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Cannot get the split names for the dataset.
Error code:   SplitsNamesError
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 376, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/tmp/modules-cache/datasets_modules/datasets/google--cvss/deddc64a3349c61055639998c3030bb336979195ded16cf757e8c37f9294b3f7/", line 106, in _split_generators
                  downloaded_files = dl_manager.download_and_extract(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/", line 946, in download_and_extract
                  return self.extract(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/", line 909, in extract
                  urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/", line 428, in map_nested
                  mapped = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/", line 429, in <listcomp>
                  _single_map_nested((function, obj, types, None, True, None))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/", line 330, in _single_map_nested
                  return function(data_struct)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/", line 914, in _extract
                  protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/", line 390, in _get_extraction_protocol
                  raise NotImplementedError(
              NotImplementedError: Extraction protocol for TAR archives like '' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
              The above exception was the direct cause of the following exception:
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/", line 79, in get_splits_response
                  split_full_names = get_dataset_split_full_names(dataset, hf_token)
                File "/src/services/worker/src/worker/responses/", line 39, in get_dataset_split_full_names
                  return [
                File "/src/services/worker/src/worker/responses/", line 42, in <listcomp>
                  for split in get_dataset_split_names(dataset, config, use_auth_token=hf_token)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 426, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 381, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus

CVSS is a massively multilingual-to-English speech-to-speech translation corpus, covering sentence-level parallel speech-to-speech translation pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation corpus. The translation speech in CVSS is synthesized with two state-of-the-art TTS models trained on the LibriTTS corpus.

CVSS includes two versions of spoken translation for all the 21 x-en language pairs from CoVoST 2, with each version providing unique values:

  • CVSS-C: All the translation speeches are in a single canonical speaker's voice. Despite being synthetic, these speeches are of very high naturalness and cleanness, as well as having a consistent speaking style. These properties ease the modeling of the target speech and enable models to produce high quality translation speech suitable for user-facing applications.

  • CVSS-T: The translation speeches are in voices transferred from the corresponding source speeches. Each translation pair has similar voices on the two sides despite being in different languages, making this dataset suitable for building models that preserve speakers' voices when translating speech into different languages.

Together with the source speeches originated from Common Voice, they make two multilingual speech-to-speech translation datasets each with about 1,900 hours of speech.

In addition to translation speech, CVSS also provides normalized translation text matching the pronunciation in the translation speech (e.g. on numbers, currencies, acronyms, etc.), which can be used for both model training as well as standardizing evaluation.

Please check out our paper for the detailed description of this corpus, as well as the baseline models we trained on both datasets.

Load the data

The following example loads the translation speech (i.e. target speech) and the normalized translation text (i.e. target text) released in CVSS corpus. You'll need to load the source speech and optionally the source text from Common Voice v4.0 separately, and join them by the file names.

from datasets import load_dataset

# Load only ar-en and ja-en language pairs. Omitting the `languages` argument
# would load all the language pairs.
cvss_c = load_dataset('google/cvss', 'cvss_c', languages=['ar', 'ja'])

# Print the structure of the dataset.


CVSS is released under the very permissive Creative Commons Attribution 4.0 International (CC BY 4.0) license.


Please cite this paper when referencing the CVSS corpus:

    title={{CVSS} Corpus and Massively Multilingual Speech-to-Speech Translation},
    author={Jia, Ye and Tadmor Ramanovich, Michelle and Wang, Quan and Zen, Heiga},
    booktitle={Proceedings of Language Resources and Evaluation Conference (LREC)},
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