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Cannot get the split names for the config 'all' of the dataset.
Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 138, in compute return CompleteJobResult(compute_split_names_from_info_response(dataset=self.dataset, config=self.config)) File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 117, in compute_split_names_from_info_response config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config) File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status response.raise_for_status() File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/c8/9e/c89ed8e89771ec41dc5b507bf42799f4d5cdc7213e5065d5c3b8b13bda640ab7/9a28bb17d91a289abf81196d95d9bc2de644166d3e95eb81b5c6c4a51aa1c23f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20240411%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240411T064747Z&X-Amz-Expires=259200&X-Amz-Signature=1fe17373aa8fdff5d64117a79e4cef1aee34c2c849cf58cf3c2bedf8e6766040&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%2A%3DUTF-8%27%27dev.clean-00000-of-00004.parquet%3B%20filename%3D%22dev.clean-00000-of-00004.parquet%22%3B&x-id=GetObject The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 498, in get_dataset_config_info for split_generator in builder._split_generators( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 61, in _split_generators self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f)) File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 3687, in read_schema file = ParquetFile( File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 341, in __init__ self.reader.open( File "pyarrow/_parquet.pyx", line 1250, in pyarrow._parquet.ParquetReader.open File "pyarrow/types.pxi", line 88, in pyarrow.lib._datatype_to_pep3118 File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 342, in read_with_retries out = read(*args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1856, in read out = self.cache._fetch(self.loc, self.loc + length) File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/caching.py", line 189, in _fetch self.cache = self.fetcher(start, end) # new block replaces old File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 643, in _fetch_range hf_raise_for_status(r) File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 362, in hf_raise_for_status raise HfHubHTTPError(str(e), response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: 404 Client Error: Not Found for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/c8/9e/c89ed8e89771ec41dc5b507bf42799f4d5cdc7213e5065d5c3b8b13bda640ab7/9a28bb17d91a289abf81196d95d9bc2de644166d3e95eb81b5c6c4a51aa1c23f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20240411%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240411T064747Z&X-Amz-Expires=259200&X-Amz-Signature=1fe17373aa8fdff5d64117a79e4cef1aee34c2c849cf58cf3c2bedf8e6766040&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%2A%3DUTF-8%27%27dev.clean-00000-of-00004.parquet%3B%20filename%3D%22dev.clean-00000-of-00004.parquet%22%3B&x-id=GetObject The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response for split in get_dataset_split_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 571, in get_dataset_split_names info = get_dataset_config_info( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 503, 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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Dataset Card for LibriTTS-R
LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus (http://www.openslr.org/60/) which is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, published in 2019.
Overview
This is the LibriTTS-R dataset, adapted for the datasets
library.
Usage
Splits
There are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):
- dev.clean
- dev.other
- test.clean
- test.other
- train.clean.100
- train.clean.360
- train.other.500
Configurations
There are 3 configurations, each which limits the splits the load_dataset()
function will download.
The default configuration is "all".
- "dev": only the "dev.clean" split (good for testing the dataset quickly)
- "clean": contains only "clean" splits
- "other": contains only "other" splits
- "all": contains only "all" splits
Example
Loading the clean
config with only the train.clean.360
split.
load_dataset("blabble-io/libritts_r", "clean", split="train.clean.100")
Streaming is also supported.
load_dataset("blabble-io/libritts_r", streaming=True)
Columns
{
"audio": datasets.Audio(sampling_rate=24_000),
"text_normalized": datasets.Value("string"),
"text_original": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"path": datasets.Value("string"),
"chapter_id": datasets.Value("string"),
"id": datasets.Value("string"),
}
Example Row
{
'audio': {
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS_R/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'array': ...,
'sampling_rate': 24000
},
'text_normalized': 'How quickly he disappeared!"',
'text_original': 'How quickly he disappeared!"',
'speaker_id': '3081',
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS_R/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'chapter_id': '166546',
'id': '3081_166546_000028_000002'
}
Dataset Details
Dataset Sources [optional]
- Homepage: https://www.openslr.org/141/
- Paper: https://arxiv.org/abs/2305.18802
Citation
@ARTICLE{Koizumi2023-hs,
title = "{LibriTTS-R}: A restored multi-speaker text-to-speech corpus",
author = "Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding,
Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani,
Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur",
abstract = "This paper introduces a new speech dataset called
``LibriTTS-R'' designed for text-to-speech (TTS) use. It is
derived by applying speech restoration to the LibriTTS
corpus, which consists of 585 hours of speech data at 24 kHz
sampling rate from 2,456 speakers and the corresponding
texts. The constituent samples of LibriTTS-R are identical
to those of LibriTTS, with only the sound quality improved.
Experimental results show that the LibriTTS-R ground-truth
samples showed significantly improved sound quality compared
to those in LibriTTS. In addition, neural end-to-end TTS
trained with LibriTTS-R achieved speech naturalness on par
with that of the ground-truth samples. The corpus is freely
available for download from
\textbackslashurl\{http://www.openslr.org/141/\}.",
month = may,
year = 2023,
copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
archivePrefix = "arXiv",
primaryClass = "eess.AS",
eprint = "2305.18802"
}
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