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BharatSahAIyak / autotune

A comprehensive toolkit for seamless data generation and fine-tuning of NLP models, all conveniently packed into a single block.
MIT License
9 stars 5 forks source link

Created pipeline for ASR data creation. #141

Open SarveshAtawane opened 3 months ago

SarveshAtawane commented 3 months ago

Completes this https://github.com/BharatSahAIyak/ai-tools/issues/98

Key Features

Process Flow

  1. Checks if the dataset only has one column i.e. of text.

  2. Text-to-Audio Conversion:

    • Converts each text entry to speech using Azure TTS
    • Applies varied word loudness and adds noise to the audio
    • Handles long text by splitting into 250-character chunks

  3. Dataset Processing:

    • Processes both train and test splits if available
    • If no test split is present, creates one from the processed train data
    • Maintains the original split structure of the dataset

  4. Chunk Management:

    • Splits text longer than 250 characters into chunks
    • Keeps shorter texts intact
    • Associates each chunk with its corresponding audio file

  5. Dataset Structure:

    • Resulting dataset contains 'audio' (file paths) and 'sentence' (text chunks) columns

  6. Upload:

    • Pushes the processed dataset to Hugging Face Hub
    • The new dataset is named with addition of _audio to the original name.
    • Creates a new repository if it doesn't exist

Key Changes

1. Added process_and_upload_dataset Function

2. Added output_varied_word_loudness_with_noise Function

Summary by CodeRabbit

coderabbitai[bot] commented 3 months ago

[!IMPORTANT]

Review skipped

Review was skipped as selected files did not have any reviewable changes.

Files selected but had no reviewable changes (1) * sample.env

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Walkthrough ## Walkthrough The changes introduce four new dependencies in `pyproject.toml` for advanced typing, Hugging Face model integration, Azure cognitive services for speech, and enhanced text manipulation. In `workflow/training/whisper.py`, new methods are implemented in the `WhisperFineTuning` class for audio processing and dataset management, significantly expanding the class's functionality. ## Changes | Files | Change Summary | |---------------------------|----------------------------------------------------------------------------------------------------| | pyproject.toml | Added dependencies: `typing-extensions`, `huggingface-hub`, `azure-cognitiveservices-speech`, and `textwrap3`. | | workflow/training/whisper.py | Added methods `output_varied_word_loudness_with_noise` and `process_and_upload_dataset` in `WhisperFineTuning` class. |

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