Seed-LiveInterpret 2.0: Real-time Speech Translation

• Seed-LiveInterpret 2.0 is an end-to-end speech-to-speech interpretation system that unifies recognition, translation, and synthesis while preserving speaker identity via integrated voice cloning.
• It leverages a duplex framework with multimodal pretraining and dual-stage reinforcement learning to reduce latency from ~10 seconds to approximately 3 seconds while enhancing translation accuracy.
• Extensive benchmarking on the RealSI dataset demonstrates superior performance over commercial systems, achieving over 70% correctness in challenging real-time scenarios.

Seed-LiveInterpret 2.0 is an end-to-end simultaneous speech-to-speech interpretation system designed to deliver high-fidelity, ultra-low-latency translation with integrated voice cloning. It addresses longstanding practical challenges in real-time interpretation, combining a novel duplex speech-to-speech understanding-generating framework with large-scale multimodal pretraining and a two-stage reinforcement learning paradigm. Comprehensive benchmarking demonstrates that Seed-LiveInterpret 2.0 reduces speech output latency from near 10 seconds to near-real-time (∼3 seconds), outperforms previous commercial SI systems in both translation accuracy and delay, and maintains speaker voice fidelity during cross-lingual translation (Cheng et al., 23 Jul 2025).

1. System Architecture and Duplex Framework

Seed-LiveInterpret 2.0 is fundamentally structured as a duplex end-to-end speech-to-speech interpretation system. The architecture unifies speech recognition, translation, and speech synthesis within a single, multimodal LLM. The base model, part of the Seed LLM family, is augmented by a pretrained audio encoder to process streaming audio. The system ingests incoming speech in multiple source languages, incrementally consumes audio chunks, generates segmented and contextualized text tokens, and synthesizes the translated speech output on-the-fly.

The output generation policy at any time step $t$ is expressed as:

$y_t \sim \pi_t(\cdot\;|\;\text{audio}_t, x_{<t})$

with the complete trajectory probability given by

$$\pi_t(y_{1:T} \mid \text{audio}) = \prod_{t=1}^{T} \prod_{n=1}^{N} \pi_t(y_t^n \mid y_t^{<n}, \text{audio}_t, x_{<t})$$

This formalism ensures streaming context is continuously integrated into both text and speech outputs, supporting simultaneous real-time interpretation.

The architecture supports multilingual live conversation involving multiple speakers. The duplex framework continually detects speaker turns, streams translations as text, and immediately generates cloned speech output. Speaker identity is preserved across translation, maintaining timbre and style for each speaker in the synthesized target-language output.

2. Challenges Addressed in Simultaneous Speech Interpretation

Seed-LiveInterpret 2.0 directly addresses major obstacles in the deployment of practical simultaneous interpretation:

• Transcription and translation quality: Overcomes the historic deficiencies in end-to-end ASR and NMT for SI.
• Low-latency, real-time speech synthesis: Capitalizes on the unified architecture to avoid the compounding delays and error propagation of cascaded approaches.
• Multi-speaker disambiguation: Employs streaming multi-modal attention and voice cloning to separate and consistently identify concurrent talkers.
• Mitigation of translated speech inflation: The output control mechanisms prevent over-generation artifacts in long-form discourses, improving coherence and usability.

The integrated use of duplex modeling and speaker-aware voice cloning enables close coupling between recognition, translation, and synthesis—all essential for robust real-time interpretations.

3. Performance Metrics and Empirical Evaluation

Evaluation was conducted on the RealSI dataset and standard sentence-level benchmarks. The primary metrics were:

• Translation Accuracy: Measured by Valid Information Proportion (VIP) for text and Speech Valid Information Proportion (SVIP) for speech. Human interpreters validated correctness, with Seed-LiveInterpret 2.0 exceeding 70% correctness in complex, realistic scenarios.
• Latency: Reported as Average Lagging (AL) and First Letter Appearance Lagging (FLAL). The average latency for cloned speech output was reduced from nearly 10 seconds (in previous commercial solutions) to approximately 3 seconds—a near 70% reduction.
• Comparative Performance: Outperformed state-of-the-art commercial SI systems (designated Commercial-B, Commercial-T, etc.) both in translation quality and response timing.

Metric	Commercial SI	Seed-LiveInterpret 2.0
Avg. Latency	~10 s	~3 s
VIP/SVIP	<70%	>70%

This balance of high translation fidelity with near-real-time response significantly enhances system deployability in continuous, interactive scenarios.

4. Integrated Voice Cloning

A key innovation of Seed-LiveInterpret 2.0 is seamless voice cloning: speaker voices are preserved and transferred across languages during translation. Unlike conventional SI approaches using generic text-to-speech models, this system synthesizes translated speech that retains individual speaker timbre and prosody, increasing naturalness and trust in usage settings such as live events or customer support.

The technical pipeline ensures that speaker identity vectors, extracted during source speech encoding, are directly used as conditioning in the acoustic synthesis stage, producing output that matches both the message and the voice attributes of the original utterance.

5. Pretraining and Reinforcement Learning Strategies

Model performance is underpinned by extensive pretraining and a purpose-designed reinforcement learning regime:

• Pretraining: The model is pretrained on nearly 100 billion tokens encompassing a diversity of audio-to-text, text-to-audio, and text-only tasks. This large and heterogeneous corpus facilitates robust alignment across modalities and supports high-fidelity cross-lingual speech understanding and generation.
• Two-stage Reinforcement Learning:
  • Stage 1 ("warming up"): The system is trained with single-turn rewards to encode human priors (e.g., immediate correctness, latency, format).
  • Stage 2: Multi-turn, sequence-level rewards are optimized, capturing both intra-segment consistency (for incremental comprehension and translation) and inter-segment coherence (to maintain global discourse quality).
  • The overall RL objective integrates translation accuracy, latency, and formatting, optimized via Proximal Policy Optimization (PPO) with adaptive KL divergence regularization.

This composite objective enables precise balance between translation quality and output delay under realistic SI constraints.

6. Human Interpreter Validation and Practical Usability

Extensive human interpreter studies validated Seed-LiveInterpret 2.0. Results confirm that, under realistic and complex simultaneous interpretation scenarios, over 70% correctness is achieved for both text and speech outputs. This level of accuracy, combined with large reductions in average speech output latency, brings system performance near the threshold of practical "human-level" SI for critical domains.

The reduction in average latency (from ~10 s to ~3 s) dramatically improves usability for live applications, enabling smoother conversational exchange and natural turn-taking, critical for international conferences, live media, and customer-facing deployments.

7. Broader Impact and Implications

Seed-LiveInterpret 2.0 constitutes a comprehensive advance in simultaneous speech translation technology by unifying speech understanding, multilingual generation, and personalized cloned synthesis in a single, real-time framework. Its methodological contributions—duplex integration, multi-turn RL, large-scale multimodal pretraining, and integrated voice cloning—set a new benchmark for end-to-end SI systems.

The practical implications include more natural, immediate, and interpretable live translation services; expanded accessibility for global events; and a technical foundation extensible to other real-time multilingual communication or conversational AI tasks where low latency, speaker identification, and fidelity are paramount.