add readme details

README.md

@@ -20,3 +20,89 @@ dataset_info:
   download_size: 5977562856
   dataset_size: 40431494
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+
+# Dataset Card for OpenSLR Nepali Large ASR Cleaned
+
+## Table of Contents
+- [Dataset Card for OpenSLR Nepali Large ASR Cleaned](#dataset-card-for-openslr-nepali-large-asr-cleaned)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  
+
+## Dataset Description
+
+- **Homepage:** [Original OpenSLR Large Nepali ASR Dataset link](https://www.openslr.org/54/)
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Sagar Sapkota](mailto:spkt.sagar@gmail.com)
+
+### 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.
+```python
+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
+
+```js
+{
+    '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': 'hffk .> ,?$G'
+}
+```
+
+### 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
+
+### Data Splits
+
+The dataset is not split. The consumer should split it as per their requirements.