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How to Create Timestamps for 30,000 Word Subtitles

Complete guide to processing 30,000 words for subtitles. Learn chunking strategies, model configuration, and timestamp integration for accurate subtitle creation.

#subtitle-optimization#timestamp-automation#semantic-chunking#timestamp-splitting#subtitle-generation#long-context-processing#text-chunking
Question

How to organize timecode segmentation for a 30,000-word subtitle text? Problem: The model only processes 2-3 hours out of 4 hours of subtitles (30,000 words) without throwing errors, but appears to complete work prematurely. Attempts to split the text into chunks without context result in vague formulations, and using rewrites of already processed chunks didn’t provide significant improvement. How to solve the long context problem when processing large volumes of text for creating timecodes?

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Processing 30,000 Words (4 Hours of Subtitles): Multi-Level Chunking Strategy with Context Preservation and Timestamp Integration

To process 30,000 words (4 hours of subtitles), it’s necessary to use a multi-level chunking strategy with context preservation and timestamp integration. The optimal solution combines semantic chunking with overlap, segmentation by natural speech pauses, and the use of long-context models with proper parameter configuration.

Contents

Main Problems in Long Context Processing

When working with 30,000 words (approximately 4 hours of subtitles), several key problems arise that limit processing to 2-3 hours without explicit errors. The main challenges include:

Context Window Limitations of Models
Modern LLMs, even with extended context windows, can experience “attention decay” on long sequences. As noted by deepset.ai, models can physically process more tokens, but the quality of content understanding decreases when exceeding the optimal range.

Loss of Semantic Integrity
Splitting text into chunks without preserving context creates problems with broken semantic connections. Research from eNeuro shows that effective chunking during reading helps eliminate ambiguities and improves comprehension efficiency.

Timestamp Desynchronization
When processing large volumes of text, difficulties arise in maintaining precise correspondence between text and timestamps, which is critical for subtitles.

Chunking Strategies for 30,000 Words

For effective processing of 30,000 words, a combined approach to chunking is recommended:

Semantic Chunking with Overlap

Semantic chunking groups sentences or larger text blocks based on their semantic similarity. As explained by Sanjay Kumar PhD, this approach ensures that each chunk has coherent and contextually related content.

Optimal Parameters:

  • Chunk size: 150-300 tokens
  • Overlap: 20-30 tokens between chunks
  • Chunk boundaries: natural speech pauses, topic transitions

Multi-Level Hierarchical Structure

Divide the text into several levels:

  1. Main blocks (30-60 minutes): large thematic sections
  2. Sub-blocks (10-15 minutes): logically complete fragments
  3. Micro-chunks (2-3 minutes): for detailed processing and timestamp generation

Adaptive Boundary Determination

Use natural text boundaries for splitting:

  • Speech pauses
  • Topic changes
  • Question-answer pairs
  • Transition markers (“however”, “in conclusion”, “therefore”)

Integrating Timestamps into the Processing Workflow

For correct timestamp generation, integrate temporal information at all processing stages:

Pre-processing with Timecodes

python
def add_timecodes_to_chunks(text_segments, timecodes):
    """
    Adds timestamps to text segments
    """
    chunks_with_timecodes = []
    for segment, start_time, end_time in zip(text_segments, timecodes):
        chunks_with_timecodes.append({
            'text': segment,
            'start_time': start_time,
            'end_time': end_time,
            'duration': end_time - start_time
        })
    return chunks_with_timecodes

Context-Dependent Chunking

When splitting text, consider temporal parameters:

  • Minimum chunk duration: 2 seconds
  • Maximum chunk duration: 15 seconds
  • Optimal duration: 4-8 seconds

Adjustment Based on Speech Patterns

Analyze natural speech rhythms to determine optimal splitting points. As noted by Saudisoft Localization, timestamps in subtitle files determine the exact time of text appearance and disappearance.

