What is Soundwave?

Soundwave is an open-source speech understanding model developed by The Chinese University of Hong Kong (Shenzhen). It focuses on the intelligent alignment and comprehension of speech and text, leveraging innovative alignment and compression adapter technologies to bridge the representation gap between speech and text. This enables efficient speech feature compression and enhanced performance in various speech-related tasks.

Key Features of Soundwave

• Speech and Text Alignment: Soundwave can precisely align speech signals with text. By designing alignment adapters and compression adapters, it converts audio sequences into a representation space that the model can understand, while dynamically compressing the length of the speech sequence to match the text.
• Speech Translation: The model excels in speech translation tasks, converting speech input in one language into text or speech output in another language. It has efficient alignment capabilities and strong language understanding.
• Speech Q&A: Soundwave supports speech Q&A functionality. Users can ask questions via speech, and the model can understand the questions and respond in speech or text.
• Speech Emotion Recognition: Soundwave can recognize emotional information in speech by analyzing features such as tone, speed, and intensity, determining the speaker's emotional state (e.g., happiness, sadness, anger).
• Multimodal Interaction: The model also supports multimodal interaction, combining speech, text, and other input forms to provide a richer interactive experience.

Technical Principles of Soundwave

• Speech and Text Alignment: Alignment is achieved through the design of an Alignment Adapter and the use of CTC loss. The Alignment Adapter includes a linear layer and a single-layer Transformer Encoder, converting audio sequences into a representation space that the model can understand, ensuring that speech and text can interact within the same representation space.
• Speech Feature Compression: At this stage, the model dynamically compresses the length of the speech sequence to match the text using a Shrinking Adapter. First, semantic features are selected based on CTC-predicted peaks, then auxiliary information (e.g., paralinguistic information) is queried and collected from the original sequence, and finally, these two types of features are fused to achieve sequence length reduction.
• Supervised Fine-Tuning: During the fine-tuning phase, the model only adjusts LoRA parameters, improving task processing capabilities based on text and speech instruction data. By learning various Q&A formats, speech tasks, and instruction formats, the model enhances its ability to follow instructions and understand speech.

Project Links for Soundwave

• GitHub Repository: https://github.com/FreedomIntelligence/Soundwave
• HuggingFace Model Library: https://huggingface.co/FreedomIntelligence/Soundwave
• arXiv Technical Paper: https://arxiv.org/pdf/2502.12900

Application Scenarios of Soundwave

• Smart Voice Assistants: Soundwave can be integrated into smart voice assistants (e.g., smart home devices, smart speakers) to provide a more natural and accurate voice interaction experience. Users can query information, control devices, set reminders, etc., via voice commands.
• Speech Translation: Soundwave is useful for cross-border meetings, travel, online education, etc., helping users overcome language barriers and achieve seamless communication.
• Language Learning Assistance: Through speech translation and Q&A features, Soundwave can help students practice foreign language pronunciation, understand grammar structures, and improve language learning effectiveness.
• Content Creation: Soundwave can be used in content creation, such as automatically generating video subtitles, audio scripts, etc.
• Medical Transcription: Doctors can record medical records via speech, and Soundwave can convert them into accurate text records, saving time and improving efficiency.