Datasets:

SLLMCommunity
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SQuTR

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label SQuTR@2ee35db586f9013cb98114b2e0db9661c205c4f6
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1169, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1098, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label SQuTR@2ee35db586f9013cb98114b2e0db9661c205c4f6

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SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval

SQuTR (Spoken Query-to-Text Retrieval) is a large-scale bilingual benchmark designed to evaluate the robustness of information retrieval systems under realistic acoustic perturbations.

While speech interaction is becoming a primary interface for IR systems, performance often degrades significantly in noisy environments. SQuTR provides a standardized framework featuring 37,317 complex queries across 6 domains, synthesized with 200 real speakers, and evaluated under 4 graded noise levels.

🌟 Key Features

Bilingual & Multi-Domain: Includes 6 subsets from MTEB and C-MTEB covering Wikipedia, Finance, Medical, and Encyclopedia domains.
High-Fidelity Synthesis: Generated using CosyVoice-3 with diverse speaker profiles, totaling 190.4 hours of audio.
Robustness Evaluation: Explicitly models four acoustic conditions: Clean, Low Noise (20dB), Medium Noise (10dB), and High Noise (0dB).
MTEB Compatibility: Follows standard JSONL/BEIR formatting for seamless integration into modern retrieval pipelines.

📂 Dataset Structure

The dataset is organized by language and subset. Each subset (e.g., fiqa) contains the original text documents and the synthesized audio queries under different SNR conditions.

SQuTR/
└── source_data/
    ├── en/ (English Datasets: fiqa, hotpotqa, nq)
    │   └── [subset_name]/
    │       ├── audio_clean/              # Clean original audio files (.wav)
    │       ├── audio_noise_snr_0/        # Audio with 0dB Signal-to-Noise Ratio
    │       ├── audio_noise_snr_10/       # Audio with 10dB Signal-to-Noise Ratio
    │       ├── audio_noise_snr_20/       # Audio with 20dB Signal-to-Noise Ratio
    │       ├── qrels/                    # Query relevance judgments (TSV/JSONL)
    │       ├── corpus.jsonl              # Text corpus documents
    │       ├── queries.jsonl             # Original text queries
    │       ├── queries_with_audio_clean.jsonl         # Metadata mapping text to clean audio
    │       ├── queries_with_audio_noise_snr_0.jsonl   # Metadata for 0dB noise queries
    │       ├── queries_with_audio_noise_snr_10.jsonl  # Metadata for 10dB noise queries
    │       └── queries_with_audio_noise_snr_20.jsonl  # Metadata for 20dB noise queries
    └── zh/ (Chinese Datasets: DuRetrieval, MedicalRetrieval, T2Retrieval)
        └── [subset_name]/
            └── (Same structure as above)

💾 How to Use the Dataset

You can download the dataset directly from this Hugging Face repository. To use the evaluation scripts, please refer to our GitHub Repository.

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