Pocket Tts GGUF
GGUF / ggml conversion of kyutai/pocket-tts for use with CrispStrobe/CrispASR.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 59,3–641 MB
- Modelo base
- kyutai/pocket-tts
- Versão
- vRevision d106810
- Atualizado
- 26 de out. de 2026
- Downloads
- 4,5 mil
- Fonte
- Hugging Face
Descrição da fonte
Pocket TTS — GGUF (ggml-quantised)
GGUF / ggml conversion of kyutai/pocket-tts for use with CrispStrobe/CrispASR.
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).
Released under CC-BY-4.0 license.
Voice cloning
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.
# clone the timbre of ref.wav (any sample rate; mono is used)
./build/bin/crispasr --backend pocket-tts -m pocket-tts-english-f16.gguf \
--voice ref.wav \
--tts "The quick brown fox jumps over the lazy dog." \
--tts-output fox.wav --seed 42
Pocket TTS produces near-silence without voice conditioning, so if you omit
--voice a built-in default reference is used automatically. Use a clean,
single-speaker reference of a few seconds for best results.
Files
The multilingual Q8_0/F16 files are full voice-cloning models. German, Spanish,
Italian, and Portuguese are Kyutai's distilled 6-layer releases; French is the
larger, undistilled 24-layer preview. The decoder-only novc alternatives
are currently English-only.
| File | Voice clone | Quant | Size | Notes |
|---|---|---|---|---|
pocket-tts-english-f16.gguf | ✅ | F16 | 219 MB | Reference quality, cloning |
pocket-tts-english-q8_0.gguf | ✅ | Q8_0 | 124 MB | Near-F16, cloning |
pocket-tts-english-q4_k.gguf | ✅ | Q4_K | 73 MB | Smallest with cloning |
pocket-tts-english-novc-f16.gguf | — | F16 | 200 MB | Decoder only |
pocket-tts-english-novc-q8_0.gguf | — | Q8_0 | 110 MB | Decoder only |
pocket-tts-english-novc-q4_k.gguf | — | Q4_K | 62 MB | Decoder only, smallest |
pocket-tts-german-q8_0.gguf | ✅ | Q8_0 | 124 MB | German, distilled 6L |
pocket-tts-spanish-q8_0.gguf | ✅ | Q8_0 | 124 MB | Spanish, distilled 6L |
pocket-tts-italian-q8_0.gguf | ✅ | Q8_0 | 124 MB | Italian, distilled 6L |
pocket-tts-portuguese-q8_0.gguf | ✅ | Q8_0 | 124 MB | Portuguese, distilled 6L |
pocket-tts-french_24l-q8_0.gguf | ✅ | Q8_0 | 365 MB | French, undistilled 24L preview |
F16 equivalents of every multilingual Q8_0 file are also available (about 219 MB for each 6-layer model and 673 MB for French).
Quick start
# 1. Build CrispASR
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j --target crispasr-cli
# 2. Download a model (voice-cloning F16 shown)
huggingface-cli download cstr/pocket-tts-GGUF pocket-tts-english-f16.gguf --local-dir .
# 3. Synthesize
./build/bin/crispasr --backend pocket-tts -m pocket-tts-english-f16.gguf \
--tts "Hello, how are you today?" \
--tts-output hello.wav --seed 42
Or with auto-download. -l de, es, it, pt, or fr selects and caches
the matching Q8_0 checkpoint; omit -l for English:
./build/bin/crispasr --backend pocket-tts -m auto --auto-download -l es \
--accept-license pocket-tts-terms \
--voice ref.wav --i-have-rights \
--tts "Hola, este modelo ya habla español." \
--tts-output hola.wav
Python binding
from crispasr import Session
sess = Session("pocket-tts-english-f16.gguf")
sess.set_tts_seed(42)
pcm = sess.synthesize("Hello world.")
sess.write_wav("hello.wav", pcm)
Conversion
Converted with models/convert-pocket-tts-to-gguf.py from the CrispASR repo
(--voice-cloning bakes in the Mimi encoder + speaker projection). The Mimi
codec and SentencePiece tokenizer are embedded in the single GGUF.
