We introduce SpiRit-LM, a foundation multimodal language model that freely mixes text and speech. Our model is based on a 7B pretrained text language model that we extend to the speech modality by continuously training it on text and speech units. Speech and text sequences are concatenated as a single stream of tokens, and trained with a word-level interleaving method using a small automatically curated speech-text parallel corpus. SpiRit-LM comes in two versions: a Base version that uses speech phonetic units (HuBERT) and an Expressive version that models expressivity using pitch and style units in addition to the phonetic units. For both versions, the text is encoded with subword BPE tokens. The resulting model displays both the semantic abilities of text models and the expressive abilities of speech models. Additionally, we demonstrate that SpiRit-LM can learn new tasks in a few-shot fashion across modalities (i.e., ASR, TTS, Speech Classification). We make available model weights and inference code.1,2

Machine translation models sometimes lead to added toxicity: translated outputs may contain more toxic content that the original input. In this paper, we introduce MinTox, a novel pipeline to automatically identify and mitigate added toxicity at inference time, without further model training. MinTox leverages a multimodal (speech and text) toxicity classifier that can scale across languages.We demonstrate the capabilities of MinTox when applied to SEAMLESSM4T, a multi-modal and massively multilingual machine translation system. MinTox significantly reduces added toxicity: across all domains, modalities and language directions, 25% to95% of added toxicity is successfully filtered out, while preserving translation quality

Despite broad interest in modeling spoken dialogue agents, most approaches are inherently “half-duplex” – restricted to turn-based interaction with responses requiring explicit prompting by the user or implicit tracking of interruption or silence events. Human dialogue, by contrast, is “full-duplex” allowing for rich synchronicity in the form of quick and dynamic turn-taking, overlapping speech, and backchanneling. Technically, the challenge of achieving full-duplex dialogue with LLMs lies in modeling synchrony as pre-trained LLMs do not have a sense of “time”. To bridge this gap, we propose Synchronous LLMs for full-duplex spoken dialogue modeling. We design a novel mechanism to integrate time information into Llama3-8b so that they run synchronously with the real-world clock. We also introduce a training recipe that uses 212k hours of synthetic spoken dialogue data generated from text dialogue data to create a model that generates meaningful and natural spoken dialogue, with just 2k hours of real-world spoken dialogue data. Synchronous LLMs outperform state-of-the-art in dialogue meaningfulness while maintaining naturalness. Finally, we demonstrate the model’s ability to participate in full-duplex dialogue by simulating interaction between two agents trained on different datasets, while considering Internet-scale latencies of up to 240 ms.