Add tedlium dataset (all 3 releases)

docs/source/datasets.rst

@@ -4,11 +4,11 @@ torchaudio.datasets
 All datasets are subclasses of :class:`torch.utils.data.Dataset`
 i.e, they have ``__getitem__`` and ``__len__`` methods implemented.
 Hence, they can all be passed to a :class:`torch.utils.data.DataLoader`
-which can load multiple samples parallelly using ``torch.multiprocessing`` workers. 
+which can load multiple samples parallelly using ``torch.multiprocessing`` workers.
 For example: ::
-    
+
     yesno_data = torchaudio.datasets.YESNO('.', download=True)
-    data_loader = torch.utils.data.DataLoader(yesno_data, 
+    data_loader = torch.utils.data.DataLoader(yesno_data,
                                               batch_size=1,
                                               shuffle=True,
                                               num_workers=args.nThreads)
@@ -22,7 +22,7 @@ All the datasets have almost similar API. They all have two common arguments:
 ``transform`` and  ``target_transform`` to transform the input and target respectively.
 
 
-.. currentmodule:: torchaudio.datasets 
+.. currentmodule:: torchaudio.datasets
 
 
 CMUARCTIC
@@ -81,6 +81,13 @@ SPEECHCOMMANDS
   :special-members:
 
 
+TEDLIUM
+~~~~~~~~~~~~~~
+
+.. autoclass:: TEDLIUM
+  :members: __getitem__
+  :special-members: get_phoneme_dict
+
 VCTK
 ~~~~
 

test/torchaudio_unittest/datasets/tedlium_test.py

@@ -0,0 +1,124 @@
+import os
+
+from torchaudio.datasets import tedlium
+
+from torchaudio_unittest.common_utils import (
+    TempDirMixin,
+    TorchaudioTestCase,
+    get_whitenoise,
+    save_wav,
+    normalize_wav,
+)
+
+# Used to generate a unique utterance for each dummy audio file
+UTTERANCES = [
+    "AaronHuey_2010X 1 AaronHuey_2010X 0.0 2.0 <o,f0,female> script1\n",
+    "AaronHuey_2010X 1 AaronHuey_2010X 2.0 4.0 <o,f0,female> script2\n",
+    "AaronHuey_2010X 1 AaronHuey_2010X 4.0 6.0 <o,f0,female> script3\n",
+    "AaronHuey_2010X 1 AaronHuey_2010X 6.0 8.0 <o,f0,female> script4\n",
+    "AaronHuey_2010X 1 AaronHuey_2010X 8.0 10.0 <o,f0,female> script5\n",
+]
+
+
+class TestTedlium(TempDirMixin, TorchaudioTestCase):
+    backend = "default"
+
+    root_dir = None
+    samples = {}
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root_dir = cls.get_base_temp_dir()
+        cls.root_dir = dataset_dir = os.path.join(cls.root_dir, "tedlium")
+        os.makedirs(dataset_dir, exist_ok=True)
+        sample_rate = 16000  # 16kHz
+        seed = 0
+
+        for release in ["release1", "release2", "release3"]:
+            data = get_whitenoise(sample_rate=sample_rate, duration=10.00, n_channels=1, dtype="float32", seed=seed)
+            if release in ["release1", "release2"]:
+                release_dir = os.path.join(
+                    dataset_dir,
+                    tedlium._RELEASE_CONFIGS[release]["folder_in_archive"],
+                    tedlium._RELEASE_CONFIGS[release]["subset"],
+                )
+            else:
+                release_dir = os.path.join(
+                    dataset_dir,
+                    tedlium._RELEASE_CONFIGS[release]["folder_in_archive"],
+                    tedlium._RELEASE_CONFIGS[release]["data_path"],
+                )
+            os.makedirs(release_dir, exist_ok=True)
+            os.makedirs(os.path.join(release_dir, "stm"), exist_ok=True)  # Subfolder for transcripts
+            os.makedirs(os.path.join(release_dir, "sph"), exist_ok=True)  # Subfolder for audio files
+            filename = f"{release}.sph"
+            path = os.path.join(os.path.join(release_dir, "sph"), filename)
+            save_wav(path, data, sample_rate)
+
+            trans_filename = f"{release}.stm"
+            trans_path = os.path.join(os.path.join(release_dir, "stm"), trans_filename)
+            with open(trans_path, "w") as f:
+                f.write("".join(UTTERANCES))
+
+            # Create a samples list to compare with
+            cls.samples[release] = []
+            for utterance in UTTERANCES:
+                talk_id, _, speaker_id, start_time, end_time, identifier, transcript = utterance.split(" ", 6)
+                start_time = int(float(start_time)) * sample_rate
+                end_time = int(float(end_time)) * sample_rate
+                sample = (
+                    data[:, start_time:end_time],
+                    sample_rate,
+                    transcript,
+                    talk_id,
+                    speaker_id,
+                    identifier,
+                )
+                cls.samples[release].append(sample)
+            seed += 1
+
+    def test_tedlium_release1(self):
+        release = "release1"
+        dataset = tedlium.TEDLIUM(self.root_dir, release=release)
+        num_samples = 0
+        for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset):
+            self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.samples[release][i][1]
+            assert transcript == self.samples[release][i][2]
+            assert talk_id == self.samples[release][i][3]
+            assert speaker_id == self.samples[release][i][4]
+            assert identifier == self.samples[release][i][5]
+            num_samples += 1
+
+        assert num_samples == len(self.samples[release])
+
+    def test_tedlium_release2(self):
+        release = "release2"
+        dataset = tedlium.TEDLIUM(self.root_dir, release=release)
+        num_samples = 0
+        for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset):
+            self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.samples[release][i][1]
+            assert transcript == self.samples[release][i][2]
+            assert talk_id == self.samples[release][i][3]
