scipy==1.7.1
+screen-resolution-extra==0.0.0
+SecretStorage==2.3.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+service-identity==18.1.0
+simplejson==3.16.0
+six==1.14.0
+sos==4.1
+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
+terminado==0.10.1
+testpath==0.5.0
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+Twisted==18.9.0
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
+urllib3==1.25.8
+virtualenv==20.6.0
+wadllib==1.3.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+
+
+2021-10-04 03:04:50,730 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-10-04 03:04:52,523 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer is already trained.
+2021-10-04 03:04:52,523 - speechbrain.tokenizers.SentencePiece - INFO - ==== Loading Tokenizer ===
+2021-10-04 03:04:52,523 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer path: results/tokenizer_seg_bpe5k_char/5000_char.model
+2021-10-04 03:04:52,523 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer vocab_size: 5000
+2021-10-04 03:04:52,523 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer type: char
+2021-10-04 03:04:52,558 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-10-04 03:05:04,514 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/dev.json
+2021-10-04 03:05:04,514 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-10-04 03:05:04,514 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-10-04 03:05:04,514 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-10-04 03:05:16,440 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/eval.json
+2021-10-04 03:05:16,440 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-10-04 03:05:16,440 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-10-04 03:05:16,440 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-10-04 03:05:28,162 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/test.json
+2021-10-04 03:05:28,162 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-10-04 03:05:28,162 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-10-04 19:38:40,556 - speechbrain.core - INFO - Beginning experiment!
+2021-10-04 19:38:40,556 - speechbrain.core - INFO - Experiment folder: results/tokenizer_seg_bpe5k_char
+2021-10-04 19:38:41,182 - speechbrain.utils.superpowers - DEBUG - appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==19.3.0
+Automat==0.8.0
+autopep8==1.5.7
+backcall==0.2.0
+backports.entry-points-selectable==1.1.0
+black==19.10b0
+bleach==3.3.1
+blessings==1.7
+blinker==1.4
+bottle==0.12.19
+certifi==2019.11.28
+cffi==1.14.6
+cfgv==3.3.0
+chardet==3.0.4
+Click==7.0
+cloud-init==21.2
+colorama==0.4.3
+command-not-found==0.3
+configobj==5.0.6
+constantly==15.1.0
+cryptography==2.8
+cupshelpers==1.0
+cycler==0.10.0
+d2l==0.16.6
+datasets==1.11.0
+dbus-python==1.2.16
+debugpy==1.3.0
+decorator==5.0.9
+defer==1.0.6
+defusedxml==0.7.1
+dill==0.3.4
+distlib==0.3.2
+distro==1.4.0
+distro-info===0.23ubuntu1
+entrypoints==0.3
+filelock==3.0.12
+flake8==3.7.9
+fsspec==2021.7.0
+gpustat==0.6.0
+gpuview==0.4.0
+httplib2==0.14.0
+huggingface-hub==0.0.16
+hyperlink==19.0.0
+HyperPyYAML==1.0.0
+identify==2.2.11
+idna==2.8
+importlib-metadata==1.5.0
+incremental==16.10.1
+ipykernel==6.0.2
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.10.1
+joblib==1.0.1
+jsonpatch==1.22
+jsonpointer==2.0
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.12
+jupyter-console==6.4.0
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+keyring==18.0.1
+kiwisolver==1.3.1
+language-selector==0.1
+launchpadlib==1.10.13
+lazr.restfulclient==0.14.2
+lazr.uri==1.0.3
+macaroonbakery==1.3.1
+MarkupSafe==1.1.0
+matplotlib==3.4.2
+matplotlib-inline==0.1.2
+mccabe==0.6.1
+mistune==0.8.4
+more-itertools==4.2.0
+multiprocess==0.70.12.2
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+netifaces==0.10.4
+nodeenv==1.6.0
+notebook==6.4.0
+numpy==1.21.2
+nvidia-ml-py3==7.352.0
+oauthlib==3.1.0
+packaging==21.0
+pandas==1.3.0
+pandocfilters==1.4.3
+parso==0.8.2
+pathspec==0.9.0
+pexpect==4.6.0
+pickleshare==0.7.5
+Pillow==8.3.1
+platformdirs==2.0.2
+pluggy==0.13.1
+pre-commit==2.15.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.6.1
