"""
The setting of Superb KS
Authors
* Yist Y. Lin 2021
* Leo 2022
"""
import logging
import pickle
from pathlib import Path
from typing import OrderedDict
import pandas as pd
from omegaconf import MISSING
from torch.utils.data import Dataset
from s3prl.dataio.corpus.speech_commands import SpeechCommandsV1
from s3prl.dataio.encoder.category import CategoryEncoder
from s3prl.dataio.sampler import BalancedWeightedSampler, FixedBatchSizeBatchSampler
from s3prl.nn.linear import MeanPoolingLinear
from .superb_sid import SuperbSID
logger = logging.getLogger(__name__)
__all__ = [
"gsc1_for_classification",
"SuperbKS",
]
[docs]def gsc1_for_classification(
target_dir: str,
cache_dir: str,
gsc1: str,
gsc1_test: str,
get_path_only: bool = False,
):
"""
Prepare Google Speech Command for classfication task
following :obj:`SuperbKS.prepare_data` format.
Args:
gsc1 (str): The root path of the Google Speech Command V1 training set
gsc1_test (str): The root path of the Google Speech Command V1 test set
**others: refer to :obj:`SuperbKS.prepare_data`
"""
target_dir = Path(target_dir)
train_path = target_dir / f"train.csv"
valid_path = target_dir / f"valid.csv"
test_paths = [target_dir / f"test.csv"]
if get_path_only:
return train_path, valid_path, test_paths
def gsc_v1_for_superb(gsc1: str, gsc1_test: str):
corpus = SpeechCommandsV1(gsc1, gsc1_test)
def format_fields(data: dict):
import torchaudio
formated_data = OrderedDict()
for key, value in data.items():
data_point = {
"wav_path": value["wav_path"],
"label": value["class_name"],
"start_sec": None,
"end_sec": None,
}
if value["class_name"] == "_silence_":
# NOTE: for silence, crop into 1-second segments, which
# is the standard way reported in the original paper
info = torchaudio.info(value["wav_path"])
for start in list(range(info.num_frames))[:: info.sample_rate]:
seg = data_point.copy()
end = min(start + 1 * info.sample_rate, info.num_frames)
seg["start_sec"] = start / info.sample_rate
seg["end_sec"] = end / info.sample_rate
formated_data[f"{key}_{start}_{end}"] = seg
else:
formated_data[key] = data_point
return formated_data
train_data, valid_data, test_data = corpus.data_split
return (
format_fields(train_data),
format_fields(valid_data),
format_fields(test_data),
)
train_data, valid_data, test_data = gsc_v1_for_superb(gsc1, gsc1_test)
def dict_to_csv(data_dict, csv_path):
keys = sorted(list(data_dict.keys()))
fields = sorted(data_dict[keys[0]].keys())
data = dict()
for field in fields:
data[field] = []
for key in keys:
data[field].append(data_dict[key][field])
data["id"] = keys
df = pd.DataFrame(data)
df.to_csv(csv_path, index=False)
dict_to_csv(train_data, train_path)
dict_to_csv(valid_data, valid_path)
dict_to_csv(test_data, test_paths[0])
return train_path, valid_path, test_paths
[docs]class SuperbKS(SuperbSID):
[docs] def default_config(self) -> dict:
return dict(
start=0,
stop=None,
target_dir=MISSING,
cache_dir=None,
remove_all_cache=False,
prepare_data=dict(
gsc1=MISSING,
gsc1_test=MISSING,
),
build_encoder=dict(),
build_dataset=dict(
train=dict(
sox_effects=[
["channels", "1"],
["rate", "16000"],
["gain", "-3.0"],
],
),
valid=dict(
sox_effects=[
["channels", "1"],
["rate", "16000"],
["gain", "-3.0"],
],
),
test=dict(
sox_effects=[
["channels", "1"],
["rate", "16000"],
["gain", "-3.0"],
],
),
),
build_batch_sampler=dict(
train=dict(
batch_size=32,
),
valid=dict(
batch_size=32,
),
test=dict(
batch_size=32,
),
),
build_upstream=dict(
name=MISSING,
),
build_featurizer=dict(
layer_selections=None,
normalize=False,
),
build_downstream=dict(
hidden_size=256,
),
build_model=dict(
upstream_trainable=False,
),
build_task=dict(),
build_optimizer=dict(
name="Adam",
conf=dict(
lr=1.0e-4,
),
),
build_scheduler=dict(
name="ExponentialLR",
gamma=0.9,
),
save_model=dict(),
save_task=dict(),
train=dict(
total_steps=200000,
log_step=100,
eval_step=5000,
save_step=1000,
gradient_clipping=1.0,
gradient_accumulate=1,
valid_metric="accuracy",
valid_higher_better=True,
auto_resume=True,
resume_ckpt_dir=None,
),
evaluate=dict(),
)
[docs] def prepare_data(
self,
prepare_data: dict,
target_dir: str,
cache_dir: str,
get_path_only: bool = False,
):
"""
Prepare the task-specific data metadata (path, labels...).
