Datasets:

esc-benchmark
/

esc-datasets

@@ -1,271 +0,0 @@
----
-annotations_creators:
-- expert-generated
-- crowdsourced
-- machine-generated
-language:
-- en
-language_creators:
-- crowdsourced
-- expert-generated
-license:
-- cc-by-4.0
-- apache-2.0
-- cc0-1.0
-- cc-by-nc-3.0
-- other
-multilinguality:
-- monolingual
-pretty_name: esc-datasets
-size_categories:
-- 100K<n<1M
-- 1M<n<10M
-source_datasets:
-- original
-- extended|librispeech_asr
-- extended|common_voice
-tags:
-- asr
-- benchmark
-- speech
-- esc
-task_categories:
-- automatic-speech-recognition
-task_ids: []
-extra_gated_prompt: |-
-    Three of the ESC datasets have specific terms of usage that must be agreed to before using the data.
-    To do so, fill in the access forms on the specific datasets' pages:
-      * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
-      * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
-      * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
-extra_gated_fields:
- I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset: checkbox
- I hereby confirm that I have accepted the terms of usages on GigaSpeech page: checkbox
- I hereby confirm that I have accepted the terms of usages on SPGISpeech page: checkbox
----
-All eight of datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:
-```python
-from datasets import load_dataset
-librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="train")
-```
-- `"esc-benchmark"`: the repository namespace. This is fixed for all ESC datasets.
-- `"librispeech"`: the dataset name. This can be changed to any of any one of the eight datasets in ESC to download that dataset.
--  `split="train"`: the split. Set this to one of train/validation/test to generate a specific split. Omit the `split` argument to generate all splits for a dataset.
-The datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.
-## Dataset Information
-A data point can be accessed by indexing the dataset object loaded through `load_dataset`:
-```python
-print(librispeech[0])
-```
-A typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:
-```python
-{
-  'dataset': 'librispeech',
-  'audio': {'path': '/home/esc-bencher/.cache/huggingface/datasets/downloads/extracted/d2da1969fe9e7d06661b5dc370cf2e3c119a14c35950045bcb76243b264e4f01/374-180298-0000.flac',
-      'array': array([ 7.01904297e-04,  7.32421875e-04,  7.32421875e-04, ...,
-             -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
-      'sampling_rate': 16000},
-    'text': 'chapter sixteen i might have told you of the beginning of this liaison in a few lines but i wanted you to see every step by which we came i to agree to whatever marguerite wished',
-    'id': '374-180298-0000'
-}
- ```
-### Data Fields
-- `dataset`: name of the ESC dataset from which the sample is taken.
-- `audio`: a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
-- `text`: the transcription of the audio file.
-- `id`: unique id of the data sample.
-### Data Preparation
-#### Audio
-The audio for all ESC datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts.
-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]`.
-#### Transcriptions
-The transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_&lt;unk>_) or converting symbolic punctuation to spelled out form (_&lt;comma>_  to  _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.
-Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esc-benchmark/esc for scoring.
-### Access
-All eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:
-* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
-* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
-* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
-## LibriSpeech
-The LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the [LibriVox](https://librivox.org) project. It is licensed under CC-BY-4.0.
-Example Usage:
-```python
-librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech")
-```
-Train/validation splits:
-- `train` (combination of `train.clean.100`, `train.clean.360` and `train.other.500`)
-- `validation.clean`
-- `validation.other`
-Test splits:
-- `test.clean`
-- `test.other`
-Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
-```python
-librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", subconfig="clean.100")
-```
-- `clean.100`: 100 hours of training data from the 'clean' subset
-- `clean.360`: 360 hours of training data from the 'clean' subset
-- `other.500`: 500 hours of training data from the 'other' subset
-## Common Voice
-Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0.
-Example usage:
-```python
-common_voice = load_dataset("esc-benchmark/esc-datasets", "common_voice", use_auth_token=True)
-```
-Training/validation splits:
-- `train`
-- `validation`
-Test splits:
-- `test`
-## VoxPopuli
-VoxPopuli s a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0.
-Example usage:
-```python
-voxpopuli = load_dataset("esc-benchmark/esc-datasets", "voxpopuli")
-```
-Training/validation splits:
-- `train`
-- `validation`
-Test splits:
-- `test`
-## TED-LIUM
-TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.
-Example usage:
-```python
-tedlium = load_dataset("esc-benchmark/esc-datasets", "tedlium")
-```
-Training/validation splits:
-- `train`
-- `validation`
-Test splits:
-- `test`
-## GigaSpeech
-GigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0.
-Example usage:
-```python
-gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech", use_auth_token=True)
-```
-Training/validation splits:
-- `train` (`l` subset of training data (2,500 h))
-- `validation`
-Test splits:
-- `test`
-Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
-```python
-gigaspeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="xs", use_auth_token=True)
-```
-- `xs`: extra-small subset of training data (10 h)
-- `s`: small subset of training data (250 h)
-- `m`: medium subset of training data (1,000 h)
-- `xl`: extra-large subset of training data (10,000 h)
-## SPGISpeech
-SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.
-Loading the dataset requires authorization.
-Example usage:
-```python
-spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", use_auth_token=True)
-```
-Training/validation splits:
-- `train` (`l` subset of training data (~5,000 h))
-- `validation`
-Test splits:
-- `test`
-Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
-```python
-spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s", use_auth_token=True)
-```
-- `s`: small subset of training data (~200 h)
-- `m`: medium subset of training data (~1,000 h)
-## Earnings-22
-Earnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0.
-Example usage:
-```python
-earnings22 = load_dataset("esc-benchmark/esc-datasets", "earnings22")
-```
-Training/validation splits:
-- `train`
-- `validation`
-Test splits:
-- `test`
-## AMI
-The AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0.
-Example usage:
-```python
-ami = load_dataset("esc-benchmark/esc-datasets", "ami")
-```
-Training/validation splits:
-- `train`
-- `validation`
-Test splits:
-- `test`

cv_release_stats.py DELETED Viewed

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esc-datasets.py DELETED Viewed

@@ -1,1484 +0,0 @@
-# coding=utf-8
-#
-# 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.
-# Lint as: python3
-"""ESC benchmark datasets."""
-import csv
-from collections import defaultdict
-import os
-import json
-import urllib
-import re
-import logging
-import soundfile as sf
-import numpy as np
-from tqdm.auto import tqdm
-import requests
-from io import BytesIO
-from pathlib import Path
-from huggingface_hub import HfApi, HfFolder
-import datasets
-_DESCRIPTIONS = {
-    "ami": """
-    The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings.
-    The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
-    synchronized to a common timeline. These include close-talking and far-field microphones, individual and
-    room-view video cameras, and output from a slide projector and an electronic whiteboard.
-    """,
-    "spgispeech": """
-    The SPGISpeech corpus is derived from company earnings calls manually transcribed by S&P Global, Inc.
