Mitsuko

Mitsuko - AI-powered subtitle translation and transcription

Launched on May 21, 2025

Mitsuko is an advanced AI tool specializing in subtitle translation and audio transcription. It leverages cutting-edge AI models including Gemini, Claude, Grok and OpenAI's GPT to deliver superior translation quality. The platform offers three core functionalities: context-aware subtitle translation, precise audio-to-text transcription, and cross-episode context extraction. Mitsuko outperforms conventional machine translation in contextual understanding, cultural adaptation and tonal alignment. Users benefit from flexible credit-based pricing with detailed cost breakdowns for different AI models. The service supports multiple subtitle formats and provides customizable instructions for optimized results.

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Product Overview

Mitsuko is an AI-powered solution designed for professional-grade subtitle translation and audio transcription. The platform combines multiple state-of-the-art AI models to deliver:

  • Subtitle Translation: Supports SRT and ASS formats using Gemini, Claude, Grok and OpenAI models
  • Audio Transcription: Generates perfectly timed subtitles from audio files with custom instructions
  • Context Extraction: Maintains consistency across episodes through structured context documents

Key Advantages:

  • Context Awareness: Prioritizes meaning over literal translation
  • Cultural Adaptation: Handles idioms and cultural references effectively
  • Tonal Alignment: Matches character speech patterns consistently

Product Features

Subtitle Translation

  • Contextual Understanding: Analyzes scene context for accurate intent
  • Speech Pattern Matching: Aligns translations with character voices
  • Cultural Localization: Adapts idioms and cultural references
  • Custom Instructions: Allows user guidance for specific requirements

Audio Transcription

  • Precision Timing: Generates frame-accurate subtitle synchronization
  • Intelligent Segmentation: Divides content by sentences and clauses
  • Pre-processing Instructions: Accepts custom directives before transcription

Context Management

  • Multi-source Extraction: Gathers context from subtitles, audio or text
  • Structured Documentation: Creates organized context references
  • Cross-episode Consistency: Maintains unified terminology and style

Technical Architecture

The processing workflow follows this sequence:

Pricing Structure

Mitsuko operates on a transparent credit system with detailed model-specific costs:

AI Model Input Token Cost Output Token Cost Context Window Max Completion
DeepSeek R1 0.607 2.41 128k 128k
Gemini 2.5 Pro 1.5 12 1M 66k
Claude 3.7 Sonnet 3.6 18 200k 64k
GPT-4o 3 12 128k 16k

Audio Transcription Rates:

  • Free Tier: 100MB file limit
  • Premium (≤100min): 2760 credits/minute
  • Premium (>100min): 5520 credits/minute

Mitsuko pricing page screenshot

Usage Guide

  1. File Upload: Submit subtitle or audio files
  2. Model Selection: Choose preferred AI engine
  3. Custom Instructions (Optional): Provide specific guidance
  4. Processing: Automatic translation/transcription
  5. Result Download: Receive processed files

Competitive Advantages

  • Superior Quality: Outperforms conventional machine translation
  • Context Retention: Maintains narrative continuity
  • Flexible Pricing: Pay-as-you-go credit system
  • Model Variety: Multiple AI engine options

Limitations

  • Credit Costs: Premium models require significant credits
  • Learning Curve: Advanced features need familiarization

Case Study

Original Subtitle (Angry character context):

もう我慢できない!

Mitsuko Translation (Context-aware):

I've had enough of this!

Generic Translation (Literal):

I cannot endure anymore!

Support Services

  • Community Support: Join Discord community
  • Developer Resources: Access GitHub repository
  • Customer Service: Contact via website

Frequently Asked Questions

Supports SRT and ASS formats. Use custom instructions for specific requirements. Based on input/output tokens with model-specific rates. Delivers frame-accurate subtitle synchronization. Yes, depending on selected model capabilities. Gathers context from multiple media sources. Yes, with file size limitations.
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