Upload tedlium.py

tedlium.py

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+# Copyright 2022 The HuggingFace Datasets Authors.
+#
+# 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.
+
+"""TED-LIUM speech recognition dataset."""
+
+import os
+import re
+import numpy as np
+
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+from pydub import AudioSegment
+
+_LICENSE = "licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en)"
+
+class TedliumReleaseConfig(datasets.BuilderConfig):
+  """BuilderConfig for a release of the TED-LIUM dataset."""
+
+  def __init__(self, *, url, download_url, split_paths, citation, **kwargs):
+    super(TedliumReleaseConfig, self).__init__(
+        version=datasets.Version("1.0.1"), **kwargs)
+    self.url = url
+    self.download_url = download_url
+    # List of split, path pairs containing the relative path within the
+    # extracted tarball to the data for each split.
+    self.split_paths = split_paths
+    self.citation = citation
+
+
+def _make_builder_configs():
+  """Creates builder configs for all supported Tedlium dataset releases."""
+  release1 = TedliumReleaseConfig(
+      name="release1",
+      description="""\
+        The TED-LIUM corpus is English-language TED talks, with transcriptions,
+        sampled at 16kHz. It contains about 118 hours of speech.
+
+        This is the TED-LIUM corpus release 1,
+        licensed under Creative Commons BY-NC-ND 3.0
+        (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en).
+        """,
+      citation="""\
+        @inproceedings{rousseau2012tedlium,
+          title={TED-LIUM: an Automatic Speech Recognition dedicated corpus},
+          author={Rousseau, Anthony and Del{\\'e}glise, Paul and Est{\\`e}ve, Yannick},
+          booktitle={Conference on Language Resources and Evaluation (LREC)},
+          pages={125--129},
+          year={2012}
+        }
+        """,
+      url="https://www.openslr.org/7/",
+      download_url="http://www.openslr.org/resources/7/TEDLIUM_release1.tar.gz",
+      split_paths=[(datasets.Split.TRAIN, os.path.join("TEDLIUM_release1",
+                                                   "train")),
+                   (datasets.Split.VALIDATION,
+                    os.path.join("TEDLIUM_release1", "dev")),
+                   (datasets.Split.TEST, os.path.join("TEDLIUM_release1", "test"))])
+
+  release2 = TedliumReleaseConfig(
+      name="release2",
+      description="""\
+        This is the TED-LIUM corpus release 2,
+        licensed under Creative Commons BY-NC-ND 3.0
+        (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en).
+
+        All talks and text are property of TED Conferences LLC.
+
+        The TED-LIUM corpus was made from audio talks and their transcriptions
+        available on the TED website. We have prepared and filtered these data
+        in order to train acoustic models to participate to the International
+        Workshop on Spoken Language Translation 2011 (the LIUM English/French
+        SLT system reached the first rank in the SLT task).
+
+        Contains 1495 talks and transcripts.
+        """,
+      citation="""\
+        @inproceedings{rousseau2014tedlium2,
+          title={Enhancing the {TED-LIUM} Corpus with Selected Data for Language Modeling and More {TED} Talks},
+          author={Rousseau, Anthony and Del{\\'e}glise, Paul and Est{\\`e}ve, Yannick},
+          booktitle={Conference on Language Resources and Evaluation (LREC)},
+          year={2014}
+        }
+        """,
+      url="https://www.openslr.org/19/",
+      download_url="http://www.openslr.org/resources/19/TEDLIUM_release2.tar.gz",
+      split_paths=[(datasets.Split.TRAIN, os.path.join("TEDLIUM_release2",
+                                                   "train")),
+                   (datasets.Split.VALIDATION,
+                    os.path.join("TEDLIUM_release2", "dev")),
+                   (datasets.Split.TEST, os.path.join("TEDLIUM_release2", "test"))])
+
+  release3 = TedliumReleaseConfig(
+      name="release3",
+      description="""\
+        This is the TED-LIUM corpus release 3, licensed under Creative Commons
+        BY-NC-ND 3.0.
+
+        All talks and text are property of TED Conferences LLC.
+
+        This new TED-LIUM release was made through a collaboration between the
+        Ubiqus company and the LIUM (University of Le Mans, France)
+
+        Contents:
+
+        - 2351 audio talks in NIST sphere format (SPH), including talks from
+          TED-LIUM 2: be careful, same talks but not same audio files (only
+          these audio file must be used with the TED-LIUM 3 STM files)
+        - 452 hours of audio
+        - 2351 aligned automatic transcripts in STM format
+        - TEDLIUM 2 dev and test data: 19 TED talks in SPH format with
+          corresponding manual transcriptions (cf. 'legacy' distribution below).
+        - Dictionary with pronunciations (159848 entries), same file as the one
+          included in TED-LIUM 2
+        - Selected monolingual data for language modeling from WMT12 publicly
+          available corpora: these files come from the TED-LIUM 2 release, but
+          have been modified to get a tokenization more relevant for English
+          language
+
+        Two corpus distributions:
+        - the legacy one, on which the dev and test datasets are the same as in
+          TED-LIUM 2 (and TED-LIUM 1).
