Jellypod vs Sesame

Comparing the features of Jellypod to Sesame

Feature
Jellypod
Sesame

Capability Features

AI Voice Library
Consistent Personality
Context Augmentation
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Customizable AI Hosts
Dataset Size
1 million hours
Embeddable Player
Emotional Intelligence
Evaluation Suite
Global Distribution Focus
Human-in-the-Loop Labeling
Intro/Outro Music Support
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multilingual Content Translation
Multiple Speaker Handling
Number of Hosts per Episode
4
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
One-Click Publishing
Outline Guided Creation
Partial Multilingual Support Planned
Planned for 20+ languages
Podcast Website Hosting
Pronunciation Correction
Script Editing
Sequence Length
2048
Single-Stage Model
Subjective Metrics
Comparative Mean Opinion Score
Supported Language List
EnglishSpanishFrenchHindiPortugueseChineseGermanJapaneseArabicRussianKoreanIndonesianItalianDutchTurkishPolishSwedishFilipinoMalayRomanianUkrainianGreekCzechDanishFinnishBulgarianCroatianSlovakTamil
Supported Source Quantity
70+
Supported Source Types
WebsitesBlogsPDFsPowerPointsOther FilesSpreadsheetsYouTube Videos
Text and Audio Input
TextAudio
Text-Based Audio Editing
Training Epochs
5
User Content Ownership
Users own the content they create
Voice Cloning

Integration Features

API Availability
Apple Podcasts Integration
GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
RSS Feed Integration
Source Upload Methods
Paste URLUpload FileDeep Search
Spotify Distribution
YouTube Publishing

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Pre-trained Language Model Use
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly
Video Generation

Pricing Features

Free Preview
Free Tier
Open Source
Apache 2.0