from tedlium

tedlium.py

@@ -0,0 +1,385 @@
+# 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
+from collections import defaultdict
+from io import BytesIO
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_DL_URL = "https://huggingface.co/datasets/LIUM/tedlium/resolve/main/"
+
+_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_urls, split_paths, citation, **kwargs):
+        super(TedliumReleaseConfig, self).__init__(version=datasets.Version("1.0.1"), **kwargs)
+        self.url = url
+        self.download_urls = download_urls
+        # 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_urls={
+            "train": [_DL_URL + os.path.join("TEDLIUM_release1", "train.tar.gz")],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release1", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release1", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "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_urls={
+            "train": [
+                _DL_URL + os.path.join("TEDLIUM_release2", "train_1.tar.gz"),
+                _DL_URL + os.path.join("TEDLIUM_release2", "train_2.tar.gz"),
+            ],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release2", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release2", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
+
+    release3 = TedliumReleaseConfig(
+        name="release3",
+        description="""\
+        This is the TED-LIUM corpus release 3, licensed under Creative Commons
+        BY-NC-ND 3.0. This is the 'legacy' version of the corpus, in which the dev and test datasets are the same as in
+        TED-LIUM 2 (and TED-LIUM 1).
+
+        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.
+        - 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
+
+        """,
+        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_urls={
+            "train": [
+                _DL_URL + os.path.join("TEDLIUM_release3", "legacy", "train_1.tar.gz"),
+                _DL_URL + os.path.join("TEDLIUM_release3", "legacy", "train_2.tar.gz"),
+            ],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release3", "legacy", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release3", "legacy", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
+
+    release3_speaker_adaptation = TedliumReleaseConfig(
+        name="release3-speaker-adaptation",
+        description="""\
+            This is the TED-LIUM corpus release 3, licensed under Creative Commons
+            BY-NC-ND 3.0. This is the 'speaker adaptation' version of the corpus, specially designed for experiments on
+            speaker adaptation.
+
+            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)
+            """,
+        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_urls={
+            "train": [
+                _DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "train_1.tar.gz"),
+                _DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "train_2.tar.gz"),
+            ],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
+
+    return [release1, release2, release3, release3_speaker_adaptation]
+
+
+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):
+        archive_path = dl_manager.download(self.config.download_urls)
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+        splits = []
+        for split, path in self.config.split_paths:
+            kwargs = {
+                "filepath": [dl_manager.iter_archive(sharded_path) for sharded_path in archive_path[split]],
+                "local_extracted_archive": local_extracted_archive.get(split),
+                "split_path": path,
+            }
+            splits.append(datasets.SplitGenerator(name=split, gen_kwargs=kwargs))
+        return splits
+
+    def _generate_examples(self, filepath, local_extracted_archive, split_path):
+        """Generate examples from a TED-LIUM stm file."""
+        if local_extracted_archive:
+            for local_archive in local_extracted_archive:
+                # The stm directory houses the speaker and transcription information in .stm format
+                split_dir = os.path.join(local_archive, split_path)
+                stm_files = [os.path.join(split_dir, f) for f in os.listdir(split_dir) if f.endswith(".stm")]
+                for file in stm_files:
+                    # the .sph speaker file almost always has the same file name as the .stm file
+                    speaker_file = Path(file).stem
+                    audio_file = os.path.join(split_dir, speaker_file + ".sph")
+                    segment, sampling_rate = sf.read(audio_file, dtype=np.int16)
+                    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)
+                            if speaker_file != fn:
+                                # handle the case where the stm file does not have the same file name as the transcript
+                                speaker_file = fn
+                                audio_file = os.path.join(split_dir, speaker_file + ".sph")
+                                segment, sampling_rate = sf.read(audio_file, dtype=np.int16)
+                            samples = _extract_audio_segment(segment, sampling_rate, float(start), float(end))
+                            key = "-".join([speaker, start, end, label])
+                            example = {
+                                "audio": {"path": audio_file, "array": samples, "sampling_rate": sampling_rate},
+                                "text": transcript,
+                                "speaker_id": speaker,
+                                "gender": _parse_gender(label),
+                                "file": audio_file,
+                                "id": key,
+                            }
+                            yield key, example
+
+        else:
+            audio_data = {}
+            transcripts = defaultdict(list)
+            for file in filepath:
+                for path, f in file:
+                    if path.endswith(".sph"):
+                        # get the speaker id
+                        fn = path.split("/")[-1].strip(".sph")
+                        # read the audio data from raw byte form and add key-value pair to dict
+                        audio_data[fn] = sf.read(BytesIO(f.read()), dtype=np.int16)
+                    elif path.endswith(".stm"):
+                        for line in f:
+                            if line:
+                                line = line.decode("utf-8").strip()
+                                fn, channel, speaker, start, end, label, transcript = line.split(" ", 6)
+                                transcript = _maybe_trim_suffix(transcript)
+                                audio_file = path.replace("stm", "sph")
+                                key = "-".join([speaker, start, end, label])
+                                # append metadata information to the dict of transcripts for the associated speaker
+                                transcripts[fn].append(
+                                    {
+                                        "text": transcript,
+                                        "speaker_id": speaker,
+                                        "gender": _parse_gender(label),
+                                        "file": audio_file,
+                                        "id": key,
+                                        "start": start,
+                                        "end": end,
+                                        "channel": channel,
+                                        "fn": fn,
+                                    }
+                                )
+
+                    if audio_data and audio_data.keys() == transcripts.keys():
+                        for fn, speaker in transcripts.items():
+                            for transcript in speaker:
+                                segment, sampling_rate = audio_data[transcript["fn"]]
+                                samples = _extract_audio_segment(
+                                    segment,
+                                    sampling_rate,
+                                    float(transcript["start"]),
+                                    float(transcript["end"]),
+                                )
+                                audio = {"path": transcript["file"], "array": samples, "sampling_rate": sampling_rate}
+                                key = transcript["id"]
+                                yield key, {
+                                    "audio": audio,
+                                    "text": transcript["text"],
+                                    "speaker_id": transcript["speaker_id"],
+                                    "gender": transcript["gender"],
+                                    "file": transcript["file"],
+                                    "id": transcript["id"],
+                                }
+                        audio_data = {}
+                        transcripts = defaultdict(list)
+
+
+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 _extract_audio_segment(segment, sampling_rate, start_sec, end_sec):
+    """Extracts segment of audio samples (as an ndarray) from the given segment."""
+    # The dataset only contains mono audio.
+    start_sample = int(start_sec * sampling_rate)
+    end_sample = min(int(end_sec * sampling_rate), segment.shape[0])
+    samples = segment[start_sample:end_sample]
+    return samples
+
+
+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