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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

fixed multilingual ds push + removed noise addition #156

Open kartikbhtt7 opened 1 month ago

kartikbhtt7 commented 1 month ago

Summary by CodeRabbit

coderabbitai[bot] commented 1 month ago
📝 Walkthrough
📝 Walkthrough ## Walkthrough The pull request introduces significant modifications to the `WhisperFineTuning` class in the `workflow/training/whisper.py` file. It replaces existing audio processing methods with a new `text_to_audio` method that generates audio files using Azure's Cognitive Services for text-to-speech. The `process_and_upload_dataset` method is updated to generate audio files for text chunks, and the dataset splitting logic now utilizes the `train_test_split` method from the `datasets` library. Enhanced logging is also implemented for improved traceability. ## Changes | Files | Change Summary | |--------------------------------------|--------------------------------------------------------------------------------------------------------| | `workflow/training/whisper.py` | - Added `text_to_audio(self, text, output_path)` method. | | | - Updated `process_and_upload_dataset(self, dataset, dataset_name)` method with new audio generation logic. | | | - Enhanced logging for better visibility into processing steps. | | | - Replaced custom dataset splitting with `train_test_split` from the `datasets` library. | ## Possibly related PRs - #144: This PR introduces enhancements to the `WhisperFineTuning` class, including a `process_and_upload_dataset` method that processes text to audio using Azure's speech synthesis service, directly related to the changes made in the main PR.

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