Source code for torchaudio.datasets.tedlium

import os
from typing import Tuple, Union
from pathlib import Path

import torchaudio
from torch import Tensor
from import Dataset
from torchaudio.datasets.utils import (

    "release1": {
        "folder_in_archive": "TEDLIUM_release1",
        "url": "",
        "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27",
        "data_path": "",
        "subset": "train",
        "supported_subsets": ["train", "test", "dev"],
        "dict": "TEDLIUM.150K.dic",
    "release2": {
        "folder_in_archive": "TEDLIUM_release2",
        "url": "",
        "checksum": "93281b5fcaaae5c88671c9d000b443cb3c7ea3499ad12010b3934ca41a7b9c58",
        "data_path": "",
        "subset": "train",
        "supported_subsets": ["train", "test", "dev"],
        "dict": "TEDLIUM.152k.dic",
    "release3": {
        "folder_in_archive": "TEDLIUM_release-3",
        "url": "",
        "checksum": "ad1e454d14d1ad550bc2564c462d87c7a7ec83d4dc2b9210f22ab4973b9eccdb",
        "data_path": "data/",
        "subset": None,
        "supported_subsets": [None],
        "dict": "TEDLIUM.152k.dic",

[docs]class TEDLIUM(Dataset): """ Create a Dataset for Tedlium. It supports releases 1,2 and 3. Args: root (str or Path): Path to the directory where the dataset is found or downloaded. release (str, optional): Release version. Allowed values are ``"release1"``, ``"release2"`` or ``"release3"``. (default: ``"release1"``). subset (str, optional): The subset of dataset to use. Valid options are ``"train"``, ``"dev"``, and ``"test"`` for releases 1&2, ``None`` for release3. Defaults to ``"train"`` or ``None``. download (bool, optional): Whether to download the dataset if it is not found at root path. (default: ``False``). """ def __init__( self, root: Union[str, Path], release: str = "release1", subset: str = None, download: bool = False, audio_ext=".sph" ) -> None: self._ext_audio = audio_ext if release in _RELEASE_CONFIGS.keys(): folder_in_archive = _RELEASE_CONFIGS[release]["folder_in_archive"] url = _RELEASE_CONFIGS[release]["url"] subset = subset if subset else _RELEASE_CONFIGS[release]["subset"] else: # Raise warning raise RuntimeError( "The release {} does not match any of the supported tedlium releases{} ".format( release, _RELEASE_CONFIGS.keys(), ) ) if subset not in _RELEASE_CONFIGS[release]["supported_subsets"]: # Raise warning raise RuntimeError( "The subset {} does not match any of the supported tedlium subsets{} ".format( subset, _RELEASE_CONFIGS[release]["supported_subsets"], ) ) # Get string representation of 'root' in case Path object is passed root = os.fspath(root) basename = os.path.basename(url) archive = os.path.join(root, basename) basename = basename.split(".")[0] self._path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["data_path"]) if subset in ["train", "dev", "test"]: self._path = os.path.join(self._path, subset) if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): checksum = _RELEASE_CONFIGS[release]["checksum"] download_url(url, root, hash_value=checksum) extract_archive(archive) # Create list for all samples self._filelist = [] stm_path = os.path.join(self._path, "stm") for file in sorted(os.listdir(stm_path)): if file.endswith(".stm"): stm_path = os.path.join(self._path, "stm", file) with open(stm_path) as f: l = len(f.readlines()) file = file.replace(".stm", "") self._filelist.extend((file, line) for line in range(l)) # Create dict path for later read self._dict_path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["dict"]) self._phoneme_dict = None def _load_tedlium_item(self, fileid: str, line: int, path: str) -> Tuple[Tensor, int, str, int, int, int]: """Loads a TEDLIUM dataset sample given a file name and corresponding sentence name. Args: fileid (str): File id to identify both text and audio files corresponding to the sample line (int): Line identifier for the sample inside the text file path (str): Dataset root path Returns: tuple: ``(waveform, sample_rate, transcript, talk_id, speaker_id, identifier)`` """ transcript_path = os.path.join(path, "stm", fileid) with open(transcript_path + ".stm") as f: transcript = f.readlines()[line] talk_id, _, speaker_id, start_time, end_time, identifier, transcript = transcript.split(" ", 6) wave_path = os.path.join(path, "sph", fileid) waveform, sample_rate = self._load_audio(wave_path + self._ext_audio, start_time=start_time, end_time=end_time) return (waveform, sample_rate, transcript, talk_id, speaker_id, identifier) def _load_audio(self, path: str, start_time: float, end_time: float, sample_rate: int = 16000) -> [Tensor, int]: """Default load function used in TEDLIUM dataset, you can overwrite this function to customize functionality and load individual sentences from a full ted audio talk file. Args: path (str): Path to audio file start_time (int, optional): Time in seconds where the sample sentence stars end_time (int, optional): Time in seconds where the sample sentence finishes Returns: [Tensor, int]: Audio tensor representation and sample rate """ start_time = int(float(start_time) * sample_rate) end_time = int(float(end_time) * sample_rate) kwargs = {"frame_offset": start_time, "num_frames": end_time - start_time} return torchaudio.load(path, **kwargs)
[docs] def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: """Load the n-th sample from the dataset. Args: n (int): The index of the sample to be loaded Returns: tuple: ``(waveform, sample_rate, transcript, talk_id, speaker_id, identifier)`` """ fileid, line = self._filelist[n] return self._load_tedlium_item(fileid, line, self._path)
def __len__(self) -> int: """TEDLIUM dataset custom function overwritting len default behaviour. Returns: int: TEDLIUM dataset length """ return len(self._filelist) @property def phoneme_dict(self): """dict[str, tuple[str]]: Phonemes. Mapping from word to tuple of phonemes. Note that some words have empty phonemes. """ # Read phoneme dictionary if not self._phoneme_dict: self._phoneme_dict = {} with open(self._dict_path, "r", encoding="utf-8") as f: for line in f.readlines(): content = line.strip().split() self._phoneme_dict[content[0]] = tuple(content[1:]) # content[1:] can be empty list return self._phoneme_dict.copy()


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