Meta's Spirit LM model synthesizes speech and text in a single stream, overcoming limitations of previous pipelines that kept speech and text separate.
By interleaving text and speech tokens for training, Spirit LM combines the semantic strengths of text-based LLMs with the expressive potential of speech models.
Current performance of Spirit LM in text-only tasks slightly lags behind that of Llama 2, indicating room for further improvements.
Traditional systems require transcribing speech to text for LLMs, limiting expressive capabilities; Spirit LM integrates both modalities for richer generation.
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