PhanithLIM/fast-whisper-tiny

Whisper Model in CTranslate2

The Whisper Model in CTranslate2 is an optimized, high-performance implementation of OpenAI's Whisper automatic speech recognition (ASR) system, designed for efficient inference in production environments. This model is built on top of CTranslate2, an inference engine that offers fast, memory-efficient execution of transformer-based models.

Whisper is a state-of-the-art multilingual ASR model that can transcribe speech into text across a variety of languages and dialects. It also supports multilingual speech-to-text conversion, robust noise resilience, and the ability to handle various acoustic conditions, making it versatile for diverse applications, such as transcription services, voice assistants, and automated captioning systems.

CTranslate2 optimizes Whisper for:

• Low-latency Inference: Faster response times, ideal for real-time applications.
• Memory Efficiency: Optimized for low-memory environments, making it suitable for mobile and embedded devices.
• Multi-language Support: Handles multiple languages, dialects, and accents with high accuracy.

Performance

In a test on a CPU, a 3-minute long audio clip was transcribed by the Whisper model in CTranslate2 in 50 seconds, showcasing impressive transcription speed and efficiency for real-time or batch processing tasks.

Whether you're developing a real-time transcription tool or need efficient batch processing for large-scale transcription tasks, the Whisper model in CTranslate2 provides an optimized, powerful solution.