README.md

metadata

language:
  - ml
license: cc-by-sa-4.0
task_categories:
  - automatic-speech-recognition
  - text-to-speech
tags:
  - malayalam
  - speech
  - audio
pretty_name: Malayalam Speech Dataset (VibeVoice)
size_categories:
  - 1K<n<10K

Malayalam Speech Dataset (VibeVoice)

This dataset contains Malayalam speech audio files and their corresponding transcriptions, prepared for fine-tuning ASR (Automatic Speech Recognition) models like VibeVoice.

Dataset Description

The dataset consists of approximately 4,126 Malayalam audio clips, split into training and testing sets with a 90/10 ratio, stratified by speaker gender.

• Total Audio Files: 4,126
• Train Split: 3,712 samples
• Test Split: 414 samples
• Total Duration: ~5-6 hours (estimated)
• Format: 16kHz WAV
• Annotation: JSON segments and split-specific metadata.jsonl entries.

Dataset Structure

The dataset is organized into train and test directories:

1. train/: Contains 3,712 audio-JSON pairs and metadata.jsonl.
2. test/: Contains 414 audio-JSON pairs and metadata.jsonl.

Each split contains:

• [id].wav: The audio file.
• [id].json: Segment information and transcription.
• metadata.jsonl: Mapping of audio files to transcriptions.

VibeVoice JSON format

Each audio file is accompanied by a JSON file with the following structure:

{
  "audio_duration": 2.523,
  "audio_path": "female_mlf_01130_00015565294.wav",
  "segments": [
    {
      "start": 0.0,
      "end": 2.523,
      "text": "അങ്ങനെ രണ്ടാം ലോകമഹായുദ്ധം അവസാനിച്ചു",
      "speaker": 0
    }
  ]
}

metadata.jsonl format

{"file_name": "female_mlf_01130_00015565294.wav", "transcription": "അങ്ങനെ രണ്ടാം ലോകമഹായുദ്ധം അവസാനിച്ചു"}

How to use

from datasets import load_dataset

dataset = load_dataset("Arjunj/malayalam_speech", split="train")

# Access a sample
sample = dataset[0]
print(sample["audio"])
print(sample["transcription"])

Source

This dataset was processed and collected from various Malayalam TTS/ASR sources, including OpenSLR IDs 63 and 64, formatted specifically for VibeVoice fine-tuning requirements.

License

Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).