+            assert speaker_id == self.samples[release][i][4]
+            assert identifier == self.samples[release][i][5]
+            num_samples += 1
+
+        assert num_samples == len(self.samples[release])
+
+    def test_tedlium_release3(self):
+        release = "release3"
+        dataset = tedlium.TEDLIUM(self.root_dir, release=release)
+        num_samples = 0
+        for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset):
+            self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.samples[release][i][1]
+            assert transcript == self.samples[release][i][2]
+            assert talk_id == self.samples[release][i][3]
+            assert speaker_id == self.samples[release][i][4]
+            assert identifier == self.samples[release][i][5]
+            num_samples += 1
+
+        assert num_samples == len(self.samples[release])
+

torchaudio/datasets/__init__.py

@@ -8,6 +8,7 @@
 from .ljspeech import LJSPEECH
 from .cmuarctic import CMUARCTIC
 from .libritts import LIBRITTS
+from .tedlium import TEDLIUM
 
 __all__ = (
     "COMMONVOICE",
@@ -18,7 +19,8 @@
     "LJSPEECH",
     "GTZAN",
     "CMUARCTIC",
-    "LIBRITTS"
+    "LIBRITTS",
     "diskcache_iterator",
     "bg_iterator",
+    "TEDLIUM",
 )

torchaudio/datasets/tedlium.py

@@ -0,0 +1,208 @@
+import os
+from typing import Tuple
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import (
+    download_url,
+    extract_archive,
+)
+
+
+_RELEASE_CONFIGS = {
+    "release1": {
+        "folder_in_archive": "TEDLIUM_release1",
+        "url": "http://www.openslr.org/resources/7/TEDLIUM_release1.tar.gz",
+        "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27",
+        "data_path": "",
+        "subset": "train",
+        "supported_subsets": ["train", "test", "dev"],
+        "dict": "TEDLIUM.150K.dic",
+    },
+    "release2": {
+        "folder_in_archive": "TEDLIUM_release2",
+        "url": "http://www.openslr.org/resources/19/TEDLIUM_release2.tar.gz",
+        "checksum": "93281b5fcaaae5c88671c9d000b443cb3c7ea3499ad12010b3934ca41a7b9c58",
+        "data_path": "",
+        "subset": "train",
+        "supported_subsets": ["train", "test", "dev"],
+        "dict": "TEDLIUM.152k.dic",
+    },
+    "release3": {
+        "folder_in_archive": "TEDLIUM_release-3",
+        "url": "http://www.openslr.org/resources/51/TEDLIUM_release-3.tgz",
+        "checksum": "ad1e454d14d1ad550bc2564c462d87c7a7ec83d4dc2b9210f22ab4973b9eccdb",
+        "data_path": "data/",
+        "subset": None,
+        "supported_subsets": [None],
+        "dict": "TEDLIUM.152k.dic",
+    },
+}
+
+
+class TEDLIUM(Dataset):
+    """
+    Create a Dataset for Tedlium. It supports releases 1,2 and 3, each item is a list containings:
+    [waveform, sample_rate, transcript, talk_id, speaker_id, identifier].
+
+    Constructor arguments:
+
+    Args:
+        root (str): Path containing dataset or target path where its downloaded if needed
+        release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE.
+        subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None
+        download (bool, optional): Download dataset in case is not founded in root path. Defaults to False.
+        audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph".
+
+    Special functions:
+
+    _load_tedlium_item: Loads a TEDLIUM dataset sample given a file name and corresponding sentence name
+
+    _load_audio: 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
+
+    get_phoneme_dict: Returns the phoneme dictionary of a TEDLIUM release
+
+    """
+
+    def __init__(
+        self, root: str, release: str = "release1", subset: str = None, download: bool = False, audio_ext=".sph"
+    ) -> None:
+        """Constructor for TEDLIUM dataset.
+
+        Args:
+            root (str): Path containing dataset or target path where its downloaded if needed
+            release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE.
+            subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None
+            download (bool, optional): Download dataset in case is not founded in root path. Defaults to False.
+            audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph".
+
+        Raises:
+            RuntimeError: If release identifier does not match any supported release,
+        """
+        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"],
+                )
+            )
+
+        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:
+            Tedlium_item: A namedTuple containing [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) * 16000)
+        end_time = int(float(end_time) * 16000)
+        if torchaudio.get_audio_backend() == "sox_io":
+            return torchaudio.load(path, frame_offset=start_time, num_frames=end_time - start_time)
+        return torchaudio.load(path)[:, start_time:end_time]
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """TEDLIUM dataset custom function overwritting default loadbehaviour
+        Loads a TEDLIUM sample given a index N.
+
+        Args:
+            n (int): Index of sample to be loaded
+
+        Returns:
+            Tedlium_item: A namedTuple containing [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):
+        """Returns the phoneme dictionary of a TEDLIUM release.
+
+        Returns:
+            dictionary: Phoneme dictionary for the current tedlium release
+        """
+        # 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(maxsplit=1)
+                    self._phoneme_dict[content[0]] = tuple(content[1:])  # content[1:] can be empty list
+        return self._phoneme_dict.copy()