-    according to a professional style guide detailing conventions for capitalization, punctuation, denormalization
-    of non-standard words and tran- scription of disfluencies in spontaneous speech. The basic unit of SPGISpeech is a
-    pair consisting of a 5 to 15 second long 16 bit, 16kHz mono wav audio file and its transcription.
-    """,
-    "voxpopuli": """
-    A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
-    The raw data is collected from 2009-2020 European Parliament event recordings.
-    """,
-    "tedlium": """
-    The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz.
-    All talks and text are property of TED Conferences LLC.
-    """,
-    "gigaspeech": """
-    GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
-    labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
-    and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
-    and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
-    sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
-    for speech recognition training, and to filter out segments with low-quality transcription. For system training,
-    GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
-    """,
-    "librispeech": """
-    LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
-    prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
-    audiobooks from the LibriVox project, and has been carefully segmented and aligned.
-    """,
-    "common_voice": """
-    Common Voice is Mozilla's initiative to help teach machines how real people speak.
-    The Common Voice dataset consists of a unique MP3 and corresponding text file.
-    """,
-    "earnings22": """
-    The Earnings 22 dataset ( also referred to as earnings22 ) is a 119-hour corpus of English-language earnings calls
-    collected from global companies. The primary purpose is to serve as a benchmark for industrial and academic
-    automatic speech recognition (ASR) models on real-world accented speech.
-    """
-}
-_CITATIONS = {
-    "ami": """
-    @inproceedings{10.1007/11677482_3,
-    author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas
-    and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume
-    and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre},
-    title = {The AMI Meeting Corpus: A Pre-Announcement},
-    year = {2005},
-    isbn = {3540325492},
-    publisher = {Springer-Verlag},
-    address = {Berlin, Heidelberg},
-    url = {https://doi.org/10.1007/11677482_3},
-    doi = {10.1007/11677482_3},
-    booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction},
-    pages = {28–39},
-    numpages = {12},
-    location = {Edinburgh, UK},
-    series = {MLMI'05}
-    }
-    """,
-    "spgispeech": """
-    @article{2021arXiv210402014O,
-    author = {{O'Neill}, Patrick K. and {Lavrukhin}, Vitaly and {Majumdar}, Somshubra and {Noroozi}, Vahid and {Zhang}, Yuekai
-    and {Kuchaiev}, Oleksii and {Balam}, Jagadeesh and {Dovzhenko}, Yuliya and {Freyberg}, Keenan and {Shulman}, Michael D.
-    and {Ginsburg}, Boris and {Watanabe}, Shinji and {Kucsko}, Georg},
-    title = "{SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition}",
-    journal = {arXiv e-prints},
-    keywords = {Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing},
-    year = 2021,
-    month = apr,
-    eid = {arXiv:2104.02014},
-    pages = {arXiv:2104.02014},
-    eprint = {2104.02014},
-    primaryClass = {cs.CL},
-    adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210402014O},
-    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
-    }
-    """,
-    "voxpopuli": """
-    @inproceedings{wang-etal-2021-voxpopuli,
-    title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning,
-    Semi-Supervised Learning and Interpretation",
-    author = "Wang, Changhan  and Riviere, Morgane  and Lee, Ann  and Wu, Anne  and Talnikar, Chaitanya  and Haziza,
-    Daniel  and Williamson, Mary  and Pino, Juan  and Dupoux, Emmanuel",
-    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th
-    International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
-    month = aug,
-    year = "2021",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.acl-long.80",
-    doi = "10.18653/v1/2021.acl-long.80",
-    pages = "993--1003",
-    }
-    """,
-    "tedlium": """
-    @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}
-    }
-    """,
-    "gigaspeech": """
-    @article{DBLP:journals/corr/abs-2106-06909,
-    author    = {Guoguo Chen and Shuzhou Chai and Guanbo Wang and Jiayu Du and Wei{-}Qiang Zhang and Chao Weng and Dan Su
-    and Daniel Povey and Jan Trmal and Junbo Zhang and Mingjie Jin and Sanjeev Khudanpur and Shinji Watanabe and
-    Shuaijiang Zhao and Wei Zou and Xiangang Li and Xuchen Yao and Yongqing Wang and Yujun Wang and Zhao You and Zhiyong Yan},
-    title     = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours
-               of Transcribed Audio},
-    journal   = {CoRR},
-    volume    = {abs/2106.06909},
-    year      = {2021},
-    url       = {https://arxiv.org/abs/2106.06909},
-    eprinttype = {arXiv},
-    eprint    = {2106.06909},
-    timestamp = {Wed, 29 Dec 2021 14:29:26 +0100},
-    biburl    = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib},
-    bibsource = {dblp computer science bibliography, https://dblp.org}
-    }
-    """,
-    "librispeech": """
-    @inproceedings{panayotov2015librispeech,
-    title={Librispeech: an ASR corpus based on public domain audio books},
-    author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
-    booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
-    pages={5206--5210},
-    year={2015},
-    organization={IEEE}
-    }
-    """,
-    "common_voice": """
-    @inproceedings{commonvoice:2020,
-    author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
-    title = {Common Voice: A Massively-Multilingual Speech Corpus},
-    booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
-    pages = {4211--4215},
-    year = 2020
-    }
-    """,
-    "earnings22": """
-    @misc{https://doi.org/10.48550/arxiv.2203.15591,
-    doi = {10.48550/ARXIV.2203.15591},
-    url = {https://arxiv.org/abs/2203.15591},
-    author = {Del Rio, Miguel and Ha, Peter and McNamara, Quinten and Miller, Corey and Chandra, Shipra},
-    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-    title = {Earnings-22: A Practical Benchmark for Accents in the Wild},
-    publisher = {arXiv},
-    year = {2022},
-    copyright = {Creative Commons Attribution Share Alike 4.0 International}
-    }
-    """,
-}
-_HOMEPAGE_URLS = {
-    "ami": "https://groups.inf.ed.ac.uk/ami/corpus/",
-    "spgispeech": "https://datasets.kensho.com/datasets/spgispeech",
-    "voxpopuli": "https://github.com/facebookresearch/voxpopuli",
-    "tedlium": "https://www.openslr.org/51/",
-    "gigaspeech": "https://github.com/SpeechColab/GigaSpeech",
-    "librispeech": "http://www.openslr.org/12",
-    "common_voice": "https://commonvoice.mozilla.org/en/datasets",
-    "earnings22": "https://github.com/revdotcom/speech-datasets/tree/main/earnings22",
-}
-_LICENSES = {
-    "ami": "CC BY 4.0",
-    "spgispeech": "Custom license (academic use only)",
-    "voxpopuli": "CC0, also see https://www.europarl.europa.eu/legal-notice/en/",
-    "tedlium": "Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en)",
-    "gigaspeech": "Apache License 2.0",
-    "librispeech": "CC BY 4.0",
-    "common_voice": "Mozilla Public License 2.0 (https://github.com/common-voice/common-voice/blob/main/LICENSE)",
-    "earnings22": "CC BY-SA 4.0",
-}
-_DATASET_TO_CONFIGS = {
-    "spgispeech": ["l", "s", "m"],
-    "gigaspeech": ["l", "xs", "s", "m", "xl"],
-    "librispeech": ["default", "clean.100", "clean.360", "other.500"],
-}
-_ALL_CONFIGS = list(_DATASET_TO_CONFIGS) + ["earnings22", "ami", "tedlium", "voxpopuli", "common_voice"]
-class ESCConfig(datasets.BuilderConfig):
-    """BuilderConfig for ESC benchmark dataset. """
-    def __init__(self, name, subconfig, description, citation, homepage, license, **kwargs):
-        """
-        Args:
-          name: `string`, name of a dataset to be downloaded (for example, "gigaspeech")
-          subconfig: `string`, specific configuration of a dataset, relevant for "spgispeech", "gigaspeech", and "librispeech"
-          description: `string`: dataset decsription
-          citation: `string`: dataset citation
-          homepage: `string`: dataset homepage
-          license: `string`: dataset license
-          **kwargs: keyword arguments forwarded to super.
