superb_er

(s3prl.problem.common.superb_er)

The setting of Superb ER

Authors

• Tzu-Hsien Huang 2021

• Leo 2021

• Leo 2022

SuperbER

class s3prl.problem.common.superb_er.SuperbER

Bases: SuperbSID

default_config() → dict

The default arguments for run in yaml. Note that for the fields with inner values, like build_model, the outer field name corresponds to a method name, so you can find the method build_model. Furthermore, the values inside that field will be directly passed into the method. So by changing these inner values, you can directly affect the behavior of the corresponding method. See the method documentation for all the supported arguments and their meanings.

The methods affected by the following config are: prepare_data build_encoder build_dataset build_batch_sampler build_upstream build_featurizer build_downstream build_model build_task build_optimizer build_scheduler save_model save_task train evaluate

start: 0
stop: null
target_dir: ???
cache_dir: null
remove_all_cache: false
prepare_data:
  iemocap: ???
  test_fold: ???
build_encoder: {}
build_dataset: {}
build_batch_sampler:
  train:
    batch_size: 4
    shuffle: true
  valid:
    batch_size: 4
  test:
    batch_size: 4
build_upstream:
  name: ???
build_featurizer:
  layer_selections: null
  normalize: false
build_downstream:
  hidden_size: 256
build_model:
  upstream_trainable: false
build_task: {}
build_optimizer:
  name: Adam
  conf:
    lr: 0.0001
build_scheduler:
  name: ExponentialLR
  gamma: 0.9
save_model: {}
save_task: {}
train:
  total_steps: 30000
  log_step: 500
  eval_step: 1000
  save_step: 1000
  gradient_clipping: 1.0
  gradient_accumulate: 8
  valid_metric: accuracy
  valid_higher_better: true
  auto_resume: true
  resume_ckpt_dir: null
evaluate: {}

prepare_data(prepare_data: dict, target_dir: str, cache_dir: str, get_path_only: bool = False)

Prepare the task-specific data metadata (path, labels…). By default call iemocap_for_superb with **prepare_data

Parameters:

• prepare_data (dict) – same in default_config, support arguments in iemocap_for_superb

• target_dir (str) – Parse your corpus and save the csv file into this directory

• cache_dir (str) – If the parsing or preprocessing takes too long time, you can save the temporary files into this directory. This directory is expected to be shared across different training sessions (different hypers and target_dir)

• get_path_only (str) – Directly return the filepaths no matter they exist or not.

Returns:

tuple

1. train_path (str)

2. valid_path (str)

3. test_paths (List[str])

Each path (str) should be a csv file containing the following columns:

column	description
id	(str) - the unique id for this data point
wav_path	(str) - the absolute path of the waveform file
label	(str) - a string label of the waveform
start_sec	(float) - optional, load the waveform from start_sec seconds. If not presented or is math.nan, load from the beginning.
end_sec	(float) - optional, load the waveform from end_sec seconds. If not presented or is math.nan, load to the end.

build_batch_sampler(build_batch_sampler: dict, target_dir: str, cache_dir: str, mode: str, data_csv: str, dataset)

Return the batch sampler for torch DataLoader.

Parameters:

• build_batch_sampler (dict) –

  same in default_config

  key	description
  train	(dict) - arguments for FixedBatchSizeBatchSampler
  valid	(dict) - arguments for FixedBatchSizeBatchSampler
  test	(dict) - arguments for FixedBatchSizeBatchSampler

• target_dir (str) – Current experiment directory

• cache_dir (str) – If the preprocessing takes too long time, save the temporary files into this directory. This directory is expected to be shared across different training sessions (different hypers and target_dir)

• mode (str) – train/valid/test

• data_csv (str) – the mode specific csv from prepare_data

• dataset – the dataset from build_dataset

Returns:

batch sampler for torch DataLoader

build_collate_fn(build_collate_fn: dict, mode: str)

By default returns s3prl.dataset.base.default_collate_fn

Parameters:

• build_collate_fn (dict) – same in default_config, no argument supported for now

• mode (str) – train, valid, or test

Returns:

callable

the collate_fn for torch DataLoader in train/valid/test mode

build_dataset(build_dataset: dict, target_dir: str, cache_dir: str, mode: str, data_csv: str, encoder_path: str, frame_shift: int)

Build the dataset for train/valid/test.

