Discover Insanely Fast Whisper, the groundbreaking AI transcription tool. Transcribe hours of audio in seconds, optimized for NVIDIA GPUs & Mac. Free on GitHub.
Sell Your Own AI Art Online
Create Your Own Content Agency
Create Your Own Ad Creative Agency
Provide AI SEO Consultancy
.avif)
.avif){kind=spacer}
Click here to start your free trial.
Transcribes 150 minutes of audio in less than 98 seconds using advanced AI technology.
User-friendly command-line interface for easy access to powerful transcription capabilities.
Provides high-quality transcriptions that are more reliable than many traditional methods.
Supports both transcription and translation tasks with multi-device compatibility.
PopularAiTools.ai
Click here to start your free trial.
Insanely Fast Whisper not only offers impressive transcriptions but also presents numerous opportunities for monetization. Organizations and developers can capitalize on its features by offering transcription services tailored to industries such as media, education, and healthcare.
Insanely Fast Whisper harnesses the power of OpenAI's Whisper through an effective command-line interface that delivers remarkably fast audio transcriptions. With features like GPU optimization, a plethora of CLI options, and the ability to use it without a CLI for those who prefer coding, it addresses both novice and advanced users' needs. While there are some hardware and configuration challenges, the benefits in speed and functionality outweigh these drawbacks. This project not only sets a new standard for transcription speed but opens doors to various commercial opportunities in a growing market.
Click here to start your free trial.
Insanely Fast Whisper is a project that provides a command-line interface (CLI) for transcribing audio files using OpenAI's Whisper, supported by Hugging Face Transformers, Optimum, and Flash Attention.
This tool can transcribe 150 minutes of audio in less than 98 seconds when utilizing the Whisper Large v3 model, making it exceptionally efficient.
The project has been optimized for the Nvidia A100 GPU to achieve maximum transcription speeds. It's essential to ensure that your system meets CUDA enabled requirements for optimal performance.
pipx install insanely-fast-whisperpipx install insanely-fast-whisper --force --pip-args="--ignore-requires-python"To run the inference from any path, use the command:
insanely-fast-whisper --file-name <filename or URL>If you face installation issues related to Flash Attention, ensure you install the CLI via pipx. For Windows users, the "Torch not compiled with CUDA enabled" error may require installing torch manually.
To manage Out-Of-Memory exceptions on Mac, it is advisable to reduce the batch size significantly to prevent system overload.
Yes, if you prefer not to utilize the CLI, the following code snippet can be used:
import torch
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3", device="cuda:0")
outputs = pipe("<FILE_NAME>")This project operates under the Apache-2.0 License, allowing for open-source usage and contributions.
Click here to start your free trial.
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
Click here to start your free trial.
Transcribes 150 minutes of audio in less than 98 seconds using advanced AI technology.
User-friendly command-line interface for easy access to powerful transcription capabilities.
Provides high-quality transcriptions that are more reliable than many traditional methods.
Supports both transcription and translation tasks with multi-device compatibility.
PopularAiTools.ai
Click here to start your free trial.
Insanely Fast Whisper not only offers impressive transcriptions but also presents numerous opportunities for monetization. Organizations and developers can capitalize on its features by offering transcription services tailored to industries such as media, education, and healthcare.
Insanely Fast Whisper harnesses the power of OpenAI's Whisper through an effective command-line interface that delivers remarkably fast audio transcriptions. With features like GPU optimization, a plethora of CLI options, and the ability to use it without a CLI for those who prefer coding, it addresses both novice and advanced users' needs. While there are some hardware and configuration challenges, the benefits in speed and functionality outweigh these drawbacks. This project not only sets a new standard for transcription speed but opens doors to various commercial opportunities in a growing market.
Click here to start your free trial.
Insanely Fast Whisper is a project that provides a command-line interface (CLI) for transcribing audio files using OpenAI's Whisper, supported by Hugging Face Transformers, Optimum, and Flash Attention.
This tool can transcribe 150 minutes of audio in less than 98 seconds when utilizing the Whisper Large v3 model, making it exceptionally efficient.
The project has been optimized for the Nvidia A100 GPU to achieve maximum transcription speeds. It's essential to ensure that your system meets CUDA enabled requirements for optimal performance.
pipx install insanely-fast-whisperpipx install insanely-fast-whisper --force --pip-args="--ignore-requires-python"To run the inference from any path, use the command:
insanely-fast-whisper --file-name <filename or URL>If you face installation issues related to Flash Attention, ensure you install the CLI via pipx. For Windows users, the "Torch not compiled with CUDA enabled" error may require installing torch manually.
To manage Out-Of-Memory exceptions on Mac, it is advisable to reduce the batch size significantly to prevent system overload.
Yes, if you prefer not to utilize the CLI, the following code snippet can be used:
import torch
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3", device="cuda:0")
outputs = pipe("<FILE_NAME>")This project operates under the Apache-2.0 License, allowing for open-source usage and contributions.
Click here to start your free trial.
