rishi70612/training_dataset

Dataset Card for OpenSLR Nepali Large ASR Cleaned

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)

How to use?

There are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above.

1. First, to download the original dataset with HuggingFace's Dataset API:

from datasets import load_dataset

dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="original", split='train')

2. To download the preprocessed dataset:

from datasets import load_dataset

dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="cleaned", split='train')

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.