Whisper Medium is an automatic speech recognition (ASR) model developed by OpenAI, boasting 0.77 billion parameters. This model is particularly adept at transcribing audio content with high accuracy, making it a solid choice for tasks such as converting spoken language into text, creating subtitles, or transcribing meetings and lectures. Its performance is notable for its balance between accuracy and computational efficiency, which is crucial for local deployment where resources might be more limited.
In the context of its size class, Whisper Medium punches well above its weight. Despite having fewer parameters than larger models like Whisper Large, it maintains a high level of accuracy, often comparable to more resource-intensive models. This makes it an excellent option for users who need robust ASR capabilities without the overhead of extensive computational resources. The model’s efficiency is further enhanced by its available quantization (Q8_0), which reduces the memory footprint and improves inference speed, making it suitable for deployment on devices with as little as 1.9 GB of VRAM.
Whisper Medium is ideal for developers and professionals looking to integrate ASR into applications running on mid-range hardware, such as laptops or edge devices. It is particularly useful for projects that require real-time or near-real-time transcription, where latency and resource consumption are critical factors. Users with more powerful hardware will also benefit from its efficiency, allowing them to handle multiple streams or larger datasets simultaneously.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8 | 1.428 GB | 1.93 GB | 2.43 GB | 92% |
How to run Whisper Medium
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Pure-C reimplementation. CoreML/Metal/CUDA. 1-line setup.
whisper.cpp home →- 1
Build
git clone https://github.com/ggerganov/whisper.cpp && cd whisper.cpp && make - 2
Get the model
bash ./models/download-ggml-model.sh medium - 3
Transcribe
./main -m models/ggml-medium.bin -f input.wav
Community benchmarks
Real tokens/sec reports from people running Whisper Medium on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
how much VRAM do I need to run Whisper Medium?
Whisper Medium requires 1.93 GB VRAM minimum with Q8_0 quantization. For full precision you need 1.93 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Whisper Medium?
To run Whisper Medium, you need a GPU with at least 1.9 GB of VRAM. NVIDIA GPUs such as the GTX 1060 or higher are recommended for optimal performance.
Is Whisper Medium good for coding?
Whisper Medium is primarily designed for speech recognition and is not optimized for coding tasks. For coding, models like Codex or CodeLlama are more suitable.
Whisper Medium vs Llama 3.1 8B?
Whisper Medium has 0.77 billion parameters and is specialized for speech recognition, while Llama 3.1 8B has 8 billion parameters and is a general-purpose language model. Llama 3.1 8B is better for text generation but requires more resources.
Can I run Whisper Medium on a Mac?
Yes, you can run Whisper Medium on a Mac. Ensure your Mac has a compatible GPU with at least 1.9 GB of VRAM and the necessary drivers installed.
How much VRAM does Whisper Medium need?
Whisper Medium requires at least 1.9 GB of VRAM to run efficiently. This can vary slightly depending on the quantization level used.
Is Whisper Medium censored?
Whisper Medium is not censored. It is an open-source model released under the MIT license, allowing for unrestricted use and modification.
Is Whisper Medium commercial-use allowed?
Yes, Whisper Medium is licensed under the MIT license, which allows for commercial use without any restrictions.
Whisper Medium context length?
The context length for Whisper Medium is not explicitly defined, but it is designed to handle typical speech segments effectively. For longer audio, you may need to split the input into smaller chunks.
Does Whisper Medium support function calling?
Whisper Medium does not support function calling as it is primarily a speech recognition model. Function calling is more relevant for text-based models that can execute code or APIs.
Whisper Medium quantization options?
Whisper Medium supports various quantization options, including INT8 and FP16, which can reduce the model size and improve inference speed while maintaining acceptable accuracy.
Can Whisper Medium run on CPU?
Yes, Whisper Medium can run on CPU, but it will be significantly slower compared to running on a GPU. Expect longer inference times for real-time applications.
Whisper Medium fine-tuning?
Whisper Medium can be fine-tuned for specific speech recognition tasks using labeled data. Fine-tuning can improve accuracy for domain-specific audio, such as medical or legal transcriptions.
Whisper Medium system requirements?
Whisper Medium requires a minimum of 1.9 GB of VRAM, 8 GB of RAM, and a modern CPU. A GPU with at least 1.9 GB of VRAM is highly recommended for efficient performance.
Whisper Medium performance benchmark?
Whisper Medium can process audio at approximately 10-20 tokens per second on a mid-range GPU. Performance can vary based on the specific hardware and quantization level used.
Whisper Medium for RAG?
Whisper Medium is not designed for Retrieval-Augmented Generation (RAG). It is a speech recognition model and does not have the capability to retrieve and generate text from external sources.
Whisper Medium for agents?
Whisper Medium can be integrated into voice assistants or chatbots to handle speech-to-text conversion. However, it does not have built-in capabilities for generating responses or managing conversations.
Whisper Medium for coding vs general?
Whisper Medium is not optimized for coding tasks. It is designed for general speech recognition and is best suited for transcribing audio into text.
Whisper Medium vs ChatGPT?
Whisper Medium is a speech recognition model, while ChatGPT is a text-based conversational model. ChatGPT is better for generating human-like text and handling conversations, whereas Whisper Medium excels in transcribing spoken words.
Whisper Medium download size?
The download size for Whisper Medium is approximately 1.5 GB, depending on the quantization level and format.
Best quant for Whisper Medium?
The best quantization for Whisper Medium depends on your use case. INT8 provides a good balance between size reduction and performance, while FP16 offers a slight performance boost with minimal accuracy loss.