Model Configuration for Long Context Work

Selecting a Model with Appropriate Context Window

For processing 30,000 words, choose models with a minimum context window of 32K-128K tokens:

  • GPT-4 Turbo (128K context)
  • Claude 3 (200K context)
  • Gemini Pro (128K context)

Optimizing Processing Parameters

Temperature and Top-p:

  • Temperature: 0.3-0.5 for timestamp accuracy
  • Top-p: 0.9 for balance between diversity and accuracy

Maximum Output Length:

  • Limit the model’s response length (e.g., 500-1000 tokens) to prevent generation of overly long subtitles

Using “Weak Continuation” Technique

For processing very long texts, use a technique where the model continues processing from the last processed token, not from the beginning:

python
def process_long_text_continuation(text, model, max_tokens=8000):
    """
    Processes long text using continuation technique
    """
    chunks = semantic_chunking_with_overlap(text, overlap_tokens=200)
    results = []
    
    for i, chunk in enumerate(chunks):
        if i == 0:
            # First chunk is processed completely
            prompt = f"Process the following text and generate timestamps:\n{chunk}"
        else:
            # Subsequent chunks include context from the previous one
            prev_context = chunks[i-1][-500:]  # Last 500 characters
            prompt = f"Continue processing the text. Previous context:\n{prev_context}\n\nNew fragment:\n{chunk}"
        
        result = model.generate(prompt, max_tokens=max_tokens)
        results.append(result)
    
    return results

Practical Implementation with Code Examples

Complete Example with Timestamp Generation

python
import re
from datetime import timedelta

def timecode_to_seconds(timecode):
    """Converts timecode (HH:MM:SS,ms) to seconds"""
    hours, minutes, seconds_ms = timecode.split(':')
    seconds, milliseconds = seconds_ms.split(',')
    return (int(hours) * 3600 + int(minutes) * 60 + 
            int(seconds) + int(milliseconds) / 1000)

def seconds_to_timecode(seconds):
    """Converts seconds to timecode (HH:MM:SS,ms)"""
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = seconds % 60
    return f"{hours:02d}:{minutes:02d}:{secs:06.3f}".replace('.', ',')

def process_large_subtitle_text(text_30000_words, model):
    """
    Main function for processing 30,000 words to generate timestamps
    """
    # Step 1: Pre-segmentation by natural pauses
    segments = natural_speech_segmentation(text_30000_words)
    
    # Step 2: Determine approximate durations
    word_count_per_segment = [len(seg.split()) for seg in segments]
    estimated_durations = [words * 0.5 for words in word_count_per_segment]  # ~0.5 sec per word
    
    # Step 3: Semantic chunking with overlap
    semantic_chunks = semantic_chunking_with_overlap(
        text_30000_words, 
        chunk_size=300, 
        overlap_tokens=60
    )
    
    # Step 4: Batch processing with context preservation
    all_timecodes = []
    current_time = 0
    
    for i, chunk in enumerate(semantic_chunks):
        # Add context from previous chunk
        if i > 0:
            context = semantic_chunks[i-1][-200:]
            enhanced_prompt = f"Context: {context}\n\nCurrent text: {chunk}"
        else:
            enhanced_prompt = chunk
        
        # Generate timestamps for chunk
        timecode_result = model.generate_timecodes(enhanced_prompt)
        
        # Convert and add to general list
        chunk_timecodes = [
            (seconds_to_timecode(current_time + offset), 
             seconds_to_timecode(current_time + offset + duration))
            for offset, duration in timecode_result
        ]
        
        all_timecodes.extend(chunk_timecodes)
        current_time += sum(duration for _, duration in timecode_result)
    
    return all_timecodes

def natural_speech_segmentation(text):
    """
    Segments text by natural speech pauses
    """
    # Patterns for natural pauses
    pause_patterns = [
        r'[.!?]\s+',  # Period, exclamation mark, question mark + space
        r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|\!)\s',  # More complex pattern
        r'\n\s*\n',  # Empty lines
        r'–\s+',  # Em dash
    ]
    
    segments = []
    current_segment = ""
    
    for sentence in re.split('|'.join(pause_patterns), text):
        if len(current_segment) + len(sentence) < 500:  # Maximum segment length
            current_segment += sentence + " "
        else:
            segments.append(current_segment.strip())
            current_segment = sentence + " "
    
    if current_segment:
        segments.append(current_segment.strip())
    
    return segments

Error Handling and Context Recovery

python
def robust_processing_with_recovery(text, model, max_retries=3):
    """
    Processing with automatic recovery on errors
    """
    try:
        # Attempt full processing
        result = process_large_subtitle_text(text, model)
        return result
    except Exception as e:
        if max_retries > 0:
            print(f"Error: {e}. Attempting recovery...")
            
