El LiquidCodec de código abierto de Liquid AI permite una conversión eficiente y en tiempo real de voz a texto y texto a voz al convertir el audio en tokens predecibles para modelos de lenguaje.
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Liquid AI has released LiquidCodec, the audio codec behind its LFM3-Audio-2B and LFM3-Omni-2B models. The LiquidCodec audio codec is a ~74M-parameter streaming speech tokenizer. It turns speech into tokens a language model can predict, then converts those tokens back into audio. Liquid AI had 2 goals: easy-to-predict tokens and fast conversion back to speech. The weights are on Hugging Face. TL;DR Size: ~74M parameters (73.6M on the model card); 24 kHz audio, 12.5 Hz frames, 12 codebooks. Runs on: a single CPU thread (tested on an Apple M4 Max laptop, FP32); CUDA GPUs via Transformers; Q4_0, Q4_K_M and Q8_0 GGUF builds for llama.cpp. Performance: best ViSQOL and lowest mel distance on all 8 test sets versus X-Codec 2, NanoCodec and Mimi. Best: ~361x real-time encoding on 1 CPU thread (encode RTF 0.0028 vs Mimi’s 0.0361). What is LiquidCodec? LiquidCodec is a Liquid AI speech codec that compresses 24 kHz audio into 12 token streams. Each 80 ms frame becomes 12 discrete tokens, per the LFM3-Audio-2B model card. It replaces Kyutai’s Mimi, which Liquid AI used in LFM2-Audio-1.5B. Mimi has 96.2M parameters. How does LiquidCodec work? The pipeline is encode, quantize, decode. Liquid AI uses blocks from its LFM3 architecture in both the encoder and decoder. The 2 halves are symmetric, and the encoder is fully causal, which suits streaming. Quantization uses grouped finite scalar quantization (GFSQ) instead of residual vector quantization (RVQ). In RVQ, each codebook encodes what the previous one missed. GFSQ groups are independent, so all 12 codebooks can be predicted in parallel by MaskGIT-style transformers. The decoder predicts spectral coefficients directly. This avoids learned transposed-convolution upsampling, a common cost in waveform decoders. How was it trained? Training ran in 2 stages. Stage 1 used 264,000 hours of speech across 107 languages. It applied waveform and spectral losses, without adversarial losses. Stage 2 fine-tuned on 55,000 hours of
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LLMON, un nuevo lenguaje de marcado para LLM, ahora está integrado en los modelos Granite 4.1 de IBM, mejorando la forma en que estos sistemas de IA procesan y generan datos estructurados.