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Datasets:
Titung
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seke-nepali-dataset

Tasks:
Automatic Speech Recognition
Audio Classification
Modalities:
Audio
Text
Formats:
parquet
Sub-tasks:
keyword-spotting
speaker-identification
audio-language-identification
Languages:
Seke (Nepal)
Size:
< 1K
Tags:
endangered-language
seke
nepal
sino-tibetan
low-resource
linguistics
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metadata 
license: cc-by-4.0
language:
  - skj
task_categories:
  - automatic-speech-recognition
  - audio-classification
task_ids:
  - keyword-spotting
  - speaker-identification
  - audio-language-identification
pipeline_tag: automatic-speech-recognition
pretty_name: Seke (SKJ) Language Corpus — Endangered Language Alliance
size_categories:
  - n<1K
tags:
  - endangered-language
  - seke
  - nepal
  - sino-tibetan
  - low-resource
  - linguistics
  - audio
  - field-recording
  - whisper
  - wav2vec2

🏔️ Seke (SKJ) Language Dataset

Endangered Language Alliance × Internet Archive

Seke (skj) is a critically endangered Sino-Tibetan language spoken by approximately 700 people in the five villages of Upper Mustang district, Nepal, and in diaspora communities in New York City. This dataset represents one of the most complete public audio corpora of Seke ever assembled. Dataset created by Anil Tamang (himalaya-ai).

📊 Dataset Statistics

Metric Value
Total audio clips 529
Total duration 17.8 minutes
Number of speakers 2
Sample rate 16000 Hz
Format 16-bit PCM WAV (mono)
Language ISO skj
Date range 2018–2021
Assembled 2026-04-25

Quality Tier Distribution

Tier Count Description
A 20 Gold: elicited clips, controlled studio recording, short utterances (<10s), SNR>20dB
B 205 Silver: field recordings, good quality, longer utterances
C 304 Bronze: field recordings with background noise, or long sessions

Linguistic Type Distribution

| Language Description | 515 | | Primary Text | 14 |

🔤 Language Information

  • ISO 639-3: skj
  • Family: Sino-Tibetan → Tibeto-Burman → Bodish
  • Dialect: Primarily Chhusang (Tshugsang) dialect
  • Typology: Verb-final word order, ergative alignment, evidential marking
  • Script: No standardized orthography (romanized transcriptions in this dataset)
  • Endangerment: Critically endangered (~700 speakers)
  • Region: Upper Mustang, Gandaki Province, Nepal; NYC diaspora

🗂️ Dataset Fields

Field Type Description
audio Audio 16kHz mono WAV, trimmed and normalized
duration_s float Duration in seconds
gloss_en string English translation/gloss (from ELA elicitation)
transcription_seke string Whisper ASR output (romanized approximation)
phonemes_ipa string IPA phonemes from Allosaurus (where available)
grammatical_categories string Comma-separated grammatical category tags
linguistic_type string OLAC type: Lexicon / Language Description / Primary Text
quality_tier string A/B/C quality tier
speaker_name string Speaker name
recording_date string Session date
source_identifier string Internet Archive item identifier
source_url string Direct link to IA item
is_swadesh bool Part of Swadesh list elicitation
swadesh_number int Swadesh list number (-1 if not applicable)
is_alternative_form bool Alternative pronunciation of same sentence
snr_db float Signal-to-noise ratio in dB
loudness_lufs float Original LUFS loudness (pre-normalization)

🚀 Usage 

from datasets import load_dataset

# Load full dataset
ds = load_dataset("Titung/seke-nepali-dataset")

# Load only high-quality elicited clips (Tier A)
ds_gold = load_dataset("Titung/seke-nepali-dataset", split="train")
ds_gold = ds_gold.filter(lambda x: x["quality_tier"] == "A")

# Fine-tune Whisper on Seke
from transformers import WhisperForConditionalGeneration, WhisperProcessor
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")

# Prepare features
def prepare_dataset(batch):
    audio = batch["audio"]
    batch["input_features"] = processor(
        audio["array"], sampling_rate=audio["sampling_rate"],
        return_tensors="pt"
    ).input_features[0]
    batch["labels"] = processor.tokenizer(
        batch["gloss_en"]
    ).input_ids
    return batch

ds_prepared = ds["train"].map(prepare_dataset)

📖 Source & Attribution

All audio data sourced from the Endangered Language Alliance (ELA) archive on Internet Archive:

  • Collection: https://archive.org/details/elalliance
  • ELA website: https://www.elalliance.org
  • Primary researcher: Ross Perlin, Co-Director, ELA
  • Primary speakers: Rasmina Gurung, Nora Gurung, and others
  • Recorded 2018–2021 at Columbia University and in Upper Mustang, Nepal
  • Supported in part by the National Endowment for the Humanities

Original data archived under Creative Commons Attribution 4.0 (CC-BY-4.0). This dataset derivative is also released under CC-BY-4.0. Please cite ELA and the original depositors in any academic use.

Suggested Citation 

Endangered Language Alliance. (2021). Seke (SKJ) Language Recordings.
Internet Archive. https://archive.org/details/elalliance

Dataset assembled by [Anil Tamang/himalaya-ai] (2026).
Available at: https://huggingface.co/datasets/Titung/seke-nepali-dataset

⚠️ Important Notes

  1. Transcriptions are approximate: The transcription_seke field contains Whisper ASR output on an unseen language — treat as a phonetic approximation, not verified orthography. Manual validation by a Seke speaker is strongly recommended.
  2. No standardized orthography: Seke has no official writing system. Romanizations may vary.
  3. Ethical use: This data documents a critically endangered language. Please use it to support language preservation, not for commercial exploitation.
  4. Contact ELA: For research partnerships or access to additional materials: https://www.elalliance.org/contact