            # Split into smaller chunks
            smaller_chunks = aggressive_chunking(text, chunk_size=150)
            
            results = []
            for chunk in smaller_chunks:
                try:
                    chunk_result = model.generate_timecodes(chunk)
                    results.extend(chunk_result)
                except Exception as chunk_error:
                    print(f"Chunk processing error: {chunk_error}")
                    continue
            
            # Merge results with timestamp preservation
            merged_results = merge_timecoded_results(results)
            return merged_results
        else:
            raise e

Performance and Quality Optimization

Balancing Quality and Speed

For optimal processing of 30,000 words, find a balance between:

  • Context depth: more context = quality, but slower
  • Chunk size: more chunks = faster, but lower quality
  • Overlap: more overlap = better context, but duplication

Caching and Context Memory

python
class ContextCache:
    def __init__(self, max_cache_size=5):
        self.context_cache = []
        self.max_cache_size = max_cache_size
    
    def add_context(self, context):
        if len(self.context_cache) >= self.max_cache_size:
            self.context_cache.pop(0)
        self.context_cache.append(context)
    
    def get_relevant_context(self, current_text):
        # Find most relevant content
        relevant_contexts = []
        for cached_context in self.context_cache:
            similarity = calculate_similarity(current_text, cached_context)
            if similarity > 0.7:  # Relevance threshold
                relevant_contexts.append(cached_context)
        return ' '.join(relevant_contexts[-2:])  # Last 2 relevant contexts

Post-processing and Correction

After main processing, perform timestamp correction:

  1. Eliminate overlaps: remove overlapping timestamps
  2. Smoothing: even distribution of durations
  3. Validation: check against subtitle standards

Tools for Automating the Process

Specialized Libraries for Chunking

LangChain Text Splitters

python
from langchain.text_splitter import RecursiveCharacterTextSplitter, SemanticChunker

# Recursive splitter with customizable parameters
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=300,
    chunk_overlap=60,
    length_function=len,
)

# Semantic chunking
semantic_splitter = SemanticChunker(
    embedding_model,
    chunk_size=300,
    breakpoint_threshold_type="percentile",
    breakpoint_threshold_amount=95,
)

Tools for Working with Subtitles

Subtitle Edit - free open-source tool for exporting subtitles in various formats, as noted in this Reddit discussion.

FFmpeg for audio processing and timestamp synchronization:

bash
ffmpeg -i input_audio.wav -acodec pcm_s16le -ar 16000 -ac 1 processed_audio.wav

Automation Pipelines

python
def create_subtitle_pipeline(input_text, output_file):
    """
    Complete pipeline for subtitle generation with timestamps
    """
    # Stage 1: Pre-processing
    cleaned_text = preprocess_text(input_text)
    
    # Stage 2: Segment determination
    segments = determine_speech_segments(cleaned_text)
    
    # Stage 3: Semantic chunking
    chunks = apply_semantic_chunking(segments)
    
    # Stage 4: Timestamp generation
    timecoded_segments = generate_timecodes(chunks)
    
    # Stage 5: Output formatting
    formatted_subtitles = format_srt(timecoded_segments)
    
    # Stage 6: Save result
    save_subtitle_file(formatted_subtitles, output_file)
    
    return output_file

Conclusion

For effective processing of 30,000 words (4 hours of subtitles), the following is recommended:

  1. Use a multi-level approach: combine semantic chunking with natural speech boundaries
  2. Optimize model parameters: select a model with an extended context window (128K+ tokens) and configure processing parameters
  3. Implement context overlap: add 20-30% overlap between chunks to preserve semantic integrity
  4. Automate the process: use specialized tools and pipelines for processing large text volumes
  5. Perform post-processing: correct timestamps and validate the result before final saving

These strategies will allow processing of full 4 hours of subtitles without quality loss and timestamp accuracy, solving the problem of premature model termination.

Sources

  1. Chunking Strategies for LLM Applications | Pinecone
  2. Optimizing Text Input for RAG Models: Chunking & Splitting Strategies
  3. Document Chunking for Effective Text Processing | Sanjay Kumar PhD
  4. How Do We Segment Text? Two-Stage Chunking Operation in Reading | eNeuro
  5. The Ultimate Guide to Subtitling - Saudisoft Localization
  6. Long-Context LLMs and RAG | deepset Blog
  7. Context Length in LLMs: What Is It and Why It Is Important?
  8. LLMs with largest context windows
  9. Convert SRT file to clean, presentable Text? | Reddit
  10. Subtitle timecodes: how to create, optimize or remove them?
How to choose the optimal chunk size for semantic chunking of subtitles?Which models with extended context windows are best suited for processing 4+ hours of subtitles?How to avoid loss of meaning when splitting long texts into chunks?How to integrate timestamps into the subtitle processing process using Python?How to optimize performance when processing 30,000+ words for subtitles?What automation tools exist for creating timestamps in subtitles?
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