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A.V. Mapping
Compared to Sesame
A.V. Mapping vs Sesame
Comparing the features of A.V. Mapping to Sesame
Feature
A.V. Mapping
Sesame
Capability Features
Accelerated Cash Flow
AI Video Music Mapping
Consistent Personality
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Dataset Size
1 million hours
Efficiency for Video Industry
Emotional Intelligence
Evaluation Suite
Focus on Creators
Model Sizes
Tiny: 1B backbone, 100M decoder
Small: 3B backbone, 250M decoder
Medium: 8B backbone, 300M decoder
Multiple Speaker Handling
Music Licensing
Objective Metrics
Word Error Rate
Speaker Similarity
Homograph Disambiguation
Pronunciation Consistency
Partial Multilingual Support Planned
Planned for 20+ languages
Pronunciation Correction
Sequence Length
2048
Single-Stage Model
Subjective Metrics
Comparative Mean Opinion Score
Text and Audio Input
Text
Audio
Training Epochs
5
Integration Features
GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
Limitation Features
Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Free Tier Mentioned
No Pre-trained Language Model Use
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly
Other Features
Supported Language: Chinese
Chinese
Pricing Features
Free Preview
Open Source
Apache 2.0
Pricing Plans