Whisperner : Unified open-source named entity and speech recognition model

Whisperner

#Automatic Speech Recognition #Named Entity Recognition #Deep Learning #Open Source #Multilingual Support Standard Picks Open Source

Overview :

WhisperNER is a unified model that combines Automatic Speech Recognition (ASR) and Named Entity Recognition (NER), equipped with zero-shot capabilities. This model is designed as a robust foundational model for downstream tasks of ASR with NER and can be fine-tuned on specific datasets to enhance performance. The significance of WhisperNER lies in its ability to simultaneously handle speech recognition and entity recognition tasks, improving efficiency and accuracy, especially in multilingual and cross-domain scenarios.

Target Users :

The target audience includes developers, data scientists, and enterprises that need to handle large volumes of audio data and entity recognition tasks. WhisperNER's zero-shot capabilities and high accuracy make it particularly suited for rapidly deploying speech and entity recognition solutions, especially in scenarios with limited resources or where multiple languages need to be processed.

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Use Cases

Case Study 1: A multinational company uses WhisperNER to process multilingual meeting notes, achieving automated speech-to-text and key information extraction.

Case Study 2: A research institution utilizes WhisperNER for audio data preprocessing, providing accurate inputs for subsequent machine learning model training.

Case Study 3: Developers integrate WhisperNER into a mobile application to offer users real-time speech recognition and entity recommendation features.

Features

- Zero-shot capability: Recognize multiple languages and entities without training.

- Unified model: Combines ASR and NER to enhance processing efficiency.

- Fine-tuning capability: Can be fine-tuned on specific datasets for better performance.

- Multilingual support: Suitable for speech and entity recognition in various languages.

- High accuracy: Provides highly accurate recognition results based on advanced deep learning technologies.

- Easy to integrate: Offers code samples and APIs, making integration into developers' projects straightforward.

- Open-source: The code is open-source, allowing the community to collaborate on improvements and optimizations.

How to Use

1. Create and activate a virtual environment: Use conda or pip to install the required environment and dependencies.

2. Clone the repository: Use the git clone command to download WhisperNER's code locally.

3. Install dependencies: Use pip to install all necessary dependencies based on the provided requirements.txt file.

4. Load the model and processor: Utilize the WhisperProcessor and WhisperForConditionalGeneration from the transformers library to load the pre-trained model.

5. Audio preprocessing: Use the provided audio_preprocess function to prepare audio files for processing.

6. Run the model: Input the preprocessed audio into the model to generate token IDs.

7. Post-processing: Convert the generated token IDs into text, removing the prompt to obtain the final speech recognition and entity recognition results.