-        """
-        if name in _DATASET_TO_CONFIGS:
-            # first config is the default one
-            self.subconfig = _DATASET_TO_CONFIGS[name][0] if subconfig == "default" else subconfig
-        else:
-            self.subconfig = None
-        super(ESCConfig, self).__init__(
-            name=name,
-            version=datasets.Version("1.0.0", ""),
-            **kwargs
-        )
-        self.description = description
-        self.citation = citation
-        self.homepage = homepage
-        self.license = license
-def _build_config(name, subconfig):
-    return ESCConfig(
-        name=name,
-        subconfig=subconfig,
-        description=_DESCRIPTIONS[name],
-        citation=_CITATIONS[name],
-        homepage=_HOMEPAGE_URLS[name],
-        license=_LICENSES[name],
-    )
-class ESCDatasets(datasets.GeneratorBasedBuilder):
-    """ESC benchmark dataset dataset."""
-    DEFAULT_WRITER_BATCH_SIZE = 256
-    BUILDER_CONFIGS = [
-        _build_config(name, subconfig="default") for name in _ALL_CONFIGS
-    ]
-    def _info(self):
-        features = datasets.Features(
-                    {
-                        "audio": datasets.Audio(sampling_rate=16_000),
-                        "dataset": datasets.Value("string"),
-                        "text": datasets.Value("string"),
-                        "id": datasets.Value("string"),
-                    }
-                )
-        return datasets.DatasetInfo(  # TODO: add benchmark's own license and description
-                features=features,
-                description=self.config.description,
-                homepage=self.config.homepage,
-                license=self.config.license,
-                citation=self.config.citation,
-            )
-    def _split_generators(self, dl_manager):
-        if self.config.name == "ami":
-            return self._ami_split_generators(dl_manager)
-        elif self.config.name == "spgispeech":
-            return self._spgispeech_split_generators(dl_manager)
-        elif self.config.name == "voxpopuli":
-            return self._voxpopuli_split_generators(dl_manager)
-        elif self.config.name == "tedlium":
-            return self._tedlium_split_generators(dl_manager)
-        elif self.config.name == "gigaspeech":
-            return self._gigaspeech_split_generators(dl_manager)
-        elif self.config.name == "librispeech":
-            return self._librispeech_split_generators(dl_manager)
-        elif self.config.name == "common_voice":
-            return self._common_voice_split_generators(dl_manager)
-        elif self.config.name == "earnings22":
-            return self._earnings_split_generators(dl_manager)
-    def _generate_examples(self, *args, **kwargs):
-        if self.config.name == "ami":
-            yield from self._ami_generate_examples(*args, **kwargs)
-        elif self.config.name == "spgispeech":
-            yield from self._spgispeech_generate_examples(*args, **kwargs)
-        elif self.config.name == "voxpopuli":
-            yield from self._voxpopuli_generate_examples(*args, **kwargs)
-        elif self.config.name == "tedlium":
-            yield from self._tedlium_generate_examples(*args, **kwargs)
-        elif self.config.name == "gigaspeech":
-            yield from self._gigaspeech_generate_examples(*args, **kwargs)
-        elif self.config.name == "librispeech":
-            yield from self._librispeech_generate_examples(*args, **kwargs)
-        elif self.config.name == "common_voice":
-            yield from self._common_voice_generate_examples(*args, **kwargs)
-        elif self.config.name == "earnings22":
-            yield from self._earnings_generate_examples(*args, **kwargs)
-    def _ami_split_generators(self, dl_manager):
-        splits = ["train", "dev", "eval"]
-        audio_archives_urls = {}
-        for split in splits:
-            audio_archives_urls[split] = [
-                _AMI_AUDIO_ARCHIVE_URL.format(split=split, _id=m) for m in _AMI_SAMPLE_IDS[split]
-            ]
-        audio_archives = dl_manager.download(audio_archives_urls)
-        local_extracted_archives_paths = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {
-            split: [None] * len(audio_archives[split]) for split in splits
-        }
-        annotations_urls = {split: _AMI_ANNOTATIONS_ARCHIVE_URL.format(split=split) for split in splits}
-        annotations = dl_manager.download(annotations_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
-                    "local_extracted_archives_paths": local_extracted_archives_paths["train"],
-                    "annotation": annotations["train"],
-                    "split": "train"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]],
-                    "local_extracted_archives_paths": local_extracted_archives_paths["dev"],
-                    "annotation": annotations["dev"],
-                    "split": "dev"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["eval"]],
-                    "local_extracted_archives_paths": local_extracted_archives_paths["eval"],
-                    "annotation": annotations["eval"],
-                    "split": "eval"
-                },
-            ),
-        ]
-    def _ami_generate_examples(self, audio_archives, local_extracted_archives_paths, annotation, split):
-        assert len(audio_archives) == len(local_extracted_archives_paths)
-        with open(annotation, "r", encoding="utf-8") as f:
-            transcriptions = {}
-            for line in f.readlines():
-                line_items = line.strip().split()
-                _id = line_items[0]
-                text = " ".join(line_items[1:])
-                _, meeting_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
-                audio_filename = "_".join([split, _id.lower()]) + ".wav"
-                transcriptions[audio_filename] = {
-                    "id": _id,
-                    "text": text if split != "eval" else "",
-                }
-        features = ["id", "text"]
-        for archive, local_archive_path in zip(audio_archives, local_extracted_archives_paths):
-            for audio_path, audio_file in archive:
-                # audio_path is like 'EN2001a/train_ami_en2001a_h00_mee068_0414915_0415078.wav'
-                audio_meta = transcriptions[audio_path.split("/")[-1]]
-                yield audio_path, {
-                    "audio": {
-                        "path": os.path.join(local_archive_path, audio_path) if local_archive_path else audio_path,
-                        "bytes": audio_file.read(),
-                    },
-                    "dataset": "ami",
-                    **{feature: audio_meta[feature] for feature in features}
-                }
-    def _spgispeech_split_generators(self, dl_manager):
-        subconfig = self.config.subconfig
-        subsets = [subconfig] + ["dev", "test"]
-        meta_path = dl_manager.download_and_extract(
-            {subset: os.path.join(_SPGISPEECH_META_BASE_URL, _SPGISPEECH_META_FILENAMES[subset]) for subset in subsets}
-        )
-        archive_urls = defaultdict(list)
-        for subset in subsets:
-            for subset_dir in _SPGISPEECH_SUBSET_TO_DIR[subset]:
-                for archive_name in _SPGISPEECH_AUDIO_ARCHIVES_NAMES[subset_dir]:
-                    archive_urls[subset].append(os.path.join(_SPGISPEECH_AUDIO_BASE_URL, subset_dir, archive_name))
-        archive_paths = dl_manager.download(archive_urls)
-        local_extracted_archive_paths = (
-            dl_manager.extract(archive_paths)
-            if not dl_manager.is_streaming
-            else {subset: [None] * len(archive_paths[subset]) for subset in subsets}
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths[subconfig],
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths[subconfig]],
-                    "meta_path": meta_path[subconfig],
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["dev"],
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["dev"]],
-                    "meta_path": meta_path["dev"],
