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PaddleSpeech/deepspeech/io/dataset.py

149 lines
5.0 KiB

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
from paddle.io import Dataset
from yacs.config import CfgNode
from deepspeech.frontend.utility import read_manifest
from deepspeech.utils.log import Log
__all__ = ["ManifestDataset", "TripletManifestDataset", "TransformDataset"]
logger = Log(__name__).getlog()
class ManifestDataset(Dataset):
@classmethod
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
default = CfgNode(
dict(
manifest="",
max_input_len=27.0,
min_input_len=0.0,
max_output_len=float('inf'),
min_output_len=0.0,
max_output_input_ratio=float('inf'),
min_output_input_ratio=0.0, ))
if config is not None:
config.merge_from_other_cfg(default)
return default
@classmethod
def from_config(cls, config):
"""Build a ManifestDataset object from a config.
Args:
config (yacs.config.CfgNode): configs object.
Returns:
ManifestDataset: dataet object.
"""
assert 'manifest' in config.data
assert config.data.manifest
dataset = cls(
manifest_path=config.data.manifest,
max_input_len=config.data.max_input_len,
min_input_len=config.data.min_input_len,
max_output_len=config.data.max_output_len,
min_output_len=config.data.min_output_len,
max_output_input_ratio=config.data.max_output_input_ratio,
min_output_input_ratio=config.data.min_output_input_ratio, )
return dataset
def __init__(self,
manifest_path,
max_input_len=float('inf'),
min_input_len=0.0,
max_output_len=float('inf'),
min_output_len=0.0,
max_output_input_ratio=float('inf'),
min_output_input_ratio=0.0):
"""Manifest Dataset
Args:
manifest_path (str): manifest josn file path
max_input_len ([type], optional): maximum output seq length,
in seconds for raw wav, in frame numbers for feature data. Defaults to float('inf').
min_input_len (float, optional): minimum input seq length,
in seconds for raw wav, in frame numbers for feature data. Defaults to 0.0.
max_output_len (float, optional): maximum input seq length,
in modeling units. Defaults to 500.0.
min_output_len (float, optional): minimum input seq length,
in modeling units. Defaults to 0.0.
max_output_input_ratio (float, optional): maximum output seq length/output seq length ratio.
Defaults to 10.0.
min_output_input_ratio (float, optional): minimum output seq length/output seq length ratio.
Defaults to 0.05.
"""
super().__init__()
# read manifest
self._manifest = read_manifest(
manifest_path=manifest_path,
max_input_len=max_input_len,
min_input_len=min_input_len,
max_output_len=max_output_len,
min_output_len=min_output_len,
max_output_input_ratio=max_output_input_ratio,
min_output_input_ratio=min_output_input_ratio)
self._manifest.sort(key=lambda x: x["feat_shape"][0])
def __len__(self):
return len(self._manifest)
def __getitem__(self, idx):
instance = self._manifest[idx]
return instance["utt"], instance["feat"], instance["text"]
class TripletManifestDataset(ManifestDataset):
"""
For Joint Training of Speech Translation and ASR.
text: translation,
text1: transcript.
"""
def __getitem__(self, idx):
instance = self._manifest[idx]
return instance["utt"], instance["feat"], instance["text"], instance[
"text1"]
class TransformDataset(Dataset):
"""Transform Dataset.
Args:
data: list object from make_batchset
transfrom: transform function
"""
def __init__(self, data, transform):
"""Init function."""
super().__init__()
self.data = data
self.transform = transform
def __len__(self):
"""Len function."""
return len(self.data)
def __getitem__(self, idx):
"""[] operator."""
return self.transform(self.data[idx])