The dataset viewer is not available for this subset.
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 71, 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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TTS Worker Bundle — pipeline tooling
Deployment assets for the TTS workers that generated the TinyAya parallel-speech corpora. This is tooling, not a dataset — there is nothing here to train on.
| file | ~size | what it is |
|---|---|---|
tts_bundle.tar.gz |
~2.8 GB | packaged TTS worker (model + runtime assets) |
ref-audios.tar.gz |
small | reference audio for voice conditioning |
Published so the synthetic-speech generation step is reproducible alongside its
outputs. If you want data, use
tr-hi-parallel-speech-v2
(raw speech) or
tr-hi-mimi-encoded
(training-ready).
Bundled third-party TTS models keep their own licences — unpack and check before redistributing.
Where this sits
The v0.3 speech-to-speech pipeline, end to end:
tr-hi-parallel-text text triples (en pivot -> tr / hi)
| TTS
tr-hi-parallel-speech-v2 synthetic speech + QC signals
| Mimi encode
tr-hi-mimi-encoded 8-codebook tokens + word alignments
| Stage-2 training
tr-hi-s2st-v0.3 the released model
| Model | tr-hi-s2st-v0.3 |
| Text | tr-hi-parallel-text |
| Speech | tr-hi-parallel-speech-v2 · -v3 |
| Encoded | tr-hi-mimi-encoded |
| Eval sets | fleurs-tr-hi-mimi-encoded · lahaja-eval · cv-tr-eval |
Code
| repo | what it does |
|---|---|
sound-quality-check |
4-stage speech-dataset quality control |
Project
TinyAya Stage 2 — Turkish⇄Hindi speech-to-speech translation with a text inner-monologue: a LoRA-adapted Cohere2 backbone driving a frozen Moshi depth decoder over Mimi codes.
The v0.3 run covered 76,250 steps / 2.07 epochs on a Cloud TPU v6e-16 (best val composite 2.8199 @ step 76,000). Read honestly: the text inner-monologue learns to translate (free-run chrF++ ~25.7 / 25.1), while intelligible audio synthesis remains the frontier (ASR-chrF++ 3.7 / 9.6 against a 92.1 / 86.6 ground-truth-audio ceiling) — bounded by the frozen depth decoder, not by translation understanding.
- Results: v0.3 evaluation report
- Training run: W&B
xzcb60bl· emergence report - Blog: Adapting Moshi for Low-Resource Speech Translation
Compute for the v0.3 run was provided by Google's TPU Research Cloud (TRC).
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