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["test"],
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["test"]],
-                    "meta_path": meta_path["test"],
-                    "is_test": True,
-                },
-            ),
-        ]
-    def _spgispeech_generate_examples(self, local_extracted_archive_paths, archives, meta_path, is_test):
-        # define the expected metadata dict keys,
-        # some files have metadata with erroneous entries that we have to filter out
-        dict_keys = {"id": "wav_filename", "text": "transcript"}
-        logging.info("Reading spgispeech metadata")
-        with open(meta_path, encoding="utf-8") as f:
-            csvreader = csv.DictReader(f, delimiter="|")
-            metadata = {x["wav_filename"]: dict((k, x[v]) for k, v in dict_keys.items()) for x in tqdm(csvreader, leave=False)}
-        for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives):
-            # Here we iterate over all the files within the TAR archive:
-            for audio_filename, audio_file in archive:
-                audio_filename = audio_filename.lstrip("./")
-                # if an audio file exists locally (i.e. in default, non-streaming mode) set the full path to it
-                # joining path to directory that the archive was extracted to and audio filename.
-                path = (
-                    os.path.join(local_extracted_archive_path, audio_filename)
-                    if local_extracted_archive_path
-                    else audio_filename
-                )
-                # get the .wav filename by removing the directory path from the audio filename
-                wav_filename = "/".join(audio_filename.split("/")[-2:])
-                example = dict(metadata[wav_filename])
-                if is_test: example["text"] = ""
-                example["audio"] = {"path": path, "bytes": audio_file.read()}
-                example["dataset"] = "spgispeech"
-                yield audio_filename, example
-    def _voxpopuli_split_generators(self, dl_manager):
-        n_shards_path = dl_manager.download_and_extract(_VOXPOPULI_N_SHARDS_FILE)
-        with open(n_shards_path) as f:
-            n_shards = json.load(f)["en"]  # we use only English language in this benchmark
-        splits = ["train", "dev", "test"]
-        audio_urls = {}
-        for split in splits:
-            audio_urls[split] = [
-                _VOXPOPULI_AUDIO_ARCHIVE_PATH.format(split=split, n_shard=i) for i in range(n_shards[split])
-            ]
-        meta_urls = {
-            split: _VOXPOPULI_METADATA_PATH.format(split=split) for split in splits
-        }
-        dl_manager.download_config.num_proc = len(audio_urls["train"]) // 4
-        meta_paths = dl_manager.download_and_extract(meta_urls)
-        audio_paths = dl_manager.download(audio_urls)
-        local_extracted_audio_paths = (
-            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
-            {
-                split: [None] * len(audio_paths[split]) for split in splits
-            }
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
-                    "local_extracted_archives_paths": local_extracted_audio_paths["train"],
-                    "meta_path": meta_paths["train"],
-                    "is_test": False,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]],
-                    "local_extracted_archives_paths": local_extracted_audio_paths["dev"],
-                    "meta_path": meta_paths["dev"],
-                    "is_test": False,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]],
-                    "local_extracted_archives_paths": local_extracted_audio_paths["test"],
-                    "meta_path": meta_paths["test"],
-                    "is_test": True,
-                }
-            ),
-        ]
-    def _voxpopuli_generate_examples(self, audio_archives, local_extracted_archives_paths, meta_path, is_test):
-        assert len(audio_archives) == len(local_extracted_archives_paths)
-        logging.info("Reading voxpopuli metadata.")
-        with open(meta_path) as f:
-            metadata = {x["id"]: x for x in tqdm(csv.DictReader(f, delimiter="\t"), leave=False)}
-        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
-            for audio_filename, audio_file in audio_archive:
-                audio_id = audio_filename.split(os.sep)[-1].split(".wav")[0]
-                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
-                yield audio_id, {
-                    "id": audio_id,
-                    "text": metadata[audio_id]["normalized_text"].lower() if not is_test else "",
-                    "audio": {"path": path, "bytes": audio_file.read()},
-                    "dataset": "voxpopuli",
-                }
-    def _librispeech_split_generators(self, dl_manager):
-        dev_splits, test_splits = ["dev.clean", "dev.other"], ["test.clean", "test.other"]
-        train_splits = ["train.clean.100", "train.clean.360", "train.other.500"] \
-            if self.config.subconfig == "default" else [f"train.{self.config.subconfig}"]
-        dl_urls = {config_name: _LIBRISPEECH_DL_URLS[config_name] for config_name in train_splits + dev_splits + test_splits}
-        archive_paths = dl_manager.download(dl_urls)
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archives = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
-        train_split = [
-            datasets.SplitGenerator(
-                name="train",
-                gen_kwargs={
-                    "local_extracted_archives": [local_extracted_archives.get(train_name) for train_name in train_splits],
-                    "archives": [dl_manager.iter_archive(archive_paths[train_name]) for train_name in train_splits],
-                    "is_test": False,
-                },
-            )
-        ]
-        dev_splits = [
-            datasets.SplitGenerator(
-                name="validation.clean",
-                gen_kwargs={
-                    "local_extracted_archives": [local_extracted_archives.get("dev.clean")],
-                    "archives": [dl_manager.iter_archive(archive_paths["dev.clean"])],
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="validation.other",
-                gen_kwargs={
-                    "local_extracted_archives": [local_extracted_archives.get("dev.other")],
-                    "archives": [dl_manager.iter_archive(archive_paths["dev.other"])],
-                    "is_test": False,
-                },
-            ),
-        ]
-        test_splits = [
-            datasets.SplitGenerator(
-                name="test.clean",
-                gen_kwargs={
-                    "local_extracted_archives": [local_extracted_archives.get("test.clean")],
-                    "archives": [dl_manager.iter_archive(archive_paths["test.clean"])],
-                    "is_test": True,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="test.other",
-                gen_kwargs={
-                    "local_extracted_archives": [local_extracted_archives.get("test.other")],
-                    "archives": [dl_manager.iter_archive(archive_paths["test.other"])],
-                    "is_test": True,
-                },
-            ),
-        ]
-        return train_split + dev_splits + test_splits
-    def _librispeech_generate_examples(self, archives, local_extracted_archives, is_test):
-        key = 0
-        audio_data = {}
-        transcripts = []
-        for archive, local_extracted_archive in zip(archives, local_extracted_archives):
-            for path, f in archive:
-                if path.endswith(".flac"):
-                    id_ = path.split("/")[-1][: -len(".flac")]
-                    audio_data[id_] = f.read()
-                elif path.endswith(".trans.txt"):
-                    for line in f:
-                        if line:
-                            line = line.decode("utf-8").strip()
-                            id_, transcript = line.split(" ", 1)
-                            # Error correction
-                            transcript = transcript.lower()
