Prevenzione allucinazioni/loop infiniti (Qwen3-TTS autoregressivo):
- block_repeated_ngrams: maschera i token che ricreerebbero un n-gram gia visto (n=4)
- has_repeating_cycle: ferma la generazione su cicli periodici (periodo 1-16, 4 ripetizioni)
- stuck detector: token dominante nella finestra recente (4 occorrenze in 8 token)
- fallback EOS quando tutti i logits sono mascherati (evita NaN)
- KV cache talker configurabile (--kv-cache, default 8192): 4096 overflowava con
reference lunghe + testi lunghi ("decode would overflow cache")
- parametri esposti via API (no_repeat_ngram_size, loop_max_period, loop_repeats,
loop_window) e CLI (--no-repeat-ngram, --loop-period, --loop-repeats, --loop-window)
- Docker: TTS_KV_CACHE env (default 8192)
Validato: testo 2788 char che prima degenerava in loop ora sintetizza pulito
(141s via API, nessuna ripetizione).
* Add Dockerfile (cpu/cuda), entrypoint, docs, and a GHCR release workflow
Multi-stage Dockerfile with cpu and cuda targets, built from the
existing build scripts' cmake invocations. The CUDA target documents
and applies the two docker-build-specific gotchas we hit running this
in production: CMAKE_CUDA_ARCHITECTURES needs an explicit override for
GPUs older than this project's own default arch list when building
without GPU device access, and the CUDA driver stub library needs an
explicit -L/-lcuda at link time since ggml's VMM pool allocator needs
driver-API symbols that aren't present without a real driver. Also
installs make/pkg-config/libopenblas-dev (CPU build) and libgomp1
(CUDA runtime), and copies the ggml shared libraries alongside the
binaries with LD_LIBRARY_PATH set, since the binaries' baked-in RPATH
points at the build-tree location that doesn't exist in the final
stage.
Adds a GitHub Actions workflow that builds both variants on every
push to master and version tag, publishing to
ghcr.io/serveurpersocom/qwentts.cpp, and validates the build (without
pushing) on PRs that touch the Docker files.
Verified end-to-end on a real GTX 1070 (Pascal): both cpu and cuda
targets build clean, run, and produce valid synthesized WAV output
through tts-server's HTTP API.
* Dockerfile: add a vulkan build target (AMD/Intel GPUs)
Uses the LunarG Vulkan SDK apt repo for glslc (ggml-vulkan's shader
compiler), which Ubuntu 22.04's own repos don't package. Runtime image
ships Mesa's Vulkan drivers; NVIDIA users should prefer :cuda instead,
since NVIDIA's Vulkan ICD isn't bundled.
* docker workflow: bump actions to latest majors (checkout v7, buildx v4, login v4, build-push v7)
* Support Pascal (sm_61) in the default CUDA architecture list
Adds 61-real to CMAKE_CUDA_ARCHITECTURES' default so Pascal cards
(GTX 10-series etc.) work without an explicit override -- including
in the Docker cuda target, where docker build has no GPU device to
autodetect against in the first place. Real-only (no virtual/PTX):
Pascal is now the oldest supported card, so it doesn't need to seed
forward JIT compatibility for anything older the way the 75-virtual
entry does for 7.5+.
Verified end-to-end on a real GTX 1070 (sm_61): built the cuda Docker
target with no --build-arg override, ran it with --gpus all, and
confirmed via container logs that the GPU loaded the model and served
a real synthesis request producing valid WAV output.
Adjusts the Dockerfile comments and docs/DOCKER.md accordingly.
* docker: pass through --max-prefill-tokens, add ref_text voice cloning
- entrypoint.sh: forward MAX_PREFILL_TOKENS to --max-prefill-tokens,
matching the existing optional-flag passthrough pattern
- entrypoint.sh: a same-stem .txt next to a voice's .wav now supplies
ref_text, enabling ICL clone mode instead of the x_vector_only
fallback used when no transcript is given
- switch voice registration's JSON construction from raw printf to jq
for safe escaping of arbitrary transcript text; base64 payloads go
through jq's --rawfile (not --arg) since large files blow past
ARG_MAX as a command-line argument
- add jq to all three runtime image stages (cpu/cuda/vulkan) for the
above
- docs/DOCKER.md: document both additions
Verified end-to-end on a CPU build: both the ref_text and no-ref_text
registration paths log correctly (ref_text=yes / ref_text=no) and
/health responds after voice registration completes.
* docker: opt-in fatal worst-case warmup synthesis
ggml_backend_sched grows its compute buffer to fit the largest graph
it has ever built and never shrinks it back, so VRAM use can ratchet
up the first time a long reply arrives on live traffic. This adds an
opt-in startup warmup: when WARMUP_VOICE is set, entrypoint.sh runs
one real synthesis capped at WARMUP_MAX_NEW_TOKENS (default 750)
frames after voice registration, forcing that worst-case decode/
codec-decode buffer growth to happen at startup instead of mid-request.
Complements --max-prefill-tokens, which only covers the input side.
Off by default (WARMUP_VOICE unset skips it entirely, matching every
other optional flag in this entrypoint), and fatal on failure: if the
warmup synthesis fails (most likely OOM), the container kills the
server and exits non-zero rather than come up healthy and fail
unpredictably later -- a deployment that can't afford its own
configured worst case should know that at startup, not on a live
request.
Verified on a CPU build: warmup runs after voice registration, hits
the configured frame cap exactly, and the container stays healthy.
---------
Co-authored-by: Gary <gitea@gerasch.dev>
enne2
