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Speechgpt2
Overview :
SpeechGPT2 is an end-to-end speech dialogue language model developed by the School of Computer Science at Fudan University. It can perceive and express emotions while providing appropriate voice responses in various styles based on context and human instructions. The model uses ultra-low bitrate speech codec (750bps) to simulate semantic and acoustic information and is initialized via a Multi-Input Multi-Output Language Model (MIMO-LM). Currently, SpeechGPT2 is a turn-based dialogue system, with development underway for a full-duplex real-time version that has shown promising progress. Despite limitations in computational and data resources, SpeechGPT2 has room for improvement regarding noise robustness in speech understanding and stability in speech generation quality, with plans for future open-source technical reports, code, and model weights.
Target Users :
SpeechGPT2 is ideal for users requiring advanced natural language processing capabilities, such as developers, researchers, and businesses looking to enhance voice interaction experiences. It offers a more human-like and emotionally engaging dialogue, thereby improving user experience.
Use Cases
Developers can utilize SpeechGPT2 to create applications with natural voice interaction capabilities.
Researchers can use this model for studies in speech recognition and generation.
Businesses can integrate SpeechGPT2 to enhance the interactive quality of their customer service systems.
Features
Perceive and express emotions
Provide responses in various styles, such as rap, theater, robotic, humorous, and whispering
Utilize an ultra-low bitrate speech codec (750bps)
Employ Multi-Input Multi-Output Language Model (MIMO-LM)
Generate one second of speech requiring 25 autoregressive decoding steps
Pre-trained on over 100,000 hours of academic and field speech data
High-quality multi-turn dialogue speech data
How to Use
1. Visit the SpeechGPT2 GitHub page to access the technical report and code.
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2. Read the technical report to understand the model's architecture and functionality.
3. Download and install the necessary software dependencies to run the model.
4. Configure the model parameters and training data according to the documentation.
5. Run the model and conduct tests to observe its speech recognition and generation performance.
6. Adjust model parameters as needed to optimize performance.
7. Integrate the model into applications or research projects.
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