-                            audio_file = f"{id_}.flac"
-                            audio_file = (
-                                os.path.join(local_extracted_archive, audio_file)
-                                if local_extracted_archive
-                                else audio_file
-                            )
-                            transcripts.append(
-                                {
-                                    "id": id_,
-                                    "file": audio_file,
-                                    "text": transcript if not is_test else "",
-                                }
-                            )
-                if audio_data and len(audio_data) == len(transcripts):
-                    for transcript in transcripts:
-                        audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
-                        del transcript["file"]
-                        yield key, {"audio": audio, "dataset": "librispeech", **transcript}
-                        key += 1
-                    audio_data = {}
-                    transcripts = []
-    def _common_voice_get_bundle_url(self, locale, url_template):
-        # path = encodeURIComponent(path)
-        path = url_template.replace("{locale}", locale)
-        path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
-        # use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
-        # response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
-        response = requests.get(f"{_COMMON_VOICE_API_URL}/bucket/dataset/{path}", timeout=10.0).json()
-        return response["url"]
-    def _common_voice_log_download(self, locale, bundle_version, auth_token):
-        if isinstance(auth_token, bool):
-            auth_token = HfFolder().get_token()
-        whoami = HfApi().whoami(auth_token)
-        email = whoami["email"] if "email" in whoami else ""
-        payload = {"email": email, "locale": locale, "dataset": bundle_version}
-        requests.post(f"{_COMMON_VOICE_API_URL}/{locale}/downloaders", json=payload).json()
-    def _common_voice_split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        hf_auth_token = dl_manager.download_config.use_auth_token
-        if hf_auth_token is None:
-            raise ConnectionError(
-                "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
-            )
-        bundle_version = _COMMON_VOICE_BUNDLE_URL_TEMPLATE.split("/")[0]
-        dl_manager.download_config.ignore_url_params = True
-        self._common_voice_log_download("en", bundle_version, hf_auth_token)
-        archive_path = dl_manager.download(self._common_voice_get_bundle_url("en", _COMMON_VOICE_BUNDLE_URL_TEMPLATE))
-        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
-        path_to_data = "/".join([bundle_version, "en"])
-        path_to_clips = "/".join([path_to_data, "clips"]) if path_to_data else "clips"
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_extracted_archive": local_extracted_archive,
-                    "archive_iterator": dl_manager.iter_archive(archive_path),
-                    "metadata_filepath": "/".join([path_to_data, "train.tsv"]) if path_to_data else "train.tsv",
-                    "path_to_clips": path_to_clips,
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_extracted_archive": local_extracted_archive,
-                    "archive_iterator": dl_manager.iter_archive(archive_path),
-                    "metadata_filepath": "/".join([path_to_data, "dev.tsv"]) if path_to_data else "dev.tsv",
-                    "path_to_clips": path_to_clips,
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_extracted_archive": local_extracted_archive,
-                    "archive_iterator": dl_manager.iter_archive(archive_path),
-                    "metadata_filepath": "/".join([path_to_data, "test.tsv"]) if path_to_data else "test.tsv",
-                    "path_to_clips": path_to_clips,
-                    "is_test": True,
-                },
-            ),
-        ]
-    def _common_voice_generate_examples(
-        self,
-        local_extracted_archive,
-        archive_iterator,
-        metadata_filepath,
-        path_to_clips,
-        is_test,
-    ):
-        """Yields examples."""
-        data_fields = list(self._info().features.keys())
-        metadata = {}
-        metadata_found = False
-        for path, f in archive_iterator:
-            if path == metadata_filepath:
-                metadata_found = True
-                lines = (line.decode("utf-8") for line in f)
-                reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
-                for row in reader:
-                    # set absolute path for mp3 audio file
-                    if not row["path"].endswith(".mp3"):
-                        row["path"] += ".mp3"
-                    row["path"] = os.path.join(path_to_clips, row["path"])
-                    # accent -> accents in CV 8.0
-                    if "accents" in row:
-                        row["accent"] = row["accents"]
-                        del row["accents"]
-                    # if data is incomplete, fill with empty values
-                    for field in data_fields:
-                        if field not in row:
-                            row[field] = ""
-                    metadata[row["path"]] = row
-            elif path.startswith(path_to_clips):
-                assert metadata_found, "Found audio clips before the metadata TSV file."
-                if not metadata:
-                    break
-                if path in metadata:
-                    dict_result = dict(metadata[path])
-                    # set the audio feature and the path to the extracted file
-                    path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
-                    result = {"id": dict_result["client_id"], "dataset": "common_voice",
-                              "audio": {"path": path, "bytes": f.read()}}
-                    # Error correction
-                    text = dict_result["sentence"]
-                    if text.startswith('"') and text.endswith('"'):
-                        # we can remove trailing quotation marks as they do not affect the transcription
-                        text = text[1:-1]
-                    # replace double quotation marks with single
-                    text = text.replace('""', '"')
-                    result["text"] = text if not is_test else ""
-                    yield path, result
-    def _tedlium_split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_TEDLIUM_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 {}
-        split_paths = [
-                          (datasets.Split.TRAIN, "train"),
-                          (datasets.Split.VALIDATION, "dev"),
-                          (datasets.Split.TEST, "test"),
-                      ]
-        splits = []
-        for split, split_name in split_paths:
-            kwargs = {
-                "filepath": [dl_manager.iter_archive(sharded_path) for sharded_path in archive_path[split_name]],
-                "local_extracted_archive": local_extracted_archive.get(split_name),
-                "split_path": split_name,
-            }
-            splits.append(datasets.SplitGenerator(name=split, gen_kwargs=kwargs))
-        return splits
-    def _tedlium_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)
-                            # Error correction
-                            transcript = transcript.lower()
-                            if transcript in ignore_segments:
-                                continue
-                            # delete the <unk> token from the text
-                            transcript = transcript.replace("<unk>", "")
-                            # replace spaced apostrophes with un-spaced (it 's -> it's)
-                            for contraction in tedlium_contractions:
-                                transcript = transcript.replace(contraction, contraction[1:])
-                            # JIWER compliance (for WER/CER calc.)
