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