Parameters:

• build_dataset (dict) –

  same in default_config. with train, valid, test keys, each is a dictionary with the following supported options:

  key	description
  max_secs	(float) - If a waveform is longer than max_secs seconds, randomly crop the waveform into max_secs seconds
  sox_effects	(List[List[str]]) - If not None, apply sox effects on the utterance

• target_dir (str) – Current experiment directory

• cache_dir (str) – If the preprocessing takes too long time, you can save the temporary files into this directory. This directory is expected to be shared across different training sessions (different hypers and target_dir)

• mode (str) – train/valid/test

• data_csv (str) – The metadata csv file for the specific mode

• encoder_path (str) – The pickled encoder path for encoding the labels

Returns:

torch Dataset

For all train/valid/test mode, the dataset should return each item as a dictionary containing the following keys:

key	description
x	(torch.FloatTensor) - the waveform in (seq_len, 1)
x_len	(int) - the waveform length seq_len
class_id	(int) - the encoded class id
label	(str) - the class name
unique_name	(str) - the unique id for this datapoint

build_downstream(build_downstream: dict, downstream_input_size: int, downstream_output_size: int, downstream_input_stride: int)

Return the task-specific downstream model. By default build the MeanPoolingLinear model

Parameters:

• build_downstream (dict) – same in default_config, support arguments of MeanPoolingLinear

• downstream_input_size (int) – the required input size of the model

• downstream_output_size (int) – the required output size of the model

• downstream_input_stride (int) – the input feature’s stride (from 16 KHz)

Returns:

s3prl.nn.interface.AbsUtteranceModel

build_encoder(build_encoder: dict, target_dir: str, cache_dir: str, train_csv_path: str, valid_csv_path: str, test_csv_paths: list, get_path_only: bool = False)

Build the encoder (for the labels) given the data metadata, and return the saved encoder path. By default generate and save a s3prl.dataio.encoder.CategoryEncoder from the label column of all the csv files.

Parameters:

• build_encoder (dict) – same in default_config, no argument supported for now

• target_dir (str) – Save your encoder into this directory

• cache_dir (str) – If the preprocessing takes too long time, you can save the temporary files into this directory. This directory is expected to be shared across different training sessions (different hypers and target_dir)

• train_csv_path (str) – the train path from prepare_data

• valid_csv_path (str) – the valid path from prepare_data

• test_csv_paths (List[str]) – the test paths from prepare_data

• get_path_only (str) – Directly return the filepaths no matter they exist or not.

Returns:

str

encoder_path: The encoder should be saved in the pickle format

build_featurizer(build_featurizer: dict, upstream)

By default build the featurizer with s3prl.nn.Featurizer

Parameters:

• build_featurizer (dict) – same in default_config, arguments for s3prl.nn.Featurizer

• upstream (AbsUpstream) – the upstream model built by build_upstream

Returns:

s3prl.nn.interface.AbsFeaturizer

Return the featurizer model. The featurizer is used to reduce the multiple hidden states returned from the upstream model (built by build_upstream) into a single hidden state, so can be easliy fed into the downstream model

build_model(build_model: dict, model_output_size: int, build_upstream: dict, build_featurizer: dict, build_downstream: dict)

By default build model with s3prl.nn.upstream.UpstreamDownstreamModel

Parameters:

• build_model (dict) – same in default_config, arguments for s3prl.nn.upstream.UpstreamDownstreamModel

• model_output_size (int) – the required model’s output hidden size

• build_upstream (dict) – same in default_config, refer to build_upstream

• build_featurizer (dict) – same in default_config, refer to build_featurizer

• build_downstream (dict) – same in default_config, refer to build_downstream

Returns:

torch.nn.Module

Return the entire model for the task, which takes the direct items from DataLoader as the input. Usually, the components can be built by build_upstream, build_featurizer, build_downstream, and are concated together to get the final model. The upstream extracts multiple hidden states, the featuizer reduce them into a single hidden state, and the downstream takes the hidden states as the feature for the downstream-specific model.

build_optimizer(build_optimizer: dict, parameters)

Parameters:

• build_optimizer (dict) –

  same in default_config, refer to below

  key	description
  name	(str) - the optimizer class name in torch.optim
  conf	(dict) - the arguments for initializing the optimizer class. e.g. {"lr": 1.0e-4}

• parameters (iterable) – the standard params accepted by torch.optim.Optimizer.

Returns:

torch.optim.Optimizer

An optimizer following standard torch usage

build_scheduler(build_scheduler: dict, optimizer)

Parameters:

• build_scheduler (dict) –

  same in default_config

  key	description
  name	(str) - the scheduler class name in torch.optim.lr_scheduler
  conf	(dict) - the arguments for initializing the scheduler class. e.g. {"gamma": 0.01} for torch.optim.lr_scheduler.StepLR

• optimizer – the standard torch optimizer accepted by Scheduler in torch.optim.lr_scheduler.

