Datasets:

laion
/

emolia-hq

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, 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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Emolia-HQ

Emolia-HQ is a high-quality, speaker-paired subset of the LAION Emolia dataset. Each sample includes a target utterance and a reference utterance from the same speaker, enabling speaker-conditioned tasks such as voice conversion, expressive TTS, and speaker-aware emotion recognition.

Source

Derived from laion/Emolia by:

Quality filtering: Only samples with dnsmos >= 3.0 are retained.
Speaker pairing: Each target sample is matched with a reference audio from the same speaker (different utterance), forming a "quadruplet". Samples where no same-speaker reference exists are included as pairs (target only).
Metadata enrichment: speaker_id and language_id fields are extracted from the key and injected into each sample's JSON metadata.

Data Format

The dataset is stored as WebDataset .tar files, organized by language:

emolia_hq/
  DE/   # German  (243 tars, ~130 GB)
  EN/   # English (2,380 tars, ~2,476 GB)
  FR/   # French  (298 tars, ~187 GB)
  JA/   # Japanese (96 tars, ~163 GB)
  KO/   # Korean  (246 tars, ~79 GB)
  ZH/   # Chinese (929 tars, ~1,681 GB)

Each sample within a tar file is grouped by a shared base key:

Quadruplet (target + same-speaker reference)

File	Description
`<key>.mp3`	Target audio
`<key>.json`	Target metadata
`<key>.ref.mp3`	Reference audio (same speaker, different utterance)
`<key>.ref.json`	Reference metadata

Pair (no reference found)

File	Description
`<key>.mp3`	Target audio
`<key>.json`	Target metadata

JSON Metadata Fields

Field	Description
`id`	Unique utterance ID
`text`	Transcription
`duration`	Audio duration in seconds
`dnsmos`	DNS-MOS quality score (all >= 3.0)
`speaker`	Original speaker ID
`speaker_id`	Extracted speaker ID (e.g., `DE_B00000_S00010`)
`language_id`	Extracted language code (e.g., `DE`)
`language`	Language code lowercase
`emotion_caption`	Natural language description of the emotional content
`emotion_annotation`	Dictionary of 50+ emotion/prosody scores
`characters_per_second`	Speaking rate
`wavelm_timbre_embedding`	128-dim speaker timbre embedding

Statistics

Language	Tars	Size
DE (German)	243	~130 GB
EN (English)	2,380	~2,476 GB
FR (French)	298	~187 GB
JA (Japanese)	96	~163 GB
KO (Korean)	246	~79 GB
ZH (Chinese)	929	~1,681 GB
Total	4,192	~4,716 GB

~97% of samples include a same-speaker reference audio (quadruplets). The remaining ~3% are pairs where the speaker only appeared once across the entire dataset.

Usage

import webdataset as wds

dataset = wds.WebDataset("emolia_hq/EN/EN-B000000_standard_hq.tar")

for sample in dataset:
    key = sample["__key__"]
    target_audio = sample["mp3"]          # bytes
    target_meta = sample["json"]          # bytes -> json.loads()
    ref_audio = sample.get("ref.mp3")     # bytes or None
    ref_meta = sample.get("ref.json")     # bytes or None

License

Same as the source Emolia dataset. See laion/Emolia for details.

Downloads last month: 128

Total file size:

1.54 TB

Models trained or fine-tuned on laion/emolia-hq

laion/audio-audio-majestrino-with-emotion-and-quality-experts

Audio Classification • Updated Apr 12