Config

This module contains the main configuration objects for the trainer, data, and model. These configuration classes must be filled with appropriate values to launch any training or prediction jobs.

Classes:

• TrainerConfig: The configuration object for the trainer.

• ASRDataConfig: The configuration object for ASR data.

• ModelConfig: The configuration object for the model.

• DistConfig: The configuration object for distributed data parallel.

class speeq.config.ASRDataConfig(training_path: Union[str, Path], testing_path: Union[str, Path], train_speech_processor: IProcessor, test_speech_processor: IProcessor, text_processor: IProcessor, tokenizer_path: Union[str, Path], tokenizer_type: str = 'char_tokenizer', sep: str = ',', type: str = 'csv', text_key: str = 'text', speech_key: str = 'file_path', padding_type: str = 'dynamic', text_pad_max_len: int = -1, speech_pad_max_len: int = -1, add_sos_token: bool = True, add_eos_token: bool = True, use_blank_as_pad: bool = False, sort_key: str = '', reverse: bool = False, shuffle: bool = False)

Bases: object

The ASR data configuration

Args:

training_path (Union[str, Path]): The training data path.

testing_path (Union[str, Path]): The testing data path.

train_speech_processor (IProcessor): The training data speech processor, where the run method returns the speech data with shape [B] or [1, M], or […, M, F].

test_speech_processor (IProcessor): The testing data speech processor, where the run method returns the speech data with shape [B] or [1, M], or […, M, F].

text_processor (IProcessor): The text processor.

tokenizer_path (Union[str, Path]): The path to load or save the tokenizer.

tokenizer_type (str): The tokenizer type to be used. Default char_tokenizer

sep (str): The separator used in the CSV file. Default ‘,’.

type (str): the file and the dataset type. Default ‘csv’.

text_key (Optional[str]): The name of the column that holds the text data. Default ‘text’.

speech_key (Optional[str]): The name of the column that holds the audio file path. Default ‘file_path’

padding_type (str): The padding to use static or dynamic. Default ‘dynamic’.

text_pad_max_len (str): Used if padding_type is static, which set the maximum sequence length it has to be larger than the largest text sequence size in both training and testing data.

speech_pad_max_len (str): Used if padding_type is static, which set the maximum sequence length it has to be larger than the largest speech sequence size in both training and testing data.

add_sos (bool): A flag that indicates whether to add the Start of Sequence (SOS) token to the text sequence. Default is False.

add_eos (bool): A flag that indicates whether to add the End of Sequence (EOS) token to the text sequence. Default is False.

use_blank_as_pad (bool): A flag if the blank id to be used as padding. Default False.

sort_key (Optional[str]): The key to sort the data on. Default ‘’.

reverse (bool): A flag used if a sorting key is passed. If set to False, data will be sorted in ascending order. If set to True, data will be sorted in descending order. Default is False.

shuffle (bool): A flag indicating whether the dataset should be shuffled at each iteration. Default False.

add_eos_token: bool = True

add_sos_token: bool = True

padding_type: str = 'dynamic'

reverse: bool = False

sep: str = ','

shuffle: bool = False

sort_key: str = ''

speech_key: str = 'file_path'

speech_pad_max_len: int = -1

test_speech_processor: IProcessor

testing_path: Union[str, Path]

text_key: str = 'text'

text_pad_max_len: int = -1

text_processor: IProcessor

tokenizer_path: Union[str, Path]

tokenizer_type: str = 'char_tokenizer'

train_speech_processor: IProcessor

training_path: Union[str, Path]

type: str = 'csv'

use_blank_as_pad: bool = False

class speeq.config.DistConfig(port: int, n_gpus: int, address: str, backend: str)

Bases: object

The distributed data parallel configuration.

Args:

port (int): The port used.

n_gpus (int): The number of nodes/GPUs.

address (str): The master node address.

backend (str): The backend to be used for DDP.

address: str

backend: str

n_gpus: int

port: int

class speeq.config.ModelConfig(template: ITemplate, model_path: Union[str, Path] = '')

Bases: object

The model configuration.

Args:

template (ITemplate): The model template.

model_path (Union[str, Path]): The pre-trained checkpoint to load the weights from. Default ‘’.

model_path: Union[str, Path] = ''

template: ITemplate

class speeq.config.TrainerConfig(name: str, batch_size: int, epochs: int, outdir: ~typing.Union[~pathlib.Path, str], logdir: ~typing.Union[~pathlib.Path, str], log_steps_frequency: int, criterion: str, optimizer: str, optim_args: dict, scheduler_template: ~typing.Optional[~speeq.interfaces.ITemplate] = None, dist_config: ~typing.Optional[object] = None, device: str = 'cuda', logger: str = 'tb', n_logs: int = 5, clear_screen: bool = False, criterion_args: dict = <factory>, grad_acc_steps: int = 1, grad_clip_thresh: ~typing.Union[None, float] = None, grad_clip_norm_type: float = 2.0)

Bases: object

The trainer configuration

Args:

name (str): The trainer name. it has to be one of (‘seq2seq’, ‘ctc’, ‘transducer’).

batch_size (int): the batch size.

epochs (int): The number of training epochs.

outdir (Union[Path, str]): The path to save the results to.

logdir (Union[Path, str]): The path to save the logs to.

log_steps_frequency (int): The number of steps to log the results after.

criterion (str): The criterion name to be used.

optimizer (str): The name of the optimizer to be used.

optim_args (dict): The optimizer arguments.

ignore_optim_state (bool): If True, the state of the optimizer that is loaded when a pre-trained checkpoint is passed will be ignored. otherwise, the optimizer/scheduler that is passed will be used. Default False.

scheduler_template (Union[IScheduler, None]): The scheduler template to be used. Default None.

dist_config (Union[object, None]): The DDP configuration object, for a single node/GPU training use None. Default None.

logger (str): The logger name to be used. Default ‘tb’.

n_logs (int): The number of steps to log. Default 5. clear_screen (bool): whether to clear the screen after each log or not. Default False.

criterion_args (dict): The criterion arguments if there is any. Default {}.

grad_acc_steps (int): The number of steps to accumulate gradients over. Default 1.

grad_clip_thresh (Union[None, float]): max norm of the gradients. Default None.

grad_clip_norm_type (float): type of the used p-norm. Default 2.0.

batch_size: int

clear_screen: bool = False

criterion: str

criterion_args: dict

device: str = 'cuda'

dist_config: Optional[object] = None

epochs: int

grad_acc_steps: int = 1

grad_clip_norm_type: float = 2.0

grad_clip_thresh: Union[None, float] = None

ignore_optim_state = False

log_steps_frequency: int

logdir: Union[Path, str]

logger: str = 'tb'

n_logs: int = 5

name: str

optim_args: dict

optimizer: str

outdir: Union[Path, str]

scheduler_template: Optional[ITemplate] = None