Scaling Speech Technology to 1,000+ Languages (MMS)

Unified ASR model trained on massively multilingual data covering 1,000+ languages and dialects using a shared encoder-decoder architecture with language-agnostic phonetic representations. The system uses a single model checkpoint rather than separate language-specific models, enabling efficient inference across the full language portfolio without model switching or language detection overhead.

Enables ASR for languages with minimal training data by leveraging acoustic and phonetic patterns learned from high-resource languages through a shared multilingual encoder. The architecture transfers phonetic knowledge across language boundaries, allowing the model to recognize speech in languages with <1 hour of training data by mapping their acoustic patterns to learned representations from related or typologically similar languages.

Automatically detects the language of input speech using acoustic and phonetic features learned during multilingual training. The model leverages the shared multilingual encoder to classify speech into one of 1,000+ supported languages, enabling automatic language routing without explicit user specification. Uses the learned language-specific acoustic patterns from the unified model to disambiguate between languages with high accuracy.

Produces frame-level phoneme alignments for input speech by leveraging the multilingual encoder's learned phonetic representations and attention mechanisms. The system maps acoustic frames to phoneme sequences, enabling precise temporal alignment of speech to text without language-specific alignment models. Uses the shared phonetic space learned across 1,000+ languages to perform alignment even for low-resource languages where dedicated alignment tools don't exist.

Processes audio in real-time streaming fashion with incremental transcription output, enabling low-latency speech-to-text for interactive voice applications. The system uses a streaming-compatible encoder-decoder architecture that processes audio chunks and produces partial transcriptions without waiting for complete utterances. Maintains state across audio chunks to enable contextual decoding while keeping per-chunk latency low for responsive user experiences.

Generates musical audio from text descriptions with fine-grained control over musical attributes including style, instrumentation, tempo, and mood. The system uses a conditional generative model (likely diffusion or autoregressive) that maps text descriptions to musical tokens or audio representations, with additional control tokens for specifying musical characteristics. Enables both unconditional generation from descriptions and conditional generation with explicit control over musical parameters.