Transcribe audio or YouTube videos
Scrape and summarize web content
Create and run Jupyter notebooks interactively
Generate detailed script for podcast or lecture from text input
A prompts generater
Testing Novasky-AI-T1
Forecast sales with a CSV file
Answer questions about videos using text
Generate test cases from a QA user story
Generate text based on your input
Login and Edit Projects with Croissant Editor
Generate stories and hear them narrated
Generate text bubbles from your input
Whisper Large V3 is an advanced AI model developed by OpenAI, specifically designed for audio and speech transcription. It is an enhanced version of the Whisper series, offering improved accuracy and capabilities. This model excels at transcribing audio files as well as YouTube videos, making it a versatile tool for converting spoken content into text.
• High Accuracy: Whisper Large V3 provides highly accurate transcriptions, even with challenging audio conditions or diverse accents. • Multi-Language Support: It supports transcription in multiple languages, making it a global solution for audio-to-text needs. • Real-Time Processing: The model is optimized for real-time transcription, ensuring efficient and fast results. • Long Audio Support: It can handle long audio files or videos, ensuring comprehensive transcriptions without interruption. • Speaker Identification: Whisper Large V3 can identify and label different speakers in the audio, adding context to transcriptions. • Custom Vocabulary Support: Users can integrate custom vocabulary to improve accuracy for specific terms or names. • Integration Capabilities: Easily integrates with other AI tools and workflows for seamless transcription and analysis.
Install the Model: Access Whisper Large V3 through OpenAI's API or via Hugging Face libraries.
pip install git+https://huggingface.co/OpenAI/whisper.git
Import the Model: Use Python to import the model.
from whisper import Whisper, load_audio, transcribe
model = Whisper.load_model("large")
Load Audio File: Load your audio file or YouTube video URL.
audio = load_audio("input.mp3") # For local files
# OR
# audio = load_audio("https://www.youtube.com/watch?v=/example") # For YouTube URLs
Transcribe Audio: Use the model to transcribe the audio.
transcript = model.transcribe(audio)
print(transcript["text"])
Optional: For YouTube videos, ensure you have the necessary libraries installed (e.g., pydub) to handle audio extraction.
1. What file formats does Whisper Large V3 support?
Whisper Large V3 supports most common audio formats, including MP3, WAV, M4A, and FLAC. For YouTube videos, it processes the audio directly from the URL.
2. How long does transcription take?
The transcription speed depends on the length of the audio and internet connectivity. Whisper Large V3 is optimized for real-time processing, making it faster than previous versions.
3. Can I use Whisper Large V3 offline?
No, Whisper Large V3 requires an active internet connection to process transcriptions, as it runs on OpenAI's servers.
4. Is there a limit to the audio file size or duration?
While Whisper Large V3 can handle long audio files, extremely large files may need to be split into smaller segments. The model is designed to process up to 30 minutes of audio at a time.