File size: 2,816 Bytes
0b32ad6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
from s3prl.corpus.librispeech import librispeech_for_speech2text
from s3prl.dataset.speech2phoneme_pipe import Speech2PhonemePipe
from s3prl.nn.linear import FrameLevelLinear
from s3prl.sampler import FixedBatchSizeBatchSampler, SortedSliceSampler
from s3prl.task.speech2text_ctc_task import Speech2TextCTCTask
from s3prl.util.configuration import default_cfg
from .base import SuperbProblem
class SuperbPR(SuperbProblem):
@default_cfg(
**SuperbProblem.setup.default_except(
corpus=dict(
CLS=librispeech_for_speech2text,
dataset_root="???",
),
train_datapipe=dict(
CLS=Speech2PhonemePipe,
),
train_sampler=dict(
CLS=SortedSliceSampler,
batch_size=16,
max_length=300000,
),
valid_datapipe=dict(
CLS=Speech2PhonemePipe,
),
valid_sampler=dict(
CLS=FixedBatchSizeBatchSampler,
batch_size=8,
),
test_datapipe=dict(
CLS=Speech2PhonemePipe,
),
test_sampler=dict(
CLS=FixedBatchSizeBatchSampler,
batch_size=8,
),
downstream=dict(
CLS=FrameLevelLinear,
),
task=dict(
CLS=Speech2TextCTCTask,
log_metrics=["per"],
),
)
)
@classmethod
def setup(cls, **cfg):
super().setup(**cfg)
@default_cfg(
**SuperbProblem.train.default_except(
optimizer=dict(
CLS="torch.optim.Adam",
lr=1.0e-2,
),
trainer=dict(
total_steps=100000,
log_step=100,
eval_step=1000,
save_step=100,
gradient_clipping=1.0,
gradient_accumulate_steps=2,
valid_metric="per",
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", "dryrun"),
train=train.default_cfg.deselect("workspace", "resume", "dryrun"),
inference=inference.default_cfg.deselect("workspace", "resume", "dryrun"),
)
)
@classmethod
def run(cls, **cfg):
super().run(**cfg)
|