u/AllowFreeSpeech

I had a Google AI Ultra subscription that I used to make Cinematic videos in Gemini Notebook. Unfortunately, videos continue to have serious problems:

1. Cinematic videos have a very high-pitch screeching/echo effect in the voice, making it unpleasant and painful to hear. This is the reason why I canceled, as it cannot be fixed by regenerating the video. I have been reporting this issue to Google for months, but they choose to do nothing. These videos are a complete embarrassment for this reason, and I wouldn't share them with anyone.

2. Explainer videos too increasingly have this pitch problem with a female voice.

3. Cinematic videos have clips within them that are sometimes cut off prematurely. This is annoying, but it is infrequent, and it can be fixed by regenerating the video, so it is not the reason why I canceled.

Here is an LLM's understanding of the problem:

> The problem with these TTS-generated female voice seems to be more complex than simply having an excessively high average pitch. It sounds unusually shrill, piercing, and fatiguing, with exaggerated emphasis on certain words and particularly on syllables or sounds toward the ends of words. My impression is that this may be a combination of excessive vocal brightness/high-frequency energy, overly large pitch excursions on stressed syllables, harsh resonance or sibilance, and exaggerated prosody that places too much stress, duration, or pitch movement on parts of speech that should sound relatively neutral. The result is a voice that can feel almost screeching at moments and generally unnatural or uncomfortable to listen to. Simply lowering the overall pitch may therefore not fully solve it; the underlying issue may require reducing brightness/harshness, moderating pitch variation and expressiveness, and softening the model's tendency to overemphasize syllables and word endings.

Deep Dive audio within Gemini Notebook, while very good overall, also occasionally has a garbled audio problem. At least it doesn't have the aforesaid pitch problem.

It also doesn't help that the Gemini model has fallen behind dramatically, by multiple generations, relative to other models like by OpenAI, Anthropic, Kimi, etc.

It's like Google doesn't take anything seriously.

My original goal was to create tutorial type videos. Gemini Notebook's Explainer videos can make these for non-technical topics, but not for coding topics where much code needs to be displayed. I now make these using a custom ChatGPT Work Skill in Max Thinking mode, combined with Google Vids, translating a multi-PPTX deck into an MP4 video. I will document my steps in a future post at r/GoogleVidsAI. Examples of created tutorial presentation videos are here and here.

In my current experience, all ChatGPT DeepResearch reports are powered by a super light version. They're nearly worthless. It is enshittification in its purest form, considering the reports used to be great in the distant past. It even misrepresented the title of the report. It cited zero references. This is with the paid Plus plan. Here is what ChatGPT then had to say about its own report:

> The previous report should be discarded rather than merely expanded around its edges.

In contrast, Gemini made a DeepResearch report that was 4x larger and better, also with 131 references cited.

You can see the shared results: ChatGPT vs Gemini

I am sharing my approach for making long videos in a way that attempts to produce a coherent and cohesive result. This works for both Explainer and Cinematic modes of NotebookLM. You can watch a short NotebookLM video explaining the high-level process here.

Since each individual video is only 5-10 minutes in duration, the trick is to make multiple ordered segments that flow well together. There is no limit to the number of segments, as this is limited only by your usage quota, but in practice I make them 5 to 24 segments long, and the exact number is determined by AI. Having too many segments risks repetitiveness, whereas too few segments risks being overpacked.

Here are examples:

• Cinematic: The Black Hole Information Paradox and Alternatives to Gravitational Collapse (34 min) (5 segments)
• Explainer with built-in Heritage style: The Quantum Programming Ecosystem (2h 43min) (20 segments) (male voice)
• Explainer with custom visual style: Apache Burr: Building Observable, Stateful Python AI Applications (3h 1min) (24 segments) (male voice)

Below are my steps.

1. Create notebook with reports or sources

My first step is to compile DeepResearch reports on a topic of interest. For this I use the DeepResearch feature in ChatGPT and Gemini. I also use a custom DeepResearcher GPT which has its own rigorous and complementary approach to research. I export all three generated reports to markdown format. Alternatively, if I have a handful of PDF files, I can use them directly. Markdown reports work better than having multiple PDFs because the information is pre-digested, well organized, and every AI can read markdown files reliably at every step in the process.

ChatGPT allows exporting a DeepResearch report to markdown format directly. To export a Gemini DeepResearch report to markdown, I first export it as a Google Docs file, then download it as a file in markdown format.

The definition prompt and settings for my custom GPT are here. I cannot share the link to this custom GPT directly as it's forced by OpenAI to be private, but you are free to create something yourself with a definition that suits you. Note that it is very necessary to use an Extended Thinking model for it. Also included with my definition is a knowledge file that I upload in the custom GPT configuration.

