README.md

metadata

license: cc-by-sa-4.0
dataset_info:
  features:
    - name: utterance_id
      dtype: string
    - name: speaker_id
      dtype: string
    - name: utterance
      dtype:
        audio:
          sampling_rate: 16000
    - name: transcription
      dtype: string
    - name: num_frames
      dtype: int32
  config_name: all
  splits:
    - name: train
      num_bytes: 41063364
      num_examples: 157905
  download_size: 5978669282
  dataset_size: 41063364

Dataset Card for OpenSLR Nepali Large ASR Cleaned

Table of Contents

• Dataset Card for OpenSLR Nepali Large ASR Cleaned
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits

Dataset Description

• Homepage: Original OpenSLR Large Nepali ASR Dataset link
• Repository: [Needs More Information]
• Paper: [Needs More Information]
• Leaderboard: [Needs More Information]
• Point of Contact: Sagar Sapkota

Dataset Summary

This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file. The data set has been manually quality-checked, but there might still be errors.

The audio files are sampled at a rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.

For your reference, following was the function applied on each of the original openslr utterances.

import torchaudio

SAMPLING_RATE = 16000

def process_audio_file(orig_path, new_path):
    """Read and process file in `orig_path` and save it to `new_path`"""
    waveform, sampling_rate = torchaudio.load(orig_path)
    if sampling_rate != SAMPLING_RATE:
        waveform = torchaudio.functional.resample(waveform, sampling_rate, SAMPLING_RATE)
    #  trim end silences with Voice Activity Detection
    waveform = torchaudio.functional.vad(waveform, sample_rate=SAMPLING_RATE)
    torchaudio.save(new_path, waveform, sample_rate=SAMPLING_RATE)

Supported Tasks and Leaderboards

• automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition.

Languages

Nepali

Dataset Structure

Data Instances

{
    'utterance_id': 'e1c4d414df',
    'speaker_id': '09da0',
    'utterance': {
        'path': '/root/.cache/huggingface/datasets/downloads/extracted/e3cf9a618900289ecfd4a65356633d7438317f71c500cbed122960ab908e1e8a/cleaned/asr_nepali/data/e1/e1c4d414df.flac',
        'array': array([-0.00192261, -0.00204468, -0.00158691, ...,  0.00323486, 0.00256348,  0.00262451], dtype=float32),
        'sampling_rate': 16000
    },
    'transcription': '२००५ मा बिते',
    'num_frames': 42300
}

Data Fields

• utterance_id: a string identifying the utterances
• speaker_id: obfuscated unique id of the speaker whose utterances is in the current instance
• utterance:
  • path: path to the utterance .flac file
  • array: numpy array of the utterance
  • sampling_rate: sample rate of the utterance
• transcription: Nepali text which spoken in the utterance
• num_frames: length of waveform array

Data Splits

The dataset is not split. The consumer should split it as per their requirements.