Trinity Audio vs Sesame

Comparing the features of Trinity Audio to Sesame

Feature
Trinity Audio
Sesame

Capability Features

AI-Generated Audio
Audio Distribution
Audio Experience Amplification
Audio Experience Customization
Audio Monetization
Consistent Personality
Content-to-Audio Conversion
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Dataset Size
1 million hours
Emotional Intelligence
Engaging Audio Journeys
Evaluation Suite
Increased Engagement
95% increase in visits/users
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multi-Stage Audio Workflow
CreationDistributionMonetization
Multiple Products
Trinity PlayTrinity MixTrinity ConductTrinity PlayerTrinity PulseTrinity Octopus
Multiple Speaker Handling
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
Partial Multilingual Support Planned
Planned for 20+ languages
Playlist Creation
Pronunciation Correction
Quick Conversion Time
Within minutes
Scalable Platform
RobustScalableSeamlessSimple
Sequence Length
2048
Single-Stage Model
Subjective Metrics
Comparative Mean Opinion Score
Text and Audio Input
TextAudio
Training Epochs
5

Integration Features

GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
Webflow Plugin Integration

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Explicit CMS Integrations
No Explicit Quotas
No File Format Details
No Mention of API
No Pre-trained Language Model Use
No Pricing Information
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly

Pricing Features

Free Preview
Open Source
Apache 2.0