-                            # remove multiple spaces
-                            transcript = re.sub(r"\s\s+", " ", transcript)
-                            # strip trailing spaces
-                            transcript = transcript.strip()
-                            if len(transcript) == 0:
-                                continue
-                            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 if split_path != "test" else "",
-                                "id": key,
-                                "dataset": "tedlium",
-                            }
-                            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)
-                                # Error correction
-                                transcript = transcript.lower()
-                                if transcript in ignore_segments:
-                                    continue
-                                # delete the <unk> token from the text
-                                transcript = transcript.replace("<unk>", "")
-                                # replace spaced apostrophes with un-spaced (it 's -> it's)
-                                for contraction in tedlium_contractions:
-                                    transcript = transcript.replace(contraction, contraction[1:])
-                                # JIWER compliance (for WER/CER calc.)
-                                # remove multiple spaces
-                                transcript = re.sub(r"\s\s+", " ", transcript)
-                                # strip trailing spaces
-                                transcript = transcript.strip()
-                                if len(transcript) == 0:
-                                    continue
-                                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,
-                                        "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"] if split_path != "test" else "",
-                                    "dataset": "tedlium",
-                                    "id": transcript["id"],
-                                }
-                        audio_data = {}
-                        transcripts = defaultdict(list)
-    def _gigaspeech_split_generators(self, dl_manager):
-        splits_to_configs = {
-            "train": _GIGASPEECH_CONFIGS_TO_ALL_CONFIGS[self.config.subconfig],
-            "dev": ["dev"],
-            "test": ["test"],
-        }
-        # 1. prepare sharded archives with audio files
-        audio_archives_urls = defaultdict(list)
-        for split, subsets in splits_to_configs.items():
-            for subset in subsets:
-                audio_archives_urls[split].extend(
-                    [
-                        _GIGASPEECH_AUDIO_ARCHIVE_URL.format(subset=subset, is_additional=_is_additional(subset),
-                                                             archive_id=i)
-                        for i in range(_GIGASPEECH_N_ARCHIVES[subset])
-                    ]
-                )
-        audio_archives_paths = dl_manager.download(audio_archives_urls)
-        local_audio_archives_paths = dl_manager.extract(audio_archives_paths) if not dl_manager.is_streaming \
-            else {}
-        # 2. prepare sharded metadata csv files
-        meta_urls = defaultdict(list)
-        for split, subsets in splits_to_configs.items():
-            for subset in subsets:
-                meta_urls[split].extend(
-                    [
-                        _GIGASPEECH_META_URL.format(subset=subset, is_additional=_is_additional(subset), archive_id=i)
-                        for i in range(_GIGASPEECH_N_ARCHIVES[subset])
-                    ]
-                )
-        meta_paths = dl_manager.download_and_extract(meta_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "audio_archives_iterators": [
-                        dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["train"]
-                    ],
-                    "local_audio_archives_paths": local_audio_archives_paths.get("train"),
-                    "meta_paths": meta_paths["train"],
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "audio_archives_iterators": [
-                        dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["dev"]
-                    ],
-                    "local_audio_archives_paths": local_audio_archives_paths.get("dev"),
-                    "meta_paths": meta_paths["dev"],
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "audio_archives_iterators": [
-                        dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths["test"]
-                    ],
-                    "local_audio_archives_paths": local_audio_archives_paths.get("test"),
-                    "meta_paths": meta_paths["test"],
-                    "is_test": True,
-                },
-            ),
-        ]
-    def _gigaspeech_generate_examples(self, audio_archives_iterators, local_audio_archives_paths, meta_paths, is_test):
-        assert len(audio_archives_iterators) == len(meta_paths)
-        if local_audio_archives_paths:
-            assert len(audio_archives_iterators) == len(local_audio_archives_paths)
-        for i, (meta_path, audio_archive_iterator) in enumerate(zip(meta_paths, audio_archives_iterators)):
-            meta_dict = dict()
-            with open(meta_path) as csvfile:
-                meta_csv = csv.DictReader(csvfile)
-                for line in meta_csv:
-                    meta_dict[line["sid"]] = line
-            for audio_path_in_archive, audio_file in audio_archive_iterator:
-                # `audio_path_in_archive` is like "dev_chunks_0000/YOU1000000029_S0000095.wav"
-                audio_filename = os.path.split(audio_path_in_archive)[1]
-                audio_id = audio_filename.split(".wav")[0]
-                audio_meta = meta_dict[audio_id]
-                audio_meta["id"] = audio_meta.pop("sid")
-                text = audio_meta.pop("text_tn")
-                # Error correction
-                text = text.lower()
-                if text in ignore_segments:
-                    continue
-                for junk_token in gigaspeech_junk_tokens:
-                    text = text.replace(junk_token, "")
-                # convert spelled out punctuation to symbolic form
-                for punctuation, replacement in gigaspeech_punctuation.items():
-                    text = text.replace(punctuation, replacement)
-                # JIWER compliance (for WER/CER calc.)
-                # remove multiple spaces
-                text = re.sub(r"\s\s+", " ", text)
-                # strip trailing spaces
-                text = text.strip()
-                if len(text) == 0:
-                    continue
-                audio_meta["text"] = text if not is_test else ""
-                path = os.path.join(local_audio_archives_paths[i], audio_path_in_archive) if local_audio_archives_paths \
-                    else audio_path_in_archive
-                yield audio_id, {
-                    "audio": {"path": path, "bytes": audio_file.read()},
-                    "dataset": "gigaspeech",
-                    **{feature: value for feature, value in audio_meta.items() if feature in self.info.features}
-                }
-    def _earnings_split_generators(self, dl_manager):
-        meta_url = _EARNINGS_BASE_URL + "metadata.csv"
-        meta_path = dl_manager.download_and_extract(meta_url)
-        with open(meta_path, encoding="utf-8") as f:
-            csvreader = csv.DictReader(f, delimiter=",")
-            metadata, all_ids = {}, set()
-            for row in csvreader:
-                all_ids.update([row["source_id"]])
-                metadata[row["file"]] = row["sentence"]  # we need only text in this benchmark
-        train_ids = all_ids - _EARNINGS_DEV_IDS - _EARNINGS_TEST_IDS
-        split_to_ids = {"train": train_ids, "dev": _EARNINGS_DEV_IDS, "test": _EARNINGS_TEST_IDS}
-        dl_urls = {}
-        for split, split_ids in split_to_ids.items():
-            dl_urls[split] = [_EARNINGS_BASE_URL + f"data/{source_id}.tar.gz" for source_id in split_ids]
-        archive_paths = dl_manager.download(dl_urls)
-        local_extracted_archive_paths = (
-            dl_manager.extract(archive_paths)
-            if not dl_manager.is_streaming
-            else {split: [None] * len(archive_paths[split]) for split in ["train", "dev", "test"]}
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["train"],
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["train"]],
-                    "metadata": metadata,
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["dev"],
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["dev"]],
-                    "metadata": metadata,
-                    "is_test": False,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_extracted_archive_paths": local_extracted_archive_paths["test"],
-                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["test"]],
-                    "metadata": metadata,
-                    "is_test": True,
-                },
-            ),
-        ]
-    def _earnings_generate_examples(self, local_extracted_archive_paths, archives, metadata, is_test):
-        for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives):
-            # Here we iterate over all the files within the TAR archive:
-            for audio_filename, audio_file in archive:
-                audio_filename = audio_filename.lstrip("./")
-                # if an audio file exists locally (i.e. in default, non-streaming mode) set the full path to it
-                # joining path to directory that the archive was extracted to and audio filename.
