Seamless: Multilingual Expressive and Streaming Speech Translation (2312.05187v1)

Abstract: Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion. First, we contribute an improved version of the massively multilingual and multimodal SeamlessM4T model-SeamlessM4T v2. This newer model, incorporating an updated UnitY2 framework, was trained on more low-resource language data. SeamlessM4T v2 provides the foundation on which our next two models are initiated. SeamlessExpressive enables translation that preserves vocal styles and prosody. Compared to previous efforts in expressive speech research, our work addresses certain underexplored aspects of prosody, such as speech rate and pauses, while also preserving the style of one's voice. As for SeamlessStreaming, our model leverages the Efficient Monotonic Multihead Attention mechanism to generate low-latency target translations without waiting for complete source utterances. As the first of its kind, SeamlessStreaming enables simultaneous speech-to-speech/text translation for multiple source and target languages. To ensure that our models can be used safely and responsibly, we implemented the first known red-teaming effort for multimodal machine translation, a system for the detection and mitigation of added toxicity, a systematic evaluation of gender bias, and an inaudible localized watermarking mechanism designed to dampen the impact of deepfakes. Consequently, we bring major components from SeamlessExpressive and SeamlessStreaming together to form Seamless, the first publicly available system that unlocks expressive cross-lingual communication in real-time. The contributions to this work are publicly released and accessible at https://github.com/facebookresearch/seamless_communication

Overview of "Seamless: Multilingual Expressive and Streaming Speech Translation"

The paper presents a notable contribution to the field of automatic speech translation with the introduction of a suite of models under the Seamless umbrella. These models collectively advance the capabilities of speech translation systems by integrating multilingual, expressive, and streaming translation functionalities. The authors aim to enhance machine-mediated communication, making it more akin to human interaction.

Key Contributions

1. SeamlessM4T v2: The foundation model, SeamlessM4T v2, improves upon its predecessor by expanding language support, increasing low-resource language representation, and utilizing advanced frameworks for better efficiency and accuracy. It handles speech and text input/output across multiple languages.
2. SeamlessExpressive: This model aims to preserve vocal styles and prosody during translation, addressing nuanced aspects of speech such as rhythm and pauses. The integration of prosody-aware models and expressive unit-to-speech generation allows this model to maintain expressivity when translating speech.
3. SeamlessStreaming: Utilizing the EMMA mechanism, this model provides low-latency speech translations, enabling real-time translation without waiting for complete utterance input. It is notable for its simultaneous transcription and translation capabilities across numerous languages.
4. Unified Seamless Model: By merging SeamlessExpressive and SeamlessStreaming, the authors offer a groundbreaking system that achieves expressive cross-lingual communication in real time.

Methodological and Technical Insights

• Data and Training: Leveraging a substantial amount of automatically aligned data and pseudo-labeled datasets has been crucial in training these models. The data encompasses a broad range of languages and modalities, ensuring robust model performance.
• Model Architecture: The Seamless models incorporate a variety of advanced architectural components, such as EMMA for simultaneous translation and a non-autoregressive T2U decoder for speedy and efficient unit generation.
• Evaluation Techniques: A combination of novel metrics and traditional methods were used to evaluate translation quality, latency, expressivity, and robustness. Automatic metrics were complemented by human evaluations to ensure thorough assessment.

Results and Implications

The results demonstrate significant improvements in quality and latency over previous systems and competing models. SeamlessM4T v2 achieves state-of-the-art performance in multilingual translation tasks, while SeamlessExpressive and SeamlessStreaming extend these capabilities to expressive and real-time use cases.

Practical and Theoretical Impact

• Practical Applications: These models pave the way for applications in real-time communication platforms, augmented reality, and live broadcasting, among others, offering seamless cross-lingual experiences.
• Theoretical Advancements: This work pushes the boundary of what's possible in speech translation, encouraging further exploration into expressive and low-latency translation techniques.

Future Research Directions

The paper suggests several avenues for future work, including extending language coverage, incorporating more nuanced linguistic features, and further improving translation reliability and safety. Additionally, exploring ethical considerations and potential biases inherent in machine translation remains a priority.

In conclusion, the Seamless models mark a significant stride toward realizing a Universal Speech Translator, transforming a long-held science fiction concept into a tangible technology. This advancement holds the promise of making global communication more inclusive and fluid, bridging linguistic barriers in unprecedented ways.