By default call :obj:`gsc1_for_classification` with :code:`**prepare_data`
Args:
prepare_data (dict): same in :obj:`default_config`,
support arguments in :obj:`gsc1_for_classification`
target_dir (str): Parse your corpus and save the csv file into this directory
cache_dir (str): If the parsing or preprocessing takes too long time, you can save
the temporary files into this directory. This directory is expected to be shared
across different training sessions (different hypers and :code:`target_dir`)
get_path_only (str): Directly return the filepaths no matter they exist or not.
Returns:
tuple
1. train_path (str)
2. valid_path (str)
3. test_paths (List[str])
Each path (str) should be a csv file containing the following columns:
==================== ====================
column description
==================== ====================
id (str) - the unique id for this data point
wav_path (str) - the absolute path of the waveform file
label (str) - a string label of the waveform
start_sec (float) - optional, load the waveform from :code:`start_sec` seconds. If not presented or is :code:`math.nan`, load from the beginning.
end_sec (float) - optional, load the waveform from :code:`end_sec` seconds. If not presented or is :code:`math.nan`, load to the end.
==================== ====================
"""
return gsc1_for_classification(
**self._get_current_arguments(flatten_dict="prepare_data")
)
[docs] def build_encoder(
self,
build_encoder: dict,
target_dir: str,
cache_dir: str,
train_csv_path: str,
valid_csv_path: str,
test_csv_paths: list,
get_path_only: bool = False,
):
"""
Build the encoder (for the labels) given the data metadata, and return the saved encoder path.
By default generate and save a :obj:`s3prl.dataio.encoder.CategoryEncoder` from the :code:`label` column of all the csv files.
Args:
build_encoder (dict): same in :obj:`default_config`, no argument supported for now
target_dir (str): Save your encoder into this directory
cache_dir (str): If the preprocessing takes too long time, you can save
the temporary files into this directory. This directory is expected to be shared
across different training sessions (different hypers and :code:`target_dir`)
train_csv_path (str): the train path from :obj:`prepare_data`
valid_csv_path (str): the valid path from :obj:`prepare_data`
test_csv_paths (List[str]): the test paths from :obj:`prepare_data`
get_path_only (str): Directly return the filepaths no matter they exist or not.
Returns:
str
tokenizer_path: The tokenizer should be saved in the pickle format
"""
encoder_path = Path(target_dir) / "encoder.pkl"
if get_path_only:
return encoder_path
train_csv = pd.read_csv(train_csv_path)
valid_csv = pd.read_csv(valid_csv_path)
test_csvs = [pd.read_csv(path) for path in test_csv_paths]
all_csv = pd.concat([train_csv, valid_csv, *test_csvs])
labels = all_csv["label"].tolist()
encoder = CategoryEncoder(labels)
with open(encoder_path, "wb") as f:
pickle.dump(encoder, f)
return encoder
[docs] def build_batch_sampler(
self,
build_batch_sampler: dict,
target_dir: str,
cache_dir: str,
mode: str,
data_csv: str,
dataset: Dataset,
):
"""
Return the batch sampler for torch DataLoader.
By default for train and valid, use :obj:`BalancedWeightedSampler`; for test use
:obj:`FixedBatchSizeBatchSampler`
Args:
build_batch_sampler (dict): same in :obj:`default_config`
==================== ====================
key description
==================== ====================
train (dict) - arguments for :obj:`BalancedWeightedSampler`
valid (dict) - arguments for :obj:`BalancedWeightedSampler`
test (dict) - arguments for :obj:`FixedBatchSizeBatchSampler`
==================== ====================
target_dir (str): Current experiment directory
cache_dir (str): If the preprocessing takes too long time, save
the temporary files into this directory. This directory is expected to be shared
across different training sessions (different hypers and :code:`target_dir`)
mode (str): train/valid/test
data_csv (str): the :code:`mode` specific csv from :obj:`prepare_data`
dataset: the dataset from :obj:`build_dataset`
Returns:
batch sampler for torch DataLoader
"""
def _build_batch_sampler(
train: dict = None, valid: dict = None, test: dict = None
):
train = train or {}
valid = valid or {}
test = test or {}
csv = pd.read_csv(data_csv)
labels = csv["label"].tolist()
if mode == "train":
return BalancedWeightedSampler(labels, **train)
elif mode == "valid":
return BalancedWeightedSampler(labels, **valid)
elif mode == "test":
return FixedBatchSizeBatchSampler(csv, **test)
return _build_batch_sampler(**build_batch_sampler)
[docs] def build_downstream(
self,
build_downstream: dict,
downstream_input_size: int,
downstream_output_size: int,
downstream_downsample_rate: int,
):
"""
Return the task-specific downstream model.
By default build the :obj:`MeanPoolingLinear` model
Args:
build_downstream (dict): same in :obj:`default_config`,
support arguments of :obj:`MeanPoolingLinear`
downstream_input_size (int): the required input size of the model
downstream_output_size (int): the required output size of the model
downstream_input_stride (int): the input feature's stride (from 16 KHz)
Returns:
:obj:`AbsUtteranceModel`
"""
model = MeanPoolingLinear(
downstream_input_size, downstream_output_size, **build_downstream
)
return model