Sesame vs OpenAI Whisper Transcription

Comparing the features of Sesame to OpenAI Whisper Transcription

Feature
Sesame
OpenAI Whisper Transcription

Capability Features

Audio Transcription
Audio Upload
Browser-Based Partitioning
Consistent Personality
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Dataset Size
1 million hours
Demo Mode
Emotional Intelligence
Evaluation Suite
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multiple Speaker Handling
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
One-Click Transcription
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
TextAudio
Training Epochs
5

Integration Features

GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
OpenAI Whisper Integration
Supported Audio Types
mp3mp4mpegmpgam4awavwebm

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Built-in Whisper
No Mention of Price Plans
No Pre-trained Language Model Use
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly
Requires OpenAI API Key

Pricing Features

Free Preview
Free Trial/Demo
Open Source
Apache 2.0