Returns:

torch scheduler

A scheduler following standard torch usage

build_task(build_task: dict, model: Module, encoder, valid_df: Optional[DataFrame] = None, test_df: Optional[DataFrame] = None)

Build the task, which defines the logics for every train/valid/test forward step for the model, and the logics for how to reduce all the batch results from multiple train/valid/test steps into metrics

By default build UtteranceClassificationTask

Parameters:

• build_task (dict) – same in default_config, no argument supported for now

• model (torch.nn.Module) – the model built by build_model

• encoder – the encoder built by build_encoder

Returns:

Task

build_upstream(build_upstream: dict)

By default build the upstream with s3prl.nn.upstream.S3PRLUpstream

Parameters:

build_upstream (dict) – same in default_config, arguments for s3prl.nn.upstream.S3PRLUpstream

Returns:

s3prl.nn.interface.AbsUpstream

Return an upstream model, whose forward takes the waveform input and returns multiple hidden states as features.

evaluate(evaluate: dict, mode: str, task, dataset, batch_sampler, collate_fn, eval_batch: int, dump_dir: str, device: str, num_workers: int)

The evaluate routine used by train (during validation phase) and run (during testing phase).

Parameters:

• evaluate (dict) – same in default_config, no argument supported for now

• **others – only meaningful when you want to override this train method, which is not the common case. Hence we skip the documentation for now.

classmethod get_class_from_name(name: str)

Parameters:

name (str) – the __name__ of the problem class

Returns:

Problem

load_model(model_ckpt_dir: str)

Return the saved model.

Parameters:

model_ckpt_dir (str) – Restore the model with build_model and the checkpoint saved in this directory.

Returns:

torch.nn.Module

load_model_and_task(ckpts_dir: str, task_overrides: Optional[dict] = None)

This is a helper method to combine load_model and load_task together to directly load the model and the task. This method assumes the model is saved under ckpts_dir / 'model' and the task is saved under ckpts_dir / 'task'

Returns:

tuple

1. model (torch.nn.Module)

2. task (s3prl.task.Task)

load_task(task_ckpt_dir: str, model: Module, task_overrides: Optional[dict] = None)

Return the saved task.

Parameters:

• task_ckpt_dir (str) – Restore the task with build_task and the checkpoint saved in this directory.

• model (torch.nn.Module) – the model for the task, since the model is separately saved and is required for build_task.

• task_overrides (dict) – overrides the saved initialization arguments, so can change the loaded task’s behavior. Like, change the decoding hyperparameters.

Returns:

s3prl.task.Task

main(args: Optional[List[str]] = None)

run(target_dir: str, cache_dir: Optional[str] = None, remove_all_cache: bool = False, start: int = 0, stop: Optional[int] = None, num_workers: int = 6, eval_batch: int = -1, device: str = 'cuda', world_size: int = 1, rank: int = 0, test_ckpt_dir: Optional[str] = None, prepare_data: Optional[dict] = None, build_encoder: Optional[dict] = None, build_dataset: Optional[dict] = None, build_batch_sampler: Optional[dict] = None, build_collate_fn: Optional[dict] = None, build_upstream: Optional[dict] = None, build_featurizer: Optional[dict] = None, build_downstream: Optional[dict] = None, build_model: Optional[dict] = None, build_task: Optional[dict] = None, build_optimizer: Optional[dict] = None, build_scheduler: Optional[dict] = None, save_model: Optional[dict] = None, save_task: Optional[dict] = None, train: Optional[dict] = None, evaluate: Optional[dict] = None)

stage	description
0	Parse the corpus and save the metadata file (waveform path, label…)
1	Build the encoder to encode the labels
2	Train the model
3	Evaluate the model on multiple test sets

Parameters:

• target_dir (str) – The directory that stores the script result.

• cache_dir (str) – The directory that caches the processed data. Default: /home/user/.cache/s3prl/data

• remove_all_cache (bool) – Whether to remove all the cache stored under cache_dir. Default: False

• start (int) – The starting stage of the problem script. Default: 0

• stop (int) – The stoping stage of the problem script, set None to reach the final stage. Default: None

• num_workers (int) – num_workers for all the torch DataLoder

• eval_batch (int) – During evaluation (valid or test), limit the number of batch. This is helpful for the fast development to check everything won’t crash. If is -1, disable this feature and evaluate the entire epoch. Default: -1

• device (str) – The device type for all torch-related operation: “cpu” or “cuda” Default: “cuda”

• world_size (int) – How many processes are running this script simultaneously (in parallel). Usually this is just 1, however if you are runnig distributed training, this should be > 1. Default: 1

• rank (int) – When distributed training, world_size > 1. Take world_size == 8 for example, this means 8 processes (8 GPUs) are runing in parallel. The script needs to know which process among 8 processes it is. In this case, rank can range from 0~7. All the 8 processes have the same world_size but different rank (process id).

