148 lines
4.8 KiB
Python
148 lines
4.8 KiB
Python
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import os
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import re
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from pathlib import Path
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from typing import Optional, Tuple, Union
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from torch import Tensor
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from torch.utils.data import Dataset
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from torchaudio.datasets.utils import _load_waveform
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_SAMPLE_RATE = 16000
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def _get_wavs_paths(data_dir):
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wav_dir = data_dir / "sentences" / "wav"
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wav_paths = sorted(str(p) for p in wav_dir.glob("*/*.wav"))
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relative_paths = []
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for wav_path in wav_paths:
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start = wav_path.find("Session")
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wav_path = wav_path[start:]
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relative_paths.append(wav_path)
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return relative_paths
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class IEMOCAP(Dataset):
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"""*IEMOCAP* :cite:`iemocap` dataset.
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Args:
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root (str or Path): Root directory where the dataset's top level directory is found
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sessions (Tuple[int]): Tuple of sessions (1-5) to use. (Default: ``(1, 2, 3, 4, 5)``)
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utterance_type (str or None, optional): Which type(s) of utterances to include in the dataset.
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Options: ("scripted", "improvised", ``None``). If ``None``, both scripted and improvised
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data are used.
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"""
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def __init__(
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self,
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root: Union[str, Path],
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sessions: Tuple[str] = (1, 2, 3, 4, 5),
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utterance_type: Optional[str] = None,
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):
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root = Path(root)
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self._path = root / "IEMOCAP"
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if not os.path.isdir(self._path):
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raise RuntimeError("Dataset not found.")
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if utterance_type not in ["scripted", "improvised", None]:
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raise ValueError("utterance_type must be one of ['scripted', 'improvised', or None]")
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all_data = []
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self.data = []
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self.mapping = {}
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for session in sessions:
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session_name = f"Session{session}"
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session_dir = self._path / session_name
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# get wav paths
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wav_paths = _get_wavs_paths(session_dir)
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for wav_path in wav_paths:
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wav_stem = str(Path(wav_path).stem)
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all_data.append(wav_stem)
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# add labels
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label_dir = session_dir / "dialog" / "EmoEvaluation"
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query = "*.txt"
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if utterance_type == "scripted":
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query = "*script*.txt"
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elif utterance_type == "improvised":
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query = "*impro*.txt"
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label_paths = label_dir.glob(query)
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for label_path in label_paths:
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with open(label_path, "r") as f:
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for line in f:
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if not line.startswith("["):
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continue
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line = re.split("[\t\n]", line)
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wav_stem = line[1]
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label = line[2]
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if wav_stem not in all_data:
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continue
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if label not in ["neu", "hap", "ang", "sad", "exc", "fru"]:
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continue
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self.mapping[wav_stem] = {}
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self.mapping[wav_stem]["label"] = label
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for wav_path in wav_paths:
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wav_stem = str(Path(wav_path).stem)
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if wav_stem in self.mapping:
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self.data.append(wav_stem)
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self.mapping[wav_stem]["path"] = wav_path
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def get_metadata(self, n: int) -> Tuple[str, int, str, str, str]:
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"""Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
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but otherwise returns the same fields as :py:meth:`__getitem__`.
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Args:
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n (int): The index of the sample to be loaded
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Returns:
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Tuple of the following items;
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str:
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Path to audio
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int:
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Sample rate
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str:
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File name
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str:
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Label (one of ``"neu"``, ``"hap"``, ``"ang"``, ``"sad"``, ``"exc"``, ``"fru"``)
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str:
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Speaker
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"""
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wav_stem = self.data[n]
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wav_path = self.mapping[wav_stem]["path"]
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label = self.mapping[wav_stem]["label"]
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speaker = wav_stem.split("_")[0]
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return (wav_path, _SAMPLE_RATE, wav_stem, label, speaker)
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def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]:
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"""Load the n-th sample from the dataset.
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Args:
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n (int): The index of the sample to be loaded
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Returns:
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Tuple of the following items;
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Tensor:
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Waveform
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int:
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Sample rate
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str:
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File name
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str:
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Label (one of ``"neu"``, ``"hap"``, ``"ang"``, ``"sad"``, ``"exc"``, ``"fru"``)
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str:
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Speaker
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"""
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metadata = self.get_metadata(n)
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waveform = _load_waveform(self._path, metadata[0], metadata[1])
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return (waveform,) + metadata[1:]
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def __len__(self):
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return len(self.data)
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