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Whisper Ner V1
Overview :
Whisper-NER is an innovative model that allows for simultaneous speech transcription and entity recognition. This model supports open-type Named Entity Recognition (NER) and can identify a diverse and evolving set of entities. Whisper-NER is designed as a robust foundational model for automatic speech recognition (ASR) and NER downstream tasks and can be fine-tuned on specific datasets to enhance performance.
Target Users :
The target audience for Whisper-NER includes developers and data scientists, particularly those who need to process voice data and extract useful information. With its capability of joint speech transcription and entity recognition, it is perfectly suited for scenarios requiring automated processing of large volumes of voice data, such as voice assistants, voice analytics, and security monitoring.
Use Cases
Example 1: Using Whisper-NER to transcribe meeting recordings and identify companies and locations mentioned in the meeting.
Example 2: In a security monitoring system, using Whisper-NER to transcribe surveillance audio in real-time and recognize suspicious activities.
Example 3: In customer service, utilizing Whisper-NER to analyze voice recordings of customer feedback, automatically identifying the issues and needs mentioned by customers.
Features
- Joint audio transcription and named entity recognition: Whisper-NER can recognize entities while transcribing speech.
- Support for open-type NER: Able to identify and adapt to changing types of entities.
- Strong foundational model: Suitable for downstream tasks in automatic speech recognition and named entity recognition.
- Fine-tuning capability: Can be fine-tuned on specific datasets to improve model performance.
- Trained on the NuNER dataset: Ensures the model's performance on English data.
- Support for multiple entity labels: Users can specify multiple entity tags separated by commas.
- Efficient inference process: Provides detailed code examples to facilitate inference.
How to Use
1. Install the necessary libraries, such as torch and transformers.
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2. Load the pre-trained WhisperProcessor and WhisperForConditionalGeneration models from Hugging Face.
3. Prepare the audio files and load them into the model.
4. Set entity labels, such as 'person, company, location'.
5. Use the model to perform inference, generating token IDs.
6. Post-process the token IDs into text and remove the prompt.
7. Analyze the transcription results and the recognized entities to extract the required information.
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