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Gradium has open-sourced Phonon, a family of small text-to-speech models built to run on the device. The Gradium Phonon on-device TTS model comes in 31M and 40M sizes. The English Phonon 31m ships under Apache 2.0 and fits in a 64.5 MB file. Developers get natural speech with no cloud call, no GPU and no per-character bill. TL;DR Size: 31M parameters (excluding the audio tokenizer), 64.5 MB q8 GGUF, 24 kHz mono output. Phonon 40m: 40M, 74.6 MB. Runs on: CPUs, phones, Raspberry Pi 5, browsers (WASM, WebGPU) and NVIDIA GPUs. No GPU required. Performance: top English Elo among open models Gradium tested; about 7 ms to first audio on CPU. Best: 1.70% WER on a 500-sentence hard set, versus 3.50% for Kokoro (Gradium’s eval). Worst: 0.51% Seed-TTS LLM WER, behind Kokoro (0.45%) and Magpie (0.46%). What is Gradium Phonon? Phonon is an open-weight TTS family from Gradium, a Paris voice AI company spun out of Kyutai. It generates speech locally on phones, laptops, boards and browsers. The family has three models: Phonon 31m: English only, 37 built-in voices, Apache 2.0. Phonon 40m: 1 model per language (English, French, German, Spanish, Portuguese), CC BY-NC 4.0. Phonon 40m multilingual: all 5 languages in 1 model, 102 voices, CC BY-NC 4.0. How does Phonon work? Phonon uses a causal transformer backbone. Its prefix combines text and speaker embeddings. A SentencePiece tokenizer with a 4,000-token vocabulary reads raw text, with no phoneme conversion. The backbone generates 1 latent vector per 80 ms frame. A flow-matching head turns that vector plus noise into an audio embedding in a single step. A smaller variant of Kyutai’s Mimi decoder then outputs 24 kHz PCM audio. A separate end-of-speech head decides when to stop. Phonon 31m uses 6 transformer layers, width 512 and 8 attention heads. Phonon 40m uses 9 layers at the same width. Both are distilled from a larger Phonon teacher. Training ran on an NVIDIA H100 cluster with NCCL and CUDA Graphs. The work build
Una ayuda automática para ordenar, basada en fuentes, palabras clave y entidades, en una escala de 1–12. Alta: 9–12; media: 6–8; baja: 1–5. No mide urgencia, verifica noticias ni representa respaldo editorial.
Cohere Rerank 5 mejora la precisión de búsqueda al devolver pasajes relevantes específicos y puntuaciones calibradas, lo que permite a los desarrolladores enviar menos tokens de mayor calidad a los LLMs para un mejor rendimiento de RAG.