Datasets:

KU-AGI
/

CC3M_dualcodec_pretokenize

Tasks:

Automatic Speech Recognition

Text-to-Speech

Languages:

English

License:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

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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CC3M spoken captions (image–speech) — DualCodec pre-tokenized

DualCodec (12 Hz) pre-tokenized speech for omni speech–vision MLLM training. Source: Conceptual Captions 3M (filtered); speech synthesized with CosyVoice2.

Contents (train split)


Total samples	≈ 500,000 (image–caption–speech triples)
Total audio	≈ 735 hours
WebDataset shards	see repo (`.tar`)
Mean / median duration	5.29s / 4.92s
p90 / max duration	8.08s / 25.67s
Length cap	30s

Counts/hours are computed by sampling shards (per-shard sample count × #shards); duration stats from a scanned subset.

Duration distribution

bucket	share
0-5s	51.3%
5-10s	44.9%
10-15s	3.5%
15-20s	0.2%
>20s	0.0%

Format (WebDataset `.tar`)

Each sample shares a key and consists of {key}.sem.npy, {key}.ac.npy, {key}.txt, {key}.jpg:

.sem.npy — DualCodec semantic codes, int16, shape (T,), vocab 16384.
.ac.npy — DualCodec acoustic codes, int16, shape (7, T), vocab 4096 per codebook.
.txt — spoken caption text.
Image (.jpg): RGB 256×256, joined from the source image corpus by sample key.

Frame rate is 12 Hz, so duration in seconds = T / 12.

Length & padding

Codes are stored at their true variable length (no padding baked in). Samples longer than the training grid are handled at load time. During training, sequences are padded to a fixed 240-frame (20 s) grid by appending the DualCodec encoded-silence column (semantic code 3716, with its matching acoustic column) — i.e. padding is applied as code-level silence, not waveform zeros, so the padded region matches the codec's silence distribution. Samples exceeding the grid (> 240 frames) are skipped rather than cropped to preserve audio–text alignment.