Fontes
1 fonte
Fontes
1 fonteVerificado 26 de out.
Download files
Artefatos de modelo
16 artefatos
Artefatos de modelo
16 artefatospocket-tts-english-novc-f16.gguf
gguf · 190 MB · SHA-256 4a2fbce8e247…8655 · Hugging Face
Download filepocket-tts-english-novc-q4_k.gguf
gguf · 59,3 MB · SHA-256 05cc42aef309…12bc · Hugging Face
Download filepocket-tts-english-novc-q8_0.gguf
gguf · 105 MB · SHA-256 5e76fe89ec30…208f · Hugging Face
Download filepocket-tts-french_24l-q8_0.gguf
gguf · 348 MB · SHA-256 9fe5ca8c8797…1cff · Hugging Face
Download filepocket-tts-portuguese-q8_0.gguf
gguf · 118 MB · SHA-256 02f28b603e1e…7e81 · Hugging Face
Download fileTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- license: cc-by-4.0 language: - en - de - es - it - pt - fr base_model: - kyutai/pocket-tts pipeline_tag: text-to-speech tags: - tts - text-to-speech - voice-cloning - pocket-tts - mimi - kyutai - gguf - crispasr library_name: ggml --- # Pocket TTS — GGUF (ggml-quantised) GGUF / ggml conversion of [`kyutai/pocket-tts`](https://huggingface.co/kyutai/pocket-tts) for use with **[CrispStrobe/CrispASR](https://github.com/CrispStrobe/CrispASR)**. 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). Released under **CC-BY-4.0** license. ## Voice cloning 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. ```bash # clone the timbre of ref.wav (any sample rate; mono is used) ./build/bin/crispasr --backend pocket-tts -m pocket-tts-english-f16.gguf \ --voice ref.wav \ --tts "The quick brown fox jumps over the lazy dog." \ --tts-output fox.wav --seed 42 ``` Pocket TTS produces near-silence without voice conditioning, so if you omit `--voice` a built-in default reference is used automatically. Use a clean, single-speaker reference of a few seconds for best results. ## Files The multilingual Q8_0/F16 files are full voice-cloning models. German, Spanish, Italian, and Portuguese are Kyutai's distilled 6-layer releases; French is the...
--- license: cc-by-4.0 language: - en base_model: - kyutai/pocket-tts pipeline_tag: text-to-speech tags: - tts - text-to-speech - voice-cloning - pocket-tts - mimi - kyutai - gguf - crispasr library_name: ggml --- # Pocket TTS — GGUF (ggml-quantised) GGUF / ggml conversion of [`kyutai/pocket-tts`](https://huggingface.co/kyutai/pocket-tts) for use with **[CrispStrobe/CrispASR](https://github.com/CrispStrobe/CrispASR)**. 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, 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). Released under **CC-BY-4.0** license. ## Voice cloning Pocket TTS is zero-shot: it clones the timbre of a short reference clip. The **voice-cloning builds** (`pocket-tts-english-{f16,q8_0,q4_k}.gguf` — the ones *without* `novc` in the name) embed the Mimi VAE **encoder** and speaker projection needed to condition on a reference; the `novc` builds omit that encoder and are ~20 MB smaller. ```bash # clone the timbre of ref.wav (any sample rate; mono is used) ./build/bin/crispasr --backend pocket-tts -m pocket-tts-english-f16.gguf \ --voice ref.wav \ --tts "The quick brown fox jumps over the lazy dog." \ --tts-output fox.wav --seed 42 ``` Pocket TTS produces near-silence without voice conditioning, so if you omit `--voice` a built-in default reference is used automatically. Use a clean, single-speaker reference of a few seconds for best results. ## Files Two families: **voice-cloning** (default, embeds the Mimi encoder) and **`novc`** (decoder only, smaller — use when you only need the built-in default voice). | File | Voice clone | Quan...
Source context: 4522 downloads · 7 likes · Pipeline text-to-speech · Library ggml · Repo cstr/pocket-tts-GGUF