-                path = (
-                    os.path.join(local_extracted_archive_path, audio_filename)
-                    if local_extracted_archive_path
-                    else audio_filename
-                )
-                # Error correction
-                text = metadata[audio_filename]
-                if text.lower() in ignore_segments:
-                    continue
-                # Remove  junk tokens
-                for junk_token in earnings_junk_tokens:
-                    text = text.replace(junk_token, "")
-                # JIWER compliance (for WER/CER calc.)
-                # remove multiple spaces
-                text = re.sub(r"\s\s+", " ", text)
-                # strip trailing spaces
-                text = text.strip()
-                if len(text) == 0:
-                    continue
-                yield audio_filename, {
-                    "id": audio_filename,
-                    "text": text if not is_test else "",
-                    "dataset": "earnings22",
-                    "audio": {"path": path, "bytes": audio_file.read()}
-                }
-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
-def _is_additional(name):
-    if name in {"s", "m", "l", "xl"}:
-        return "_additional"
-    return ""
-_AMI_TRAIN_SAMPLE_IDS = [
-    "EN2001a",
-    "EN2001b",
-    "EN2001d",
-    "EN2001e",
-    "EN2003a",
-    "EN2004a",
-    "EN2005a",
-    "EN2006a",
-    "EN2006b",
-    "EN2009b",
-    "EN2009c",
-    "EN2009d",
-    "ES2002a",
-    "ES2002b",
-    "ES2002c",
-    "ES2002d",
-    "ES2003a",
-    "ES2003b",
-    "ES2003c",
-    "ES2003d",
-    "ES2005a",
-    "ES2005b",
-    "ES2005c",
-    "ES2005d",
-    "ES2006a",
-    "ES2006b",
-    "ES2006c",
-    "ES2006d",
-    "ES2007a",
-    "ES2007b",
-    "ES2007c",
-    "ES2007d",
-    "ES2008a",
-    "ES2008b",
-    "ES2008c",
-    "ES2008d",
-    "ES2009a",
-    "ES2009b",
-    "ES2009c",
-    "ES2009d",
-    "ES2010a",
-    "ES2010b",
-    "ES2010c",
-    "ES2010d",
-    "ES2012a",
-    "ES2012b",
-    "ES2012c",
-    "ES2012d",
-    "ES2013a",
-    "ES2013b",
-    "ES2013c",
-    "ES2013d",
-    "ES2014a",
-    "ES2014b",
-    "ES2014c",
-    "ES2014d",
-    "ES2015a",
-    "ES2015b",
-    "ES2015c",
-    "ES2015d",
-    "ES2016a",
-    "ES2016b",
-    "ES2016c",
-    "ES2016d",
-    "IB4005",
-    "IN1001",
-    "IN1002",
-    "IN1005",
-    "IN1007",
-    "IN1008",
-    "IN1009",
-    "IN1012",
-    "IN1013",
-    "IN1014",
-    "IN1016",
-    "IS1000a",
-    "IS1000b",
-    "IS1000c",
-    "IS1000d",
-    "IS1001a",
-    "IS1001b",
-    "IS1001c",
-    "IS1001d",
-    "IS1002b",
-    "IS1002c",
-    "IS1002d",
-    "IS1003a",
-    "IS1003b",
-    "IS1003c",
-    "IS1003d",
-    "IS1004a",
-    "IS1004b",
-    "IS1004c",
-    "IS1004d",
-    "IS1005a",
-    "IS1005b",
-    "IS1005c",
-    "IS1006a",
-    "IS1006b",
-    "IS1006c",
-    "IS1006d",
-    "IS1007a",
-    "IS1007b",
-    "IS1007c",
-    "IS1007d",
-    "TS3005a",
-    "TS3005b",
-    "TS3005c",
-    "TS3005d",
-    "TS3006a",
-    "TS3006b",
-    "TS3006c",
-    "TS3006d",
-    "TS3007a",
-    "TS3007b",
-    "TS3007c",
-    "TS3007d",
-    "TS3008a",
-    "TS3008b",
-    "TS3008c",
-    "TS3008d",
-    "TS3009a",
-    "TS3009b",
-    "TS3009c",
-    "TS3009d",
-    "TS3010a",
-    "TS3010b",
-    "TS3010c",
-    "TS3010d",
-    "TS3011a",
-    "TS3011b",
-    "TS3011c",
-    "TS3011d",
-    "TS3012a",
-    "TS3012b",
-    "TS3012c",
-    "TS3012d",
-]
-_AMI_VALIDATION_SAMPLE_IDS = [
-    "ES2011a",
-    "ES2011c",
-    "IB4001",
-    "IB4003",
-    "IB4010",
-    "IS1008a",
-    "IS1008c",
-    "TS3004a",
-    "TS3004c",
-    "ES2011b",
-    "ES2011d",
-    "IB4002",
-    "IB4004",
-    "IB4011",
-    "IS1008b",
-    "IS1008d",
-    "TS3004b",
-    "TS3004d",
-]
-_AMI_EVAL_SAMPLE_IDS = [
-    "EN2002a",
-    "EN2002b",
-    "EN2002c",
-    "EN2002d",
-    "ES2004a",
-    "ES2004b",
-    "ES2004c",
-    "ES2004d",
-    "IS1009a",
-    "IS1009b",
-    "IS1009c",
-    "IS1009d",
-    "TS3003a",
-    "TS3003b",
-    "TS3003c",
-    "TS3003d",
-]
-_AMI_SAMPLE_IDS = {
-    "train": _AMI_TRAIN_SAMPLE_IDS,
-    "dev": _AMI_VALIDATION_SAMPLE_IDS,
-    "eval": _AMI_EVAL_SAMPLE_IDS,
-}
-_AMI_BASE_DATA_URL = "https://huggingface.co/datasets/speech-seq2seq/ami/resolve/main/"
-_AMI_AUDIO_ARCHIVE_URL = _AMI_BASE_DATA_URL + "audio/ihm/{split}/{_id}.tar.gz"
-_AMI_ANNOTATIONS_ARCHIVE_URL = _AMI_BASE_DATA_URL + "annotations/{split}/text"
-_SPGISPEECH_BASE_URL = "https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/"
