|
|
from __future__ import annotations |
|
|
|
|
|
from s3prl.corpus.fluent_speech_commands import fsc_for_multiple_classfication |
|
|
from s3prl.dataset.utterance_classification_pipe import ( |
|
|
UtteranceMultipleCategoryClassificationPipe, |
|
|
) |
|
|
from s3prl.nn import MeanPoolingLinear |
|
|
from s3prl.sampler import FixedBatchSizeBatchSampler, MaxTimestampBatchSampler |
|
|
from s3prl.task.utterance_classification_task import ( |
|
|
UtteranceMultiClassClassificationTask, |
|
|
) |
|
|
from s3prl.util.configuration import default_cfg |
|
|
|
|
|
from .base import SuperbProblem |
|
|
|
|
|
|
|
|
class SuperbIC(SuperbProblem): |
|
|
""" |
|
|
Superb Intent Classification problem |
|
|
""" |
|
|
|
|
|
@default_cfg( |
|
|
**SuperbProblem.setup.default_except( |
|
|
corpus=dict( |
|
|
CLS=fsc_for_multiple_classfication, |
|
|
dataset_root="???", |
|
|
), |
|
|
train_datapipe=dict( |
|
|
CLS=UtteranceMultipleCategoryClassificationPipe, |
|
|
train_category_encoder=True, |
|
|
), |
|
|
train_sampler=dict( |
|
|
CLS=FixedBatchSizeBatchSampler, |
|
|
batch_size=32, |
|
|
shuffle=True, |
|
|
), |
|
|
valid_datapipe=dict( |
|
|
CLS=UtteranceMultipleCategoryClassificationPipe, |
|
|
), |
|
|
valid_sampler=dict( |
|
|
CLS=FixedBatchSizeBatchSampler, |
|
|
batch_size=32, |
|
|
), |
|
|
test_datapipe=dict( |
|
|
CLS=UtteranceMultipleCategoryClassificationPipe, |
|
|
), |
|
|
test_sampler=dict( |
|
|
CLS=FixedBatchSizeBatchSampler, |
|
|
batch_size=32, |
|
|
), |
|
|
downstream=dict( |
|
|
CLS=MeanPoolingLinear, |
|
|
hidden_size=256, |
|
|
), |
|
|
task=dict( |
|
|
CLS=UtteranceMultiClassClassificationTask, |
|
|
), |
|
|
) |
|
|
) |
|
|
@classmethod |
|
|
def setup(cls, **cfg): |
|
|
""" |
|
|
This setups the IC problem, containing train/valid/test datasets & samplers and a task object |
|
|
""" |
|
|
super().setup(**cfg) |
|
|
|
|
|
@default_cfg( |
|
|
**SuperbProblem.train.default_except( |
|
|
optimizer=dict( |
|
|
CLS="torch.optim.Adam", |
|
|
lr=1.0e-4, |
|
|
), |
|
|
trainer=dict( |
|
|
total_steps=200000, |
|
|
log_step=100, |
|
|
eval_step=5000, |
|
|
save_step=250, |
|
|
gradient_clipping=1.0, |
|
|
gradient_accumulate_steps=1, |
|
|
valid_metric="accuracy", |
|
|
valid_higher_better=True, |
|
|
), |
|
|
) |
|
|
) |
|
|
@classmethod |
|
|
def train(cls, **cfg): |
|
|
""" |
|
|
Train the setup problem with the train/valid datasets & samplers and the task object |
|
|
""" |
|
|
super().train(**cfg) |
|
|
|
|
|
@default_cfg(**SuperbProblem.inference.default_cfg) |
|
|
@classmethod |
|
|
def inference(cls, **cfg): |
|
|
super().inference(**cfg) |
|
|
|
|
|
@default_cfg( |
|
|
**SuperbProblem.run.default_except( |
|
|
stages=["setup", "train", "inference"], |
|
|
start_stage="setup", |
|
|
final_stage="inference", |
|
|
setup=setup.default_cfg.deselect("workspace", "resume"), |
|
|
train=train.default_cfg.deselect("workspace", "resume"), |
|
|
inference=inference.default_cfg.deselect("workspace", "resume"), |
|
|
) |
|
|
) |
|
|
@classmethod |
|
|
def run(cls, **cfg): |
|
|
super().run(**cfg) |
|
|
|