I store all markdown artifacts in a git repository for safekeeping. I upload the aforementioned reports or sources to a single new NotebookLM notebook.

2. Create segment prompts

I use an AI to write a detailed video topic customization prompt for each video segment. The AI also determines the number of segments and their order.

I actually use the same custom GPT as before to do this, but this is not a necessary coupling. I could alternatively had used a separate dedicated custom GPT for this task. My custom GPT has a highly-detailed command called VID which takes the uploaded markdown files and produces a downloadable file with a list of video generation prompts. For it to work, I have to upload all my previously generated research reports or sources to the custom GPT, and then type the VID command. It is absolutely necessary to use the Extended Thinking model for this.

If you want fewer segments, you can use VID.min command instead. If you want to be more comprehensive with more segments, use the VID.max command instead. These commands are also implemented in the custom GPT. A sample list of the video segment prompts as generated by VID.max is here.

The custom GPT also defines CHK and MRG commands for a self-critique of its result and for addressing the critique respectively. These are useful for optimizing the output of the VID commands. The CHK command is used to check the quality of the generated segment prompts, and the MRG command is used to refine the prompts based on the critique provided by CHK. Moreover, these commands can be used in a loop until convergence is reached, which is typically in 0 to 3 iterations. It is not necessary to use these two commands, but they can be helpful in hunting for missed information from the sources. I do use them.

The output file with the segment prompts also contains a few additional prompts, namely:

1. Two visual style descriptions: These are optionally relevant for the Explainer mode only. I ignore them for Cinematic mode. The Explainer mode allows a custom visual style to be specified, for which I individually try both styles to see which looks better. It is of course not necessary to use a custom visual style, and an existing good one like Heritage works well. It is however absolutely important to use a consistent visual across all segments, never the auto-selected random style.
2. Shared content style prompt: I append this to the end of each individual segment's prompt by copy-pasting it. I may first edit it slightly for taste.

3. Customize shared content style prompt

For Explainer videos for coding related topics only, I append this to the shared content style prompt:

> Generously show actual code snippets, without which the content's understanding remains shallow and ungrounded, and ensure that every code snippet fits within the slide without running off the edge.

For all Explainer videos, I append this to my shared content style prompt:

> Aggressively display high-contrast text labels and captions on each slide to reinforce all concepts, otherwise it's hard to understand what is being said. Skip the greetings and sign-offs altogether, but continue to maintain a friendly tone.

For Cinematic videos, I append this to the shared content style prompt:

> Be sure to include richly-styled visualizations of the concepts so as to keep the viewer maximally engaged.

I do this customization before appending the shared content style prompt to each individual segment's prompt.

4. Create video segments

I ensure that all sources are uploaded to a new NotebookLM notebook. The segment prompts file however must of course NOT be uploaded as a source.

I use the prompts to create the video segments. If a segment doesn't come out well, I regenerate it. If a prompt needs a change, I edit it and regenerate the affected segment or all segments.

If in case the generated Cinematic video segments have significant conceptual repetition, especially of the overall topic, I use the custom GPT to add anti-repetition guards to each prompt and to the shared prompt. This instructs the video generator to strictly avoid repetition, particularly to avoid reintroducing the overall topic in each segment.

I take significant care to ensure that I specify each segment's prompt correctly, also verifying it for correctness after the video's generation.

I correctly prefix the number of each generated segment to its name, e.g. 01 , 02 , etc. Do not use the prefix 01. (with a period) because the period is interpreted as a file extension separator when downloading the segment, breaking its name.

I carefully watch each video segment in order before considering it final. Never share what you haven't watched and vetted yourself. After all segments are finalized, I download them.

5. Ensure consistent voice across segments

For videos that are to be shared, it is important to ensure a consistently male or female voice across all segments.

All Cinematic videos currently have a female voice, so there is no possibility of having inconsistent voices.

Explainer videos can somewhat randomly have a male or female voice. At this time, the only way to obtain vocal consistency is by repeated regeneration of the inconsistent segments, perhaps in batches of two attempts per segment. For stubbornly inconsistent segments, I may increase the number of regenerations as per the Fibonacci sequence to 3, then 5.

6. Merge video segments

I stitch the finalized and downloaded video segments together using ffmpeg, the instructions for which on Mac are here.

7. Optionally create a chapter list

For Explainer videos that are to be shared, I create a draft chapter list using ffprobe, such as for YouTube, etc. Note that ffprobe comes bundled when installing ffmpeg. The instructions and script for creating the chapter list are here. I edit the chapter list to ensure that the chapter titles are correct.

As for Cinematic videos, it is not entirely necessary to create a chapter list for them as their segments flow more naturally.