MOSS-Speech eliminates the intermediary. Traditional speech models typically rely on a text-based pipeline: transcribing audio into words, generating a text reply, and then synthesizing a new voice. This sequence introduces latency and discards essential nuances like tone, pauses, and inflection. MOSS-Speech bypasses this process entirely by modeling speech-to-speech directly—removing the text bottleneck and the need for transcripts.
Technically, MOSS-Speech integrates speech-specific layers into a pretrained large language model (LLM) backbone. To preserve the LLM’s existing intelligence, the developers employ a "frozen weights" strategy. By training only the newly added modal layers while leaving the original weights untouched, the model retains its extensive reasoning and knowledge base while learning to process audio waveforms instead of text tokens.
Key Distinctions
Clone the repository and enter the project directory:
git clone https://github.com/OpenMOSS/MOSS-Speech
cd MOSS-Speech
Install the required dependencies and initialize submodules:
pip install -r requirements.txt
git submodule update --init --recursive
You can start the Gradio interface with a single command:
python3 gradio_demo.py
The interface supports four distinct workflows to cover various use cases:
The default configuration sets the model as a helpful voice assistant tasked with answering user questions via audio.
The model is also accessible via API. You can further customize behavior through Gradio’s configuration settings to better integrate the tool into your specific workflow. Consult the repository for advanced documentation.
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