+psutil==5.8.0
+ptyprocess==0.7.0
+py==1.10.0
+pyarrow==5.0.0
+pyasn1==0.4.2
+pyasn1-modules==0.2.1
+pycairo==1.16.2
+pycodestyle==2.5.0
+pycparser==2.20
+pycups==1.9.73
+pyflakes==2.1.1
+Pygments==2.9.0
+PyGObject==3.36.0
+PyHamcrest==1.9.0
+PyJWT==1.7.1
+pymacaroons==0.13.0
+PyMySQL==1.0.2
+PyNaCl==1.3.0
+pyOpenSSL==19.0.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.15.5
+pyserial==3.4
+pytest==5.4.1
+python-apt==2.0.0+ubuntu0.20.4.5
+python-dateutil==2.8.2
+python-debian===0.1.36ubuntu1
+pytube==10.9.3
+pytz==2019.3
+PyYAML==5.3.1
+pyzmq==22.1.0
+qtconsole==5.1.1
+QtPy==1.9.0
+regex==2021.7.6
+requests==2.22.0
+requests-unixsocket==0.2.0
+ruamel.yaml==0.17.10
+ruamel.yaml.clib==0.2.6
+scipy==1.7.1
+screen-resolution-extra==0.0.0
+SecretStorage==2.3.1
+Send2Trash==1.7.1
+sentencepiece==0.1.96
+service-identity==18.1.0
+simplejson==3.16.0
+six==1.14.0
+sos==4.1
+-e git+https://github.com/speechbrain/speechbrain.git@1d194bfc51ae20b9e38596d220cdf0f4977e69de#egg=speechbrain
+ssh-import-id==5.10
+supervisor==4.1.0
+systemd-python==234
+terminado==0.10.1
+testpath==0.5.0
+toml==0.10.2
+torch==1.8.1
+torchaudio==0.8.1
+torchvision==0.10.0
+tornado==6.1
+tqdm==4.62.2
+traitlets==5.0.5
+Twisted==18.9.0
+typed-ast==1.4.3
+typing-extensions==3.10.0.0
+ubuntu-advantage-tools==27.2
+ufw==0.36
+unattended-upgrades==0.1
+urllib3==1.25.8
+virtualenv==20.6.0
+wadllib==1.3.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+xkit==0.0.0
+xxhash==2.0.2
+yamllint==1.23.0
+zipp==1.0.0
+zope.interface==4.7.1
+
+
+2021-10-04 19:38:41,186 - speechbrain.utils.superpowers - DEBUG - e3e51338
+
+
+2021-10-04 19:38:43,292 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer is already trained.
+2021-10-04 19:38:43,292 - speechbrain.tokenizers.SentencePiece - INFO - ==== Loading Tokenizer ===
+2021-10-04 19:38:43,292 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer path: results/tokenizer_seg_bpe5k_char/5000_char.model
+2021-10-04 19:38:43,292 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer vocab_size: 5000
+2021-10-04 19:38:43,292 - speechbrain.tokenizers.SentencePiece - INFO - Tokenizer type: char
+2021-10-04 19:38:43,294 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-10-04 19:38:54,904 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/dev.json
+2021-10-04 19:38:54,904 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-10-04 19:38:54,904 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-10-04 19:38:54,904 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-10-04 19:39:06,509 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/eval.json
+2021-10-04 19:39:06,510 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-10-04 19:39:06,510 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0
+2021-10-04 19:39:06,510 - speechbrain.tokenizers.SentencePiece - INFO - ==== Accuracy checking for recovering text from tokenizer ===
+2021-10-04 19:39:18,349 - speechbrain.tokenizers.SentencePiece - INFO - recover words from: results/prepare_seg/test.json
+2021-10-04 19:39:18,349 - speechbrain.tokenizers.SentencePiece - INFO - Wrong recover words: 0
+2021-10-04 19:39:18,349 - speechbrain.tokenizers.SentencePiece - WARNING - accuracy recovering words: 1.0

ASR-model/tokenizer_seg_bpe5k_char/train.py

@@ -0,0 +1,30 @@
+import sys
+
+import speechbrain as sb
+from hyperpyyaml import load_hyperpyyaml
+from speechbrain.utils.distributed import run_on_main
+
+if __name__ == "__main__":
+    hparams_file_path, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
+
+    with open(hparams_file_path) as hparams_file:
+        hparams = load_hyperpyyaml(hparams_file, overrides)
+
+    sb.create_experiment_directory(
+        experiment_directory=hparams["output_folder"],
+        hyperparams_to_save=hparams_file_path,
+        overrides=overrides,
+    )
+
+    from matbn_prepare import prepare_matbn
+
+    run_on_main(
+        prepare_matbn,
+        kwargs={
+            "dataset_folder": hparams["dataset_folder"],
+            "save_folder": hparams["prepare_folder"],
+            "keep_unk": hparams["keep_unk"],
+        },
+    )
+
+    hparams["tokenizer"]()