Duplicate from LIUM/tedlium

Co-authored-by: Satya <Satya10@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+license: []
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+pretty_name: TED-LIUM
+---
+
+# Dataset Card for tedlium
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [TED-LIUM homepage](https://www.openslr.org/7/)
+- **Repository:** [Needs More Information]
+- **Paper:** [TED-LIUM: an Automatic Speech Recognition dedicated corpus](https://aclanthology.org/L12-1405/)
+- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-tedlium)
+- **Point of Contact:** [Sanchit Gandhi](mailto:sanchit@huggingface.co)
+
+### Dataset Summary
+
+The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. The three releases of the corpus range from 118 to 452 hours of transcribed speech data.
+
+
+### Example 
+
+```python
+from datasets import load_dataset
+
+tedlium = load_dataset("LIUM/tedlium", "release1") # for Release 1
+
+# see structure
+print(tedlium)
+
+# load audio sample on the fly
+audio_input = tedlium["train"][0]["audio"]  # first decoded audio sample
+transcription = tedlium["train"][0]["text"]  # first transcription
+```
+
+### Supported Tasks and Leaderboards
+
+- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/sota/speech-recognition-on-tedlium that ranks models based on their WER.
+
+### Languages
+
+The audio and transcriptions are in English, as per the TED talks at http://www.ted.com.
+
+## Dataset Structure
+
+### Data Instances
+```
+{'audio': {'path': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph', 
+  'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346,
+          0.00091553,  0.00085449], dtype=float32),
+  'sampling_rate': 16000},
+'text': '{COUGH} but <sil> i was so {COUGH} utterly unqualified for(2) this project and {NOISE} so utterly ridiculous {SMACK} and ignored the brief {SMACK} <sil>', 
+'speaker_id': 'PaulaScher_2008P', 
+'gender': 'female', 
+'file': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph', 
+'id': 'PaulaScher_2008P-1003.35-1011.16-<o,f0,female>'}
+```
+### Data Fields
+
+- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. 
+- file: A path to the downloaded audio file in .sph format.
+- text: the transcription of the audio file.
+- gender: the gender of the speaker. One of: male, female or N/A.
+- id: unique id of the data sample.
+- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
+
+### Data Splits
+There are three releases for the TED-LIUM corpus, progressively increasing the number of transcribed speech training data from 118 hours (Release 1), to 207 hours (Release 2), to 452 hours (Release 3).
+
+Release 1:
+- 774 audio talks and automatically aligned transcriptions.
+- Contains 118 hours of speech audio data.
+- Homepage: https://www.openslr.org/7/
+
+Release 2:
+- 1495 audio talks and automatically aligned transcriptions.
+- Contains 207 hours of speech audio data.
+- Dictionary with pronunciations (159848 entries).
+- Selected monolingual data for language modeling from WMT12 publicly available corpora.
+- Homepage: https://www.openslr.org/19/
+
+Release 3:
+- 2351 audio talks and automatically aligned transcriptions.
+- Contains 452 hours of speech audio data.
+- TED-LIUM 2 validation and test data: 19 TED talks with their corresponding manual transcriptions.
+- Dictionary with pronunciations (159848 entries), the 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 produce a tokenization more relevant for English language.
+- Homepage: https://www.openslr.org/51/
+
+Release 3 contains two different 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, specially designed for experiments on speaker adaptation.
+
+Each release is split into a training, validation and test set:
+
+| Split      | Release 1 | Release 2 | Release 3 |
+|------------|-----------|-----------|-----------|
+| Train      | 56,803    | 92,973    | 268,263   |
+| Validation | 591       | 591       | 591       |
+| Test       | 1,469     | 1,469     | 1,469     |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+TED-LIUM was built during [The International Workshop on Spoken Language Trans- lation (IWSLT) 2011 Evaluation Campaign](https://aclanthology.org/2011.iwslt-evaluation.1/), an annual workshop focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was obtained from publicly available TED talks at http://www.ted.com. Proper alignments between the speech and the transcribed text were generated using an in-house speaker segmentation and clustering tool (_LIUM_SpkDiarization_). Speech disfluencies (e.g. repetitions, hesitations, false starts) were treated in the following way: repetitions were transcribed, hesitations mapped to a specific filler word, and false starts not taken into account. For full details on the data collection and processing, refer to the [TED-LIUM paper](https://aclanthology.org/L12-1405/).
+
+#### Who are the source language producers?
+
+TED Talks are influential videos from expert speakers on education, business, science, tech and creativity.
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+Licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en).
+
+### Citation Information
+
+Release 1:
+```
+@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}
+}
+```
+
+Release 2:
+```
+@inproceedings{rousseau2014enhancing,
+  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 Esteve, Yannick and others},
+  booktitle={LREC},
+  pages={3935--3939},
+  year={2014}
+}
+```
+
+Release 3:
+```
+@inproceedings{hernandez2018ted,
+  author="Hernandez, Fran{\c{c}}ois
+  and Nguyen, Vincent
+  and Ghannay, Sahar
+  and Tomashenko, Natalia
+  and Est{\`e}ve, Yannick",
+  title="TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation",
+  booktitle="Speech and Computer",
+  year="2018",
+  publisher="Springer International Publishing",
+  pages="198--208",
+}
+```

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dataset_infos.json

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+{"LIUM--tedlium": {"description": "        The TED-LIUM corpus is English-language TED talks, with transcriptions,\n        sampled at 16kHz. It contains about 118 hours of speech.\n\n        This is the TED-LIUM corpus release 1,\n        licensed under Creative Commons BY-NC-ND 3.0\n        (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en).\n        ", "citation": "        @inproceedings{rousseau2012tedlium,\n          title={TED-LIUM: an Automatic Speech Recognition dedicated corpus},\n          author={Rousseau, Anthony and Del{\\'e}glise, Paul and Est{\\`e}ve, Yannick},\n          booktitle={Conference on Language Resources and Evaluation (LREC)},\n          pages={125--129},\n          year={2012}\n        }\n        ", "homepage": "https://www.openslr.org/7/", "license": "licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en)", "features": {"audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "gender": {"num_classes": 3, "names": ["unknown", "female", "male"], "id": null, "_type": "ClassLabel"}, "file": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "audio", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "ted_lium", "config_name": "release1", "version": {"version_str": "1.0.1", "description": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 13629609845.625, "num_examples": 56803, "dataset_name": "tedlium"}, "validation": {"name": "validation", "num_bytes": 197792095.0, "num_examples": 591, "dataset_name": "tedlium"}, "test": {"name": "test", "num_bytes": 352788386.375, "num_examples": 1469, "dataset_name": "tedlium"}}, "download_checksums": null, "download_size": 14151200272, "post_processing_size": null, "dataset_size": 14180190327.0, "size_in_bytes": 28331390599.0}}

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
+from collections import defaultdict
+from io import BytesIO
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+
+import datasets
+
+
+_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,
+        )
+
+    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