MODEL PROFILE

pocket-tts-GGUF

cstr · pocket-tts-GGUF

Pocket TTS is a lightweight (~100M param) continuous-latent autoregressive TTS model from Kyutai, based on the CALM paper (arXiv:2509.06926). Unlike codebook-based TTS models, Pocket TTS emits continuous float vectors — no discrete tokens, no softmax sampling: - **FlowLM backbone** — causal transformer (1024D, 16 heads, 6 layers; 24 layers for the French preview, RoPE, GELU) operating at 12.5 Hz - **Consistency head** — SimpleMLPAdaLN (512D, 6 ResBlocks) with timestep embedding → one-step LSD decode → 32-dim continuous latent vectors - **Mimi VAE decoder** — SEANet upsample convolutions + 2-layer transformer → 24 kHz PCM - **Mimi VAE encoder** *(voice-cloning builds only)* — SEANet downsample + 2-layer transformer + speaker projection → reference conditioning - **Text tokenizer** — SentencePiece BPE (4000 vocab, embedded in GGUF) Single GGUF file — no separate codec companion needed (Mimi weights and the tokenizer are embedded). Pocket TTS is zero-shot: it clones the timbre of a short reference clip. The **voice-cloning builds** (the files *without* `novc` in the name) embed the Mimi VAE **encoder** and speaker projection needed to condition on a reference; the English-only `novc` builds omit that encoder and are ~20 MB smaller.

Speech synthesislicense:cc-by-4.0
Context windowNot disclosed
Input → OutputText → Audio
Model weightsNot verified
Profile updated2026/10/31 04:24:28
OVERVIEW

Model overview

Pocket TTS is a lightweight (~100M param) continuous-latent autoregressive TTS model from Kyutai, based on the CALM paper (arXiv:2509.06926). Unlike codebook-based TTS models, Pocket TTS emits continuous float vectors — no discrete tokens, no softmax sampling: - **FlowLM backbone** — causal transformer (1024D, 16 heads, 6 layers; 24 layers for the French preview, RoPE, GELU) operating at 12.5 Hz - **Consistency head** — SimpleMLPAdaLN (512D, 6 ResBlocks) with timestep embedding → one-step LSD decode → 32-dim continuous latent vectors - **Mimi VAE decoder** — SEANet upsample convolutions + 2-layer transformer → 24 kHz PCM - **Mimi VAE encoder** *(voice-cloning builds only)* — SEANet downsample + 2-layer transformer + speaker projection → reference conditioning - **Text tokenizer** — SentencePiece BPE (4000 vocab, embedded in GGUF) Single GGUF file — no separate codec companion needed (Mimi weights and the tokenizer are embedded). Pocket TTS is zero-shot: it clones the timbre of a short reference clip. The **voice-cloning builds** (the files *without* `novc` in the name) embed the Mimi VAE **encoder** and speaker projection needed to condition on a reference; the English-only `novc` builds omit that encoder and are ~20 MB smaller.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:cc-by-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。