Stop Transcript Repeats: Cleaning Pipeline Guide

Hey guys! Let's dive into how we can seriously level up our transcription game. We're talking about tackling those pesky repetitive hallucinations in our text and introducing a slick cleaning pipeline. Trust me, this is gonna make a huge difference in the quality of our transcriptions. So, let’s get started!

The Problem: Addressing Gaps in Current Cleaning Processes

Currently, our cleaning process mainly focuses on removing prompt fragments and formatting issues. But, we're falling short when it comes to mechanically suppressing repetitive hallucinations, especially in languages like Japanese. Think about it: we often see short words repeated, sentences going on loop, enumeration patterns (A, B, A, B...), and those pesky outro phrases like "Thank you for watching!" that only belong at the end. We need a robust solution to handle all this.

Our current cleaning methods primarily target prompt fragments and formatting removal, which leaves a significant gap in addressing repetitive hallucinations. In languages like Japanese, these hallucinations often manifest as short words repeated excessively, sentences looping continuously, enumeration patterns (e.g., A, B, A, B...), and the intrusion of outro phrases such as “ご視聴ありがとうございました” (Thank you for watching), which should only appear at the end of the content. To ensure high-quality transcriptions, we need a more robust process capable of identifying and mitigating these issues. Our goal is to develop a system that effectively handles these complex scenarios, thereby improving the clarity and professionalism of the final transcript.

The current system struggles with the subtle nuances of repetitive text, especially in languages like Japanese where short word repetitions are common. This issue extends beyond mere word repetition, encompassing repetitive phrases, sentences, and even structural patterns. We need to dive deeper and implement a comprehensive solution that recognizes and rectifies these patterns without compromising the integrity of the original content. To achieve this, we must consider various factors such as the context of the repetitions, their frequency, and their impact on the overall readability of the transcription. By addressing these challenges, we can significantly enhance the reliability and user satisfaction of our transcription services.

To truly enhance the quality of our transcriptions, we need a system that can identify and intelligently suppress these repetitive elements. This involves not only detecting the repetitions but also understanding their context to ensure that necessary repetitions (like emphasis) are preserved while unnecessary ones are removed. Developing this level of sophistication in our cleaning process will require a multi-faceted approach, including advanced algorithms and configurable parameters that can be adjusted based on the specific characteristics of different languages and content types. It’s about creating a balanced, efficient system that significantly reduces repetitive hallucinations while maintaining the natural flow and meaning of the transcribed text.

The Goal: Implementing a Comprehensive Cleaning Pipeline

Our main goal is to create a Cleaning Pipeline that's inspired by the structure and safeguards of the obsidian-ai-transcriber project. This pipeline will handle:

• Prompt removal
• Repetitive hallucination suppression (short words, phrases, sentences, paragraphs, enumerations, and endings)
• Lightweight validation of Japanese text quality (optional)

We aim to do this gradually and safely, making sure everything works smoothly.

The goal is to implement a comprehensive Cleaning Pipeline inspired by the robust structure and safety measures found in the obsidian-ai-transcriber project. This pipeline will methodically address several key areas to enhance transcription quality. First, it will focus on prompt removal, ensuring that any extraneous instructions or cues embedded in the input text are cleanly excised. Second, and more critically, the pipeline will tackle repetitive hallucination suppression. This involves identifying and mitigating repetitions at various linguistic levels, including short words, medium-to-long phrases, complete sentences, paragraphs, enumeration patterns, and ending sequences. Third, we aim to incorporate a lightweight validation step specifically tailored for Japanese text quality. This step, though optional, will serve as an additional layer of scrutiny to catch subtle errors and inconsistencies.

The pipeline's design emphasizes a phased, incremental rollout, ensuring that each component is thoroughly tested and validated before full integration. This approach minimizes risks and allows for iterative refinement based on real-world performance. Furthermore, the safety mechanisms embedded in the obsidian-ai-transcriber project will be adopted to prevent unintended data loss or corruption during the cleaning process. Our objective is to create a cleaning system that not only improves transcription accuracy and clarity but also operates with a high degree of reliability and safety. This will result in transcriptions that are more polished, professional, and user-friendly, ultimately enhancing the value of our transcription services.

Our vision for the Cleaning Pipeline extends beyond mere error correction. We aim to create a system that proactively enhances the overall quality of the transcription by ensuring consistency, clarity, and coherence. The pipeline will serve as a critical component in our transcription workflow, acting as a final polish to the output before it is delivered to the end-user. By carefully managing the different stages of the cleaning process and integrating various quality checks, we can ensure that the final product is of the highest standard. This commitment to quality will not only improve user satisfaction but also reinforce our reputation for delivering reliable and accurate transcription services.

What We're Not Changing

To keep things focused, we won't be:

• Changing the model selection or audio splitting logic
• Adding UI toggles (it'll be always on). Settings will be managed internally.

We're keeping our focus laser-sharp to deliver the most impactful improvements without overcomplicating things. This means we're not touching the model selection or audio splitting logic. These are complex areas that require separate consideration and optimization. Our immediate priority is to enhance the text cleaning process, and we want to do it efficiently. Similarly, we're deliberately avoiding the addition of user interface (UI) toggles for this feature. The cleaning pipeline will be designed to operate seamlessly in the background, always-on, to ensure consistent results. This simplifies the user experience and prevents any potential confusion or errors that might arise from manually toggling cleaning features.

The configuration and settings for the cleaning pipeline will be managed internally through constants and configuration files. This approach provides us with the flexibility to fine-tune the cleaning process without exposing complex settings to the end-user. It also ensures that we can quickly deploy updates and improvements without requiring user intervention. By centralizing the settings, we maintain control over the cleaning process and can ensure that it aligns with our quality standards. This approach is consistent with our overall philosophy of providing a reliable, hands-off transcription experience that delivers high-quality results automatically.

This focused approach allows us to address the core issues of repetitive hallucinations and prompt contamination effectively. By avoiding unnecessary changes, we minimize the risk of introducing new bugs or performance bottlenecks. Our goal is to deliver a solid, dependable cleaning pipeline that enhances the quality of transcriptions without disrupting the existing workflow or user experience. This methodical approach to development ensures that we can deliver meaningful improvements in a timely manner and maintain the stability of our transcription services. We believe that this targeted enhancement will significantly improve the overall satisfaction of our users and solidify our position as a provider of top-tier transcription solutions.

Acceptance Criteria: What Success Looks Like

We'll know we've nailed it when:

• Short word repetition is reduced: No more excessive