+        - the 'speaker adaptation' one, especially designed for experiments on
+          speaker adaptation.
+        """,
+      citation="""\
+        @inproceedings{hernandez2018tedlium3,
+          title={TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation},
+          author={Hernandez, Fran{\\c{c}}ois and Nguyen, Vincent and Ghannay, Sahar and Tomashenko, Natalia and Est{\\`e}ve, Yannick},
+          booktitle={International Conference on Speech and Computer},
+          pages={198--208},
+          year={2018},
+          organization={Springer}
+        }
+        """,
+      url="https://www.openslr.org/51/",
+      download_url="http://www.openslr.org/resources/51/TEDLIUM_release-3.tgz",
+      split_paths=[
+          (datasets.Split.VALIDATION,
+           os.path.join("TEDLIUM_release-3", "legacy", "dev")),
+          (datasets.Split.TEST, os.path.join("TEDLIUM_release-3", "legacy",
+                                         "test")),
+          # The legacy/train directory contains symlinks to "data",
+          # which are skipped by extraction (see above).
+          # Work around this by manually dereferencing the links here.
+          (datasets.Split.TRAIN, os.path.join("TEDLIUM_release-3", "data"))
+      ])
+
+  return [release1, release2, release3]
+
+
+class TedLium(datasets.GeneratorBasedBuilder):
+    """ The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = _make_builder_configs()
+
+    def _info(self):
+        features = datasets.Features({
+                "audio":
+                    datasets.features.Audio(sampling_rate=16_000),
+                "text":
+                    datasets.Value('string'),
+                "speaker_id":
+                    datasets.Value('string'),
+                "gender":
+                    datasets.features.ClassLabel(names=["unknown", "female", "male"]),
+                "file": datasets.Value('string'),
+                "id":
+                    datasets.Value('string'),
+            })
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=features,
+            supervised_keys=("audio", "text"),
+            homepage=self.config.url,
+            license=_LICENSE,
+            citation=self.config.citation,
+            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(
+            self.config.download_url)
+        splits = []
+        for split, path in self.config.split_paths:
+            kwargs = {"filepath": os.path.join(data_dir, path)}
+            splits.append(datasets.SplitGenerator(name=split, gen_kwargs=kwargs))
+        return splits
+
+    def _generate_examples(self, filepath):
+        """Generate examples from a TED-LIUM stm file."""
+        # The stm directory houses the speaker and transcription information in .stm format
+        stm_dir = os.path.join(filepath, "stm")
+        # The sph directory houses the audio files in .sph format
+        sph_dir = os.path.join(filepath, "sph")
+        stm_files = [os.path.join(stm_dir, f) for f in os.listdir(stm_dir) if f.endswith('.stm')]
+        for file in stm_files:
+            with open(file) as f:
+                for line in f:
+                    line = line.strip()
+                    fn, channel, speaker, start, end, label, transcript = line.split(" ", 6)
+                    transcript = _maybe_trim_suffix(transcript)
+
+                    audio_file = "%s.sph" % fn
+                    samples = _extract_audio_segment(
+                        os.path.join(sph_dir, audio_file), int(channel), float(start),
+                        float(end))
+
+                    key = "-".join([speaker, start, end, label])
+                    example = {
+                        "audio": {"path": file, "bytes": samples},
+                        "text": transcript,
+                        "speaker_id": speaker,
+                        "gender": _parse_gender(label),
+                        "file": file,
+                        "id": key,
+                    }
+                    yield key, example
+
+def _maybe_trim_suffix(transcript):
+    # stm files for the TEDLIUM release 1 train split contain a key (enclosed in
+    # parens) at the end.
+    splits = transcript.rsplit(" ", 1)
+    transcript = splits[0]
+    if len(splits) > 1:
+        suffix = splits[-1]
+    if not suffix.startswith("("):
+        transcript += " " + suffix
+    return transcript
+
+
+def _parse_gender(label_str):
+    """Parse gender string from STM "<label>" field."""
+    gender = re.split(",|_", label_str)[-1][:-1]
+    # Fix inconsistencies in the data.
+    if not gender:
+        gender = -1  # Missing label.
+    elif gender == "<NA":  # In TEDLIUM release 3 training data.
+        gender = -1  # Missing label.
+    elif gender == "F":
+        gender = "female"
+    elif gender == "M":
+        gender = "male"
+    return gender
+
+
+def _extract_audio_segment(sph_path, channel, start_sec, end_sec):
+    """Extracts segment of audio samples (as an ndarray) from the given path."""
+    with open(sph_path, "rb") as f:
+        segment = AudioSegment.from_file(f, format="nistsphere")
+    # The dataset only contains mono audio.
+    assert segment.channels == 1
+    assert channel == 1
+    start_ms = int(start_sec * 1000)
+    end_ms = int(end_sec * 1000)
+    segment = segment[start_ms:end_ms]
+    samples = np.array(segment.get_array_of_samples())
+    return samples