|
|
from s3prl.corpus.librispeech import librispeech_for_speech2text |
|
|
from s3prl.dataset.speech2text_pipe import Speech2TextPipe |
|
|
from s3prl.nn import RNNEncoder |
|
|
from s3prl.nn.specaug import ModelWithSpecaug |
|
|
from s3prl.sampler import FixedBatchSizeBatchSampler, SortedBucketingSampler |
|
|
from s3prl.task.speech2text_ctc_task import Speech2TextCTCTask |
|
|
from s3prl.util.configuration import default_cfg |
|
|
|
|
|
from .base import SuperbProblem |
|
|
|
|
|
|
|
|
class SuperbASR(SuperbProblem): |
|
|
@default_cfg( |
|
|
**SuperbProblem.setup.default_except( |
|
|
corpus=dict( |
|
|
CLS=librispeech_for_speech2text, |
|
|
dataset_root="???", |
|
|
), |
|
|
train_datapipe=dict( |
|
|
CLS=Speech2TextPipe, |
|
|
generate_tokenizer=True, |
|
|
), |
|
|
train_sampler=dict( |
|
|
CLS=SortedBucketingSampler, |
|
|
batch_size=32, |
|
|
max_length=2000, |
|
|
shuffle=True, |
|
|
), |
|
|
valid_datapipe=dict( |
|
|
CLS=Speech2TextPipe, |
|
|
), |
|
|
valid_sampler=dict( |
|
|
CLS=FixedBatchSizeBatchSampler, |
|
|
batch_size=1, |
|
|
), |
|
|
test_datapipe=dict( |
|
|
CLS=Speech2TextPipe, |
|
|
), |
|
|
test_sampler=dict( |
|
|
CLS=FixedBatchSizeBatchSampler, |
|
|
batch_size=1, |
|
|
), |
|
|
downstream=dict( |
|
|
CLS=ModelWithSpecaug, |
|
|
model_cfg=dict( |
|
|
CLS=RNNEncoder, |
|
|
module="LSTM", |
|
|
proj_size=1024, |
|
|
hidden_size=[1024, 1024], |
|
|
dropout=[0.2, 0.2], |
|
|
layer_norm=[False, False], |
|
|
proj=[False, False], |
|
|
sample_rate=[1, 1], |
|
|
sample_style="concat", |
|
|
bidirectional=True, |
|
|
), |
|
|
specaug_cfg=dict( |
|
|
freq_mask_width_range=(0, 50), |
|
|
num_freq_mask=4, |
|
|
time_mask_width_range=(0, 40), |
|
|
num_time_mask=2, |
|
|
), |
|
|
), |
|
|
task=dict( |
|
|
CLS=Speech2TextCTCTask, |
|
|
), |
|
|
) |
|
|
) |
|
|
@classmethod |
|
|
def setup(cls, **cfg): |
|
|
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=2000, |
|
|
save_step=500, |
|
|
gradient_clipping=1.0, |
|
|
gradient_accumulate_steps=1, |
|
|
valid_metric="wer", |
|
|
valid_higher_better=False, |
|
|
), |
|
|
) |
|
|
) |
|
|
@classmethod |
|
|
def train(cls, **cfg): |
|
|
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) |
|
|
|
