| --- |
| 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. |
|
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|
