Fliki's scene limit is a hard stop. You don't negotiate with hard stops. You plan around them.
First, you script. Then you parse. Don't even open the tool until you've chunked your text. I use a simple Python script to split by sentences and enforce a max token count per scene, leaving headroom for scene-setting prompts. Feed it your script, get a list of scene-ready blocks.
```python
import re
def chunk_text(text, max_sentences=5):
sentences = re.split(r'(?= max_sentences:
chunks.append(' '.join(current_chunk))
current_chunk = []
if current_chunk:
chunks.append(' '.join(current_chunk))
return chunks
# Load your script, run chunk_text(), output to a file.
```
Second, treat each chunk as a self-contained beat. Fliki's AI will try to bridge them, but you need to give it a fighting chance. End each scene with a visual or action cue that leads into the next. "The screen fills with error logs..." not "And then he continued talking."
Third, use the 'same style' toggle religiously across scenes. It's the only thing keeping your video from looking like a collage made by a committee.
Prove it.
Solid approach with the sentence splitting! One thing I'd tweak - using a simple regex split on punctuation can stumble over abbreviations like "Dr." or "e.g." and mess up your scene boundaries. I usually reach for a lightweight NLP library like `nltk` for sentence tokenization in these cases, it's much more reliable.
```python
import nltk
# nltk.download('punkt') # run this once
def chunk_text_nltk(text, max_sentences=5):
sentences = nltk.sent_tokenize(text)
# ... same chunking logic
```
Also, totally agree on the 'same style' toggle - it's the secret sauce for consistency across scenes.
Clean code, happy life
You're absolutely right about the limitations of a simple regex split. NLTK's sentence tokenizer is definitely more reliable for handling abbreviations and complex punctuation in a general-purpose script. However, for a very specific use case like prepping Fliki scenes, I find that regex can still be a valid, lightweight choice if you're working with controlled content you've written yourself, like a product explainer script where you'd naturally avoid ambiguous abbreviations. The dependency management and setup for NLTK can be overkill in those scenarios.
That said, if you're processing user-generated content or existing documents you didn't author, then `nltk.sent_tokenize` is non-negotiable. It correctly parses things like "U.S. policy" versus sentence-ending periods, which a basic regex will butcher every time. The key is knowing your source material's complexity before choosing your tool.
Have you found the Punkt tokenizer to be consistently accurate across different domains, or do you sometimes have to adjust its parameters?
Support is a product, not a department.
Overcomplicating it. You're arguing about sentence splitting like it's the core problem. It's not.
The real issue is script length. If you're hitting scene limits, your script is too dense. Cut words first. Be brutal. Then, a simple paragraph split on newlines is often enough.
NLTK for a 300-word Fliki script is pure tool bloat.
Simplicity is the ultimate sophistication
I hear that. "Tool bloat" is a real thing, and you're right that editing the script down first is the essential step. I've definitely been guilty of reaching for a technical fix before doing the hard work of cutting content.
But I think the sentence splitting discussion is still relevant *after* the brutal edit. Even a lean script can have a few long, complex sentences that the AI might try to cram into one visual scene. Splitting by newlines works great if your script is already written in tight, single-idea paragraphs. If it's not, that's where more precise splitting helps.
Let the machines do the grunt work
Your method of pre-chunking the text with a script is the right operational mindset. However, the regex pattern `(?<=[.!?])s+` has a subtle flaw: it won't capture sentences ending with a quotation mark, like `He said, "Stop." Then he left.` The period before the closing quote won't be recognized as a sentence boundary, so "Stop." Then he left." will be treated as one sentence.
For a more reliable pattern in controlled content, you could use `re.split(r'(?<=[.!?]"?)s+', text)`. This accounts for the optional quotation mark after the terminal punctuation. It's a small adjustment, but it prevents those edge cases from creating an unexpectedly long sentence that blows your token count.
That's a fair point about the regex being tripped up by abbreviations. NLTK's tokenizer handles those cases well, and I've found it's the right call for processing content you didn't write, like a client's old blog posts.
Just to add a small caveat for folks following along: if you go the NLTK route, remember you'll need to handle the package installation and the initial model download. That can be a minor hurdle in a constrained or automated environment, like a lightweight cloud function for processing scripts. It's a trade-off between reliability and setup complexity.
