FCZP vs Sesame

Comparing the features of FCZP to Sesame

Feature
FCZP
Sesame

Capability Features

Consistent Personality
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Dataset Size
1 million hours
Diverse Content Sources
News sitesBlogs
Dynamic Episodes
Emotional Intelligence
Evaluation Suite
Interactive Episodes
Interest-Based Content
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multi-language Support
Multiple Speaker Handling
No Robotic Voices
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
Partial Multilingual Support Planned
Planned for 20+ languages
Personalisation Engine
Personalised Recommendations
Personalized Podcast Channel
Podcast Generation
Pronunciation Correction
Realtime Content
Sequence Length
2048
Single-Stage Model
Subjective Metrics
Comparative Mean Opinion Score
Text and Audio Input
TextAudio
Training Epochs
5
User-Friendly Controls

Integration Features

GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
iOS Exclusive
Memory Bottleneck in Training
No File Format Export
No Mention of Android Support
No Mention of Third-Party Integrations
No Pre-trained Language Model Use
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly

Pricing Features

Free Download
Free Preview
Monthly Subscription
Open Source
Apache 2.0