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Datasets:
syzym
/
xbmu_amdo31

Tasks:
Automatic Speech Recognition
Modalities:
Text
Languages:
tib
Size:
10K<n<100K
License:
Dataset card Viewer Files Community
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    KeyError
Message:      'wav'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1748, in _prepare_split_single
                  for key, record in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 126, in _generate_examples
                  example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
              KeyError: 'wav'
              
              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/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1789, in _download_and_prepare
                  super()._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1627, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1784, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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__key__
string
__url__
string
wav
audio
train/a_0_lmc/a_0_lmc-A02_536
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_700
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_825
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_502
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_855
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_814
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_808
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_856
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_616
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_701
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_526
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_645
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_528
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_553
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_501
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_468
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_667
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_586
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_647
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_627
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_588
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_813
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_723
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_483
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_662
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_883
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_651
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_786
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_597
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_811
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_792
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_451
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_603
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_784
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_550
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_871
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_893
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_742
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_683
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_540
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_889
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_505
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_640
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train/a_0_lmc/a_0_lmc-A02_716
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train/a_0_lmc/a_0_lmc-A02_575
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train/a_0_lmc/a_0_lmc-A02_832
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train/a_0_lmc/a_0_lmc-A02_870
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train/a_0_lmc/a_0_lmc-A02_632
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train/a_0_lmc/a_0_lmc-A02_462
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train/a_0_lmc/a_0_lmc-A02_857
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_681
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train/a_0_lmc/a_0_lmc-A02_491
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train/a_0_lmc/a_0_lmc-A02_628
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train/a_0_lmc/a_0_lmc-A02_566
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_752
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_812
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_617
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_652
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_820
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train/a_0_lmc/a_0_lmc-A02_578
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train/a_0_lmc/a_0_lmc-A02_470
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train/a_0_lmc/a_0_lmc-A02_694
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train/a_0_lmc/a_0_lmc-A02_845
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train/a_0_lmc/a_0_lmc-A02_653
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train/a_0_lmc/a_0_lmc-A02_758
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train/a_0_lmc/a_0_lmc-A02_497
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train/a_0_lmc/a_0_lmc-A02_624
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train/a_0_lmc/a_0_lmc-A02_725
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_890
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train/a_0_lmc/a_0_lmc-A02_738
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_781
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_629
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train/a_0_lmc/a_0_lmc-A02_585
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train/a_0_lmc/a_0_lmc-A02_862
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train/a_0_lmc/a_0_lmc-A02_717
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train/a_0_lmc/a_0_lmc-A02_671
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train/a_0_lmc/a_0_lmc-A02_573
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train/a_0_lmc/a_0_lmc-A02_705
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train/a_0_lmc/a_0_lmc-A02_880
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train/a_0_lmc/a_0_lmc-A02_763
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train/a_0_lmc/a_0_lmc-A02_499
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train/a_0_lmc/a_0_lmc-A02_460
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_656
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_489
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_709
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_488
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_799
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_574
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_688
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train/a_0_lmc/a_0_lmc-A02_556
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_636
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_866
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_582
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_702
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_512
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_456
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_788
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_680
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_795
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
train/a_0_lmc/a_0_lmc-A02_843
hf://datasets/syzym/xbmu_amdo31@60a8b7231ebf8bf13c179ddebfdfe03d3b5e8e13/data/wav/train.tar.gz
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Dataset Card for [XBMU-AMDO31]

Dataset Summary

XBMU-AMDO31 dataset is a speech recognition corpus of Amdo Tibetan dialect. The open source corpus contains 31 hours of speech data and resources related to build speech recognition systems, including transcribed texts and a Tibetan pronunciation dictionary.

Supported Tasks and Leaderboards

automatic-speech-recognition: The dataset can be used to train a model for Amdo Tibetan Automatic Speech Recognition (ASR). It was recorded by 66 native speakers of Amdo Tibetan, and the recorded audio was processed and manually inspected. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://github.com/sendream/xbmu_amdo31#leaderboard and ranks models based on their WER.

Languages

XBMU-AMDO31 contains audio, a Tibetan pronunciation dictionary and transcription data in Amdo Tibetan.

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

The dataset has three splits: train, evaluation (dev) and test.Each speaker had approximately 450 sentences, with a small number of individuals having fewer than 200 sen

Subset Hours Male Female Remarks
Train 25.41 27 27 18539 sentences recorded by 54 speakers
Dev 2.81 2 4 2050 sentences recorded by 6 speakers
Test 2.85 3 3 2041 sentences recorded by 6 speakers

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

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Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

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Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

This dataset is distributed under CC BY-SA 4.0.

Citation Information

[More Information Needed]

Contributions

Thanks to @speechless-z for adding this dataset.

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**Homepage:**
github.com
**Repository:**
github.com
Paper:
Paper:
**Leaderboard:**
github.com
Point of Contact:
xxlgy@xbmu.edu.cn