• test_ckpt_dir (str) – Specify the checkpoint path for testing. If not, use the validation best checkpoint under the given target_dir directory.

• **kwds – The other arguments like prepare_data and build_model are method specific-arguments for methods like prepare_data and build_model, and will not be used in the core run logic. See the specific method documentation for their supported arguments and meaning

save_model(save_model: dict, model_ckpt_dir: str, build_model_all_args: dict, model: Module)

Save the model state_dict and the model initialization arguments into the given directory. If you override this method, it is highly possible you also need to override load_model

Parameters:

• save_model (dict) – same in default_config, so the user can save additional settings, like the configuration of the dataset by duplicating the dataset hypers inside the save_model field. You can rely on the omegaconf package to simplify the duplication.

• model_ckpt_dir (str) – save the model into the this directory.

• build_model_all_args (dict) – all the arguments of build_model. By saving this dictionary, you can easily reconstruct the same model by calling build_model with the saved dictionary.

• model (torch.nn.Module) – the model to be saved.

Returns:

None

save_task(save_task: dict, task_ckpt_dir: str, build_task_all_args_except_model: dict, task: Task)

Save the task’s state, task.get_state(), and the initialization arguments into the given directory. If you override this method, it is highly possible you also need to override load_task.

Parameters:

• save_task (dict) – same in default_config, so the user can save additional settings, like the configuration of the dataset by duplicating the dataset hypers inside the save_task field. You can rely on the omegaconf package to simplify the duplication.

• task_ckpt_dir (str) – save the task into this directory.

• build_task_all_args_except_model (dict) – all the arguments of build_task except the model argument since the model should be sapartely saved by save_model. By saving this dictionary, you can easily reconstruct the same task by calling build_task with the saved dictionary.

• task (Task) – the task to be saved.

Returns:

None

train(train: dict, train_dir: str, build_model_all_args: dict, build_task_all_args_except_model: dict, save_model: dict, save_task: dict, build_optimizer: dict, build_scheduler: dict, evaluate: dict, train_dataset, train_batch_sampler, train_collate_fn, valid_dataset, valid_batch_sampler, valid_collate_fn, num_workers: int, world_size: int, rank: int, eval_batch: int, device: str, global_config: Optional[dict] = None)

Parameters:

• train (dict) –

  same in default_config

  key	description
  total_steps	(int) - the total optimization steps
  log_step	(int) - logging frequency. log every log_step step
  eval_step	(int) - evaluation frequency. Evaluate every eval_step step. Note that you can control how many batch to evaluate to speed up the development by the eval_batch argument in run
  save_step	(int) - save the checkpoint every save_step step.
  gradient_clipping	(float) - clip the gradient. important for RNNs.
  gradient_accumulate	(int) - accumulate multiple steps’ gradient before updating network parameters to simulate large-batch optimization.
  valid_metric	(str) - the metric to select the best valid checkpoint. Different Tasks have different supported valid_metrics. See build_task for the supported metrics.
  valid_higher_better	(bool) - some metrics are higher better, while some are lower better this will affect how to save the best validation checkpoint.
  auto_resume	(bool) - if there are already the last checkpoint in target_dir (see run), whether to resume from it or delete it and start a new training session.
  resume_ckpt_dir	(str) - you can directly specify the checkpoint path to resume which is not necessary in target_dir (see run).
  seed	(int) - fix the seed before the training start
  keep_num_ckpts	(int) - to prevent saving too many checkpoints, only save the keep_num_ckpts latest checkpoints and delete the old ones.
  use_scheduler	(bool) - whether to use the scheduler

• **others – only meaningful when you want to override this train method, which is not the common case. Hence we skip the documentation for now.

iemocap_for_superb

s3prl.problem.common.superb_er.iemocap_for_superb(target_dir: str, cache_dir: str, iemocap: str, test_fold: int, valid_ratio: float = 0.2, get_path_only: bool = False)

Prepare IEMOCAP for emotion classfication with SUPERB protocol, following SuperbER.prepare_data format.

Note

In SUPERB protocol, you need to do 5-fold cross validation.

Also, only use 4 emotion classes: happy, angry, neutral, and sad with balanced data points and the excited class is merged into happy class.

Parameters:

• iemocap (str) – The root path of the IEMOCAP

• test_fold (int) – Which fold to use as the test fold, select from 0 to 4

• valid_ratio (float) – given the remaining 4 folds, how many data to use as the validation set

• **others – refer to SuperbER.prepare_data