BetterWhisperX
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Betterwhisperx
Overview :
BetterWhisperX is an improved automatic speech recognition model based on WhisperX, offering fast speech-to-text services with word-level timestamps and speaker identification features. This tool is vital for researchers and developers handling large volumes of audio data, as it significantly enhances the efficiency and accuracy of speech data processing. The product is built on OpenAI's Whisper model with further optimizations and improvements. Currently, the project is free and open-source, aiming to provide the developer community with more efficient and accurate speech recognition tools.
Target Users :
The target audience includes developers, researchers, and businesses in need of voice recognition and audio analysis. BetterWhisperX is particularly suited for detailed audio content analysis scenarios, such as meeting transcriptions, lecture content transcription, and multilingual audio content analysis, due to its word-level timestamps and speaker identification features.
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Top Region: US(19.34%)
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Use Cases
Example 1: Researchers use BetterWhisperX to transcribe audio from a scientific lecture and generate a timestamped subtitle file.
Example 2: Business users transcribe meeting recordings in real-time using BetterWhisperX, quickly locating key discussion points through word-level timestamps.
Example 3: Multilingual content creators utilize BetterWhisperX to transcribe and analyze audio content in different languages to improve content production efficiency.
Features
- Batch inference support, achieving 70 times real-time transcription speed.
- Accurate word-level timestamps using wav2vec2 alignment.
- Supports multi-speaker identification through speaker binarization for audio stream segmentation.
- Voice activity detection (VAD) pre-processing to reduce hallucinations and assist batch processing with zero word error rate degradation.
- ASR models for multiple languages, automatically selecting suitable phoneme models for alignment.
- Capable of running on CPU, suitable for Mac OS X systems.
- Provides a Python interface for easy integration into other projects.
How to Use
1. Create a Python 3.10 environment: Set up and activate a new virtual environment using mamba.
2. Install CUDA and cuDNN: Install the appropriate versions of CUDA and cuDNN based on system requirements.
3. Install BetterWhisperX: Use pip to install the BetterWhisperX model.
4. Run sample audio: Utilize the whisperx command line tool to transcribe sample audio files.
5. Adjust model parameters: Modify ASR model, alignment model, batch size, and other parameters as needed.
6. Multilingual support: Specify language codes and choose the appropriate models for transcription.
7. Integrate into projects: Use the Python interface to incorporate BetterWhisperX into other projects.
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