-_SPGISPEECH_AUDIO_BASE_URL = _SPGISPEECH_BASE_URL + "audio"
-_SPGISPEECH_SUBSET_TO_DIR = {
-    "s": ["s"],
-    "m": ["s", "m_additional"],
-    "l": ["s", "m_additional", "l_additional"],
-    "dev": ["dev"],
-    "test": ["test"],
-}
-# the second number in range is the number of archives (shards) in a subset
-_SPGISPEECH_AUDIO_ARCHIVES_NAMES = {
-    "s": [f"s_part_{i}.tar.gz" for i in range(0, 6)],
-    "m_additional": [f"m_part_{i}.tar.gz" for i in range(0, 21)],
-    "l_additional": [f"l_part_{i}.tar.gz" for i in range(0, 103)],
-    "dev": [f"dev_part_{i}.tar.gz" for i in range(0, 3)],
-    "test": [f"test_part_{i}.tar.gz" for i in range(0, 3)],
-}
-_SPGISPEECH_META_BASE_URL = _SPGISPEECH_BASE_URL + "meta"
-_SPGISPEECH_META_FILENAMES = {
-    "s": "train_small.csv",
-    "m": "train_medium.csv",
-    "l": "train.csv",
-    "dev": "dev.csv",
-    "test": "test.csv",
-}
-_VOXPOPULI_BASE_DATA_DIR = "https://huggingface.co/datasets/polinaeterna/voxpopuli/resolve/main/data/"
-_VOXPOPULI_N_SHARDS_FILE = _VOXPOPULI_BASE_DATA_DIR + "n_files.json"
-_VOXPOPULI_AUDIO_ARCHIVE_PATH = _VOXPOPULI_BASE_DATA_DIR + "en/{split}/{split}_part_{n_shard}.tar.gz"
-_VOXPOPULI_METADATA_PATH = _VOXPOPULI_BASE_DATA_DIR + "en/asr_{split}.tsv"
-_LIBRISPEECH_DL_URL = "http://www.openslr.org/resources/12/"
-_LIBRISPEECH_DL_URLS = {
-    "dev.clean": _LIBRISPEECH_DL_URL + "dev-clean.tar.gz",
-    "dev.other": _LIBRISPEECH_DL_URL + "dev-other.tar.gz",
-    "test.clean": _LIBRISPEECH_DL_URL + "test-clean.tar.gz",
-    "test.other": _LIBRISPEECH_DL_URL + "test-other.tar.gz",
-    "train.clean.100": _LIBRISPEECH_DL_URL + "train-clean-100.tar.gz",
-    "train.clean.360": _LIBRISPEECH_DL_URL + "train-clean-360.tar.gz",
-    "train.other.500": _LIBRISPEECH_DL_URL + "train-other-500.tar.gz",
-}
-_COMMON_VOICE_API_URL = "https://commonvoice.mozilla.org/api/v1"
-_COMMON_VOICE_BUNDLE_URL_TEMPLATE = 'cv-corpus-9.0-2022-04-27/cv-corpus-9.0-2022-04-27-{locale}.tar.gz'
-_TEDLIUM_BASE_URL = "https://huggingface.co/datasets/LIUM/tedlium/resolve/main/TEDLIUM_release3/legacy/"
-_TEDLIUM_URLS = {
-    "train": [_TEDLIUM_BASE_URL + "train_1.tar.gz", _TEDLIUM_BASE_URL + "train_2.tar.gz"],
-    "dev": [_TEDLIUM_BASE_URL + "dev.tar.gz"],
-    "test": [_TEDLIUM_BASE_URL + "test.tar.gz"],
-}
-_GIGASPEECH_BASE_DATA_URL = "https://huggingface.co/datasets/speechcolab/gigaspeech/resolve/main/data/"
-_GIGASPEECH_AUDIO_ARCHIVE_URL = _GIGASPEECH_BASE_DATA_URL + "audio/{subset}_files{is_additional}/{subset}_chunks_{archive_id:04}.tar.gz"
-_GIGASPEECH_META_URL = _GIGASPEECH_BASE_DATA_URL + "metadata/{subset}_metadata{is_additional}/{subset}_chunks_{archive_id:04}_metadata.csv"
-_GIGASPEECH_CONFIGS_TO_ALL_CONFIGS = {
-    "xs": ["xs"],
-    "s": ["xs", "s"],
-    "m": ["xs", "s", "m"],
-    "l": ["xs", "s", "m", "l"],
-    "xl": ["xs", "s", "m", "l", "xl"],
-}
-_GIGASPEECH_N_ARCHIVES = {
-    "xs": 1,
-    "s": 23,
-    "m": 69,
-    "l": 136,
-    "xl": 602,
-    "dev": 1,
-    "test": 3,
-}
-_EARNINGS_BASE_URL = "https://huggingface.co/datasets/anton-l/earnings22_baseline_5_gram/resolve/main/"
-_EARNINGS_DEV_IDS = {
-    "4420696",
-    "4448760",
-    "4461799",
-    "4469836",
-    "4473238",
-    "4482110",
-}
-_EARNINGS_TEST_IDS = {
-    "4432298",
-    "4450488",
-    "4470290",
-    "4479741",
-    "4483338",
-    "4485244",
-}
-tedlium_contractions = [" 's", " 't", " 're", " 've", " 'm", " 'll", " 'd", " 'clock", " 'all"]
-gigaspeech_punctuation = {" <comma>": ",", " <period>": ".", " <questionmark>": "?", " <exclamationpoint>": "!"}
-gigaspeech_junk_tokens = ["<other>", "<sil>"]
-swb_junk_tokens = ["[noise]", "[laughter]", "[silence]", "[vocalized-noise]", "<a_aside>", "<b_aside>", "<e_aside>",
-                    "[laughter-", "_1", "[laugh]", "[sigh]", "[cough]", "[mn]", "[breath]", "[lipsmack]",
-                    "[sneeze]", "[skip]", "[pause]", "(%hesitation)", "(%HESITATION)"]
-swb_punctuations = ["{", "}", "[", "]-", "]", "((", "))", "(", ")", "."]
-swb_fillers = r"\b(uh|uhm|um|hmm|mm|mhm|mmm)\b"
-earnings_junk_tokens = ["<noise>", "<crosstalk>", "<affirmative>", "<inaudible>", "inaudible", "<laugh>", "<silence>"]
-ignore_segments = ["ignore_time_segment_in_scoring", "<noise>", "<music>", "[noise]", "[laughter]", "[silence]",
-                   "[vocalized-noise]", "<crosstalk>", "<affirmative>", "<inaudible>", "<laugh>", ""]
-ignore_segments = ignore_segments + gigaspeech_junk_tokens + swb_junk_tokens + earnings_junk_tokens

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