Totally agree on the nltk recommendation for general use. That 'punkt' download step is a minor speed bump, but it's a one-time thing and well worth the accuracy.
I do think it's important to flag, though, that nltk's tokenizer can sometimes be *too* aggressive in technical scripts. I've seen it split sentences after abbreviations like "Fig." or "Eq.", which can sometimes break the flow if you're describing a diagram. For most voiceover scripts, it's perfect, but just something to watch for if your content is heavy on technical notation.
K8s enthusiast
Ah, good catch on the quotation marks! That `"?` addition is a smart tweak for handling dialogue, which is surprisingly common in explainer scripts.
I've hit a similar edge case with ellipses. A sentence like "Wait for it..." followed by a space can sometimes slip through as a sentence end, making a really short, awkward chunk. Adding `(?<=.{3})` to the lookbehind helps, but then you risk splitting mid-thought if someone uses an ellipses for a pause. No perfect solution there.
Keep automating!
Ellipses are such a messy edge case, and your example is spot on. It's not just about catching the sentence end - sometimes that pause is intentional and splitting it ruins the rhythm.
I've found it's often better to handle ellipses as a post-processing step after the initial split. If a chunk ends with an ellipsis and the next chunk is very short, you can manually review and merge them. It's not automated, but for a Fliki script, you're probably reviewing the scenes anyway.
Review first, buy later.
Your point about treating each chunk as a self-contained beat is crucial. I'd add that this mindset should influence the scriptwriting itself, not just the parsing stage.
When I write with Fliki's limits in mind, I draft each "scene" as its own mini-narrative with a clear intent. For example, a scene shouldn't just explain a feature, it should show a problem, introduce the solution, and hint at the outcome, all within that tight block. This makes the chunking process mechanical rather than creative, because the logical breaks are already built in.
The 'same style' toggle is a lifesaver for consistency, but it can't fix a disjointed script. If the visual or action cue between scenes feels forced, it's often because the underlying thought isn't a complete beat.
You're dead right about planning around the hard stop, and your chunking script is the pragmatic way to enforce it. The core principle of "script first, tool second" is one most people get backwards.
But there's a hidden risk in treating the scene chunk as a pure output of the script: you can end up with a technically correct split that kills the narrative flow. I've seen scripts where the last sentence of a chunk is a dependent clause or a weak transition because it just happened to be sentence number five. The "self-contained beat" idea is what saves you, but sometimes you need to override the algorithm and manually nudge a sentence to the next chunk, even if it leaves the previous one a sentence short. The math is a guide, not a law.
That 'same style' toggle is indeed the glue, but it's also a crutch for poor scene design. If you're leaning on it too hard to maintain coherence, it's a sign your beats aren't truly self-contained.
keep it simple
Love that approach. I've found setting a max_token count per chunk is even more reliable than just counting sentences. A five-sentence block with long, technical terms can easily blow past Fliki's limit.
One tweak I make: after the script splits everything, I do a manual pass to check those "beats." Sometimes the algorithm leaves a crucial transition sentence orphaned. Moving one line to the next chunk, even if it makes the first one shorter, keeps the story flow intact. The 'same style' toggle helps, but it can't fix a broken narrative jump.
Keep automating!
Yes, absolutely. The brutal edit is non-negotiable, but after that, the granular sentence work is where the polish happens.
I see this all the time with sales demo scripts. You can have a beautifully trimmed script that's still a single, powerful value proposition sentence. The AI will try to stretch that one idea across an entire 6-second scene, which just kills the pacing. Sometimes you need to split a sentence like "Our automation cuts manual entry by 80%, freeing your team to focus on high-value conversations" into two separate visual beats, even if it's technically one grammatical sentence. The first scene shows the problem (manual entry), the next shows the outcome (team having conversations).
It forces you to think visually, which ultimately makes the final video better anyway.
hannah
The regex in your chunking function is a great starting point. It misses one common pitfall, though - sentences ending with a single or double quote, like dialogue in testimonials. You can tweak the pattern to `r'(?<=[.!?]")?s+'` to capture those. Found that out the hard way when a chunk ended mid-quote.
Also, have you considered using `tiktoken` to estimate actual token counts instead of sentences? That's been more reliable for me with Fliki's specific limits, especially for dense technical scripts.
Data is the new oil - but it's usually crude.