The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: KeyError
Message: 'mp3'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 323, in compute
compute_first_rows_from_parquet_response(
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
rows_index = indexer.get_rows_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 631, in get_rows_index
return RowsIndex(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 512, in __init__
self.parquet_index = self._init_parquet_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 529, in _init_parquet_index
response = get_previous_step_or_raise(
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/src/worker/utils.py", line 92, in get_rows_or_raise
return get_rows(
File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 69, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 234, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 109, in _generate_examples
example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
KeyError: 'mp3'Need help to make the dataset viewer work? Open a discussion for direct support.
Dataset Card for Seamless-Align-Expressive (WIP). Inspired by https://huggingface.co/datasets/allenai/nllb
Dataset Summary
This dataset was created based on metadata for mined expressive Speech-to-Speech(S2S) released by Meta AI. The S2S contains data for 5 language pairs. The S2S dataset is ~228GB compressed.
How to use the data
There are two ways to access the data:
- Via the Hugging Face Python datasets library
Scripts coming soon
- Clone the git repo
git lfs install
git clone https://huggingface.co/datasets/jhu-clsp/seamless-align-expressive
Supported Tasks and Leaderboards
N/A
Languages
Language pairs can be found here.
Dataset Structure
Each language pair contains two gzipped files, src.tar.gz and tgt.tar.gz
Data Instances
| Language Pair | Number of samples |
|---|---|
| de-en | 1385380 |
| en-es | |
| en-fr | |
| en-it | |
| en-zh |
Data Fields
Data Field can be found here.
Data Splits
The data is not split.
Dataset Creation
Curation Rationale
Source Data
Inspect links in metadata
Who are the source language producers?
Speech was collected from the web many of which are web crawls.
Annotations
Annotation process
Parallel sentences were identified using SONAR Expressive encoders. (Duquenne et al., 2023)
Who are the annotators?
The data was not human annotated.
Personal and Sensitive Information
Data may contain personally identifiable information, sensitive content, or toxic content that was publicly shared on the Internet.
Considerations for Using the Data
Social Impact of Dataset
This dataset provides data for training machine learning systems for many languages.
Discussion of Biases
Biases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques; lower resource languages generally have lower accuracy.
Other Known Limitations
Some of the translations are in fact machine translations. While some website machine translation tools are identifiable from HTML source, these tools were not filtered out en mass because raw HTML was not available from some sources and CommonCrawl processing started from WET files.
Additional Information
Dataset Curators
The data was not curated.
Licensing Information
The dataset is released under the terms of MIT. PLEASE, USE DATA RESPONSIBLY
Citation Information
Seamless Communication et al, Seamless: Multilingual Expressive and Streaming Speech Translation. arXiv Seamless: Multilingual Expressive and Streaming Speech Translation, 2023.
Duquenne et al, SONAR EXPRESSIVE: Zero-shot Expressive Speech-to-Speech Translation. https://ai.meta.com/research/publications/sonar-expressive-zero-shot-expressive-speech-to-speech-translation/, 2023
Contributions
We thank the Seamless Communication Meta AI team for open sourcing the meta data and instructions on how to use it with special thanks to Loïc Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia Gonzalez, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-jussà, Maha Elbayad, Hongyu Gong, Francisco Guzmán, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson. We also thank the Center for Language and Speech Processing(CLSP) for hosting and releasing this data, including Bismarck Bamfo Odoom and Philipp Koehn (for engineering efforts to host the data, and releasing the huggingface dataset), and Alexandre Mourachko (for organizing the connection).
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