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from __future__ import annotations
import logging
import torch
import torch.nn as nn
from tqdm import tqdm
from s3prl import Container, field
from s3prl.base.logdata import Logs
from s3prl.corpus.voxceleb1sv import voxceleb1_for_sv
from s3prl.dataset.base import DataLoader
from s3prl.dataset.speaker_verification_pipe import SpeakerVerificationPipe
from s3prl.nn.speaker_model import SuperbXvector
from s3prl.sampler import FixedBatchSizeBatchSampler
from s3prl.task.speaker_verification_task import SpeakerVerification
from s3prl.util.configuration import default_cfg
from s3prl.util.workspace import Workspace
from .base import SuperbProblem
logger = logging.getLogger(__name__)
EFFECTS = [
["channels", "1"],
["rate", "16000"],
["gain", "-3.0"],
["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
]
class SuperbSV(SuperbProblem):
"""
Superb Speaker Verification problem
"""
@default_cfg(
**SuperbProblem.setup.default_except(
corpus=dict(
CLS=voxceleb1_for_sv,
dataset_root="???",
),
train_datapipe=dict(
CLS=SpeakerVerificationPipe,
random_crop_secs=8.0,
sox_effects=EFFECTS,
),
train_sampler=dict(
CLS=FixedBatchSizeBatchSampler,
batch_size=10,
shuffle=True,
),
valid_datapipe=dict(
CLS=SpeakerVerificationPipe,
sox_effects=EFFECTS,
),
valid_sampler=dict(
CLS=FixedBatchSizeBatchSampler,
batch_size=1,
),
test_datapipe=dict(
CLS=SpeakerVerificationPipe,
sox_effects=EFFECTS,
),
test_sampler=dict(
CLS=FixedBatchSizeBatchSampler,
batch_size=1,
),
downstream=dict(
CLS=SuperbXvector,
),
task=dict(
CLS=SpeakerVerification,
loss_type="amsoftmax",
loss_cfg=dict(
margin=0.4,
scale=30,
),
),
)
)
@classmethod
def setup(cls, **cfg):
"""
This setups the ASV 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.AdamW",
lr=1.0e-4,
),
trainer=dict(
total_steps=200000,
log_step=500,
eval_step=field(1e10, "ASV do not use validation set"),
save_step=20000,
gradient_clipping=1.0e3,
gradient_accumulate_steps=5,
valid_metric="eer",
valid_higher_better=False,
max_keep=10,
),
)
)
@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_except(
inference_steps=field(
[
20000,
40000,
60000,
80000,
100000,
120000,
140000,
160000,
180000,
200000,
],
"The steps used for inference\n",
"egs: [900, 1000] - use the checkpoint of 90 and 100 steps for inference",
)
)
)
@classmethod
def inference(cls, **cfg):
cfg = Container(cfg)
workspace = Workspace(cfg.workspace)
dataset = workspace[f"{cfg.split_name}_dataset"]
sampler = workspace[f"{cfg.split_name}_sampler"]
dataloader = DataLoader(dataset, sampler, num_workers=cfg.n_jobs)
with torch.no_grad():
all_eers = []
for step in cfg.inference_steps:
step_dir = workspace / f"step-{step}"
task = step_dir["task"]
task = task.to(cfg.device)
task.eval()
test_results = []
for batch_idx, batch in enumerate(
tqdm(dataloader, desc="Test", total=len(dataloader))
):
batch = batch.to(cfg.device)
result = task.test_step(**batch)
test_results.append(result.cacheable())
logs: Logs = task.test_reduction(test_results).logs
logger.info(f"Step {step}")
metrics = {key: value for key, value in logs.scalars()}
step_dir.put(metrics, "test_metrics", "yaml")
for key, value in metrics.items():
logger.info(f"{key}: {value}")
all_eers.append(metrics["EER"])
workspace.put({"minEER": min(all_eers)}, "test_metrics", "yaml")
@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)
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