Online Demo

Translates spoken input across 100+ language pairs while preserving speaker emotion, prosody, and vocal characteristics through a unified encoder-decoder architecture trained on multilingual speech data. The system uses a single model that handles both speech recognition and synthesis end-to-end, maintaining emotional nuance by learning disentangled representations of content and speaker identity during training.

Recognizes speech in 100+ languages using a single unified model trained with multilingual data, leveraging cross-lingual acoustic and linguistic patterns to improve accuracy even for low-resource languages. The architecture uses shared encoder layers that learn language-agnostic phonetic representations, with language-specific decoder heads that adapt to phoneme inventories and prosodic patterns of each language.

Converts text input into natural-sounding speech across 100+ languages with fine-grained control over speaker characteristics including voice timbre, pitch, speaking rate, and emotional tone. The system uses a neural vocoder architecture that conditions on speaker embeddings and linguistic features, allowing synthesis of diverse voices without requiring speaker-specific training data through speaker embedding interpolation.

Processes audio input in streaming chunks to produce translated speech output with minimal latency (typically 1-3 seconds behind live speech), using a streaming-aware encoder-decoder architecture that processes partial audio frames and generates incremental translations. The system buffers audio strategically to balance latency against translation quality, using attention mechanisms that can operate on incomplete input sequences.

Automatically detects the source language of input speech without explicit language specification, using a language identification classifier trained on acoustic patterns across 100+ languages. The system operates as a preprocessing step that feeds detected language codes into downstream ASR and translation models, enabling fully automatic speech translation without user intervention.

Processes multiple audio files or long-form audio content through the complete speech-to-speech translation pipeline (ASR → translation → TTS) with optimized throughput and resource utilization. The system queues audio files, processes them through shared model instances, and outputs translated audio with metadata tracking, enabling efficient processing of large volumes without per-file model loading overhead.