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Datasets:
mami123
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1hr_demo

Modalities:
Audio
Tags:
assamese
audio
physics
class10
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metadata
license: mit
tags:
  - assamese
  - audio
  - physics
  - class10
delimiter: '|'
column_names:
  - file_name
  - text
features:
  - name: file_name
    dtype: audio
  - name: text
    dtype: string

Step 1: Audio Acquisition and Preparation

  • Source: The primary audio source was a YouTube video titled "মানুহৰ চকু আৰু বাৰে বৰণীয়া পৃথিৱী Class 10 Science Chapter11||The Human Eye and The colourful World" available at https://www.youtube.com/watch?v=vhjQOBZIJlQ&t=1566s.
  • Download & Format: The video's audio was downloaded and converted to master.mp3. While lossless compression (like WAV) is generally preferred for audio processing, MP3 was chosen for practical purposes, acknowledging that it is an already compressed format.

Step 2: Accurate Transcription (Text Ground Truth)

  • Process: The master.mp3 audio was manually transcribed into a plain text file (transcript.txt) to ensure 100% accuracy. This manual transcription is crucial as it serves as the "ground truth" for all subsequent alignment and training.
  • Challenge: Initial attempts using generic AI transcription services failed to accurately process the Assamese language, often producing broken Bengali or mixed-language output. This highlighted the necessity of a human-verified transcript for low-resource languages.

Step 3: Forced Alignment (Word-Level Timestamps)

  • Goal: To achieve a "gold standard" dataset by obtaining word-level timestamps, linking every spoken word in master.mp3 to its precise start and end time.
  • Tool: The stable-whisper library was selected for this forced alignment task.
  • Challenges & Solutions:
    • Local GPU Failure: Initial attempts to run the process on a local Mac with a Metal GPU failed due to deep-level PyTorch incompatibilities (NotImplementedError, Invalid buffer size). This demonstrated the current limitations of the local hardware/software stack for this specific task.
    • Cloud GPU Solution: To overcome local issues, the process was moved to Google Colab with a T4 GPU runtime. This provided a stable, pre-configured environment.
    • Library Bugs: The stable-whisper library exhibited internal bugs related to FFmpeg and its own API. These were bypassed by surgically modifying the Python script to use the base whisper library for audio loading while still using stable-whisper for the alignment itself.
    • Output: The process successfully generated an aligned_transcription.json file containing the required word-level timestamp data.

Step 4: Audio Segmentation & Metadata Generation

  • Process: The aligned_transcription.json file was used by a Python script (segment.py) to automate the segmentation.
    • Using the pydub library, the master.mp3 file was loaded.
    • For each word entry in the JSON, the corresponding audio segment was extracted (chunked).
    • These audio chunks were saved as individual .wav files in a wavs/ directory.
    • A metadata.csv file was generated in the LJSpeech format (path|text), linking each .wav file to its corresponding transcribed word.
  • Audio Sample Rate: A critical aspect of audio preparation is ensuring the correct sample rate. The Whisper model family requires audio at 16 kHz. The workflow handled this by resampling the audio from its original rate (e.g., 44.1 kHz) to 16 kHz before processing.

Conclusion

This structured dataset, with its word/segment-level timestamps, is now complete and hosted for demonstration purposes.