Each API request now seeds PyTorch's CPU and CUDA RNGs before generation.
The seed is auto-derived from the voice name (stable MD5 hash) if not set
in voices.json, so all existing voices get consistent voice character with
zero config changes. Override per-voice with "seed": <int> in voices.json.
Previously the sampling RNG was unseeded, causing pitch/modulation drift
across requests even when speaker embeddings (.pt files) were identical.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The upstream openai_server.py removed the instruct parameter from the
generate_voice_clone call, breaking the old patch context. Regenerated
from a fresh upstream clone with all four changes:
- non_streaming_mode=True in _stream_chunks and create_speech
- per-voice temperature/top_k/top_p in both generation paths
- --max-seq-len argument added to _parse_args
- max_seq_len=args.max_seq_len passed to FasterQwen3TTS.from_pretrained
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The upstream openai_server.py gained --max-seq-len after v5 was built.
Add it to the patch so every build gets explicit sequence length control.
Also add max_seq_len=args.max_seq_len to FasterQwen3TTS.from_pretrained()
so the argument is actually used.
Remove || true from git apply so patch failures fail the build instead
of silently producing a broken image.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The VoiceClone server was using non_streaming_mode=False, a mode designed
for streaming LLM->TTS pipelines. In that mode only one text token enters
the model's KV cache during prefill; the rest feed via trailing_text_hiddens
at one step per codec frame. For a 54-word paragraph this provides only ~4s
of text guidance for ~18s of speech — 77% generated with no text conditioning.
Without text context the model free-runs and drifts, sometimes changing gender.
Fix: switch to non_streaming_mode=True (already the default for VoiceDesign
and CustomVoice) so the full text is in the prefill throughout generation.
Also lower default temperature 0.9->0.8 and add top_p=0.9 to reduce
accumulated sampling noise over long runs. Temperature, top_k, and top_p
are now configurable per voice in voices.json.
- patches/openai_server.patch: updated for new upstream HEAD; both streaming
(WAV/PCM) and non-streaming (MP3) paths now use non_streaming_mode=True
- config/run_server.py: align warmup call to non_streaming_mode=True
- README.md: bump image tags v4->v5, add changelog section
- Version: v5
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Run Qwen3-TTS with CUDA graph acceleration on NVIDIA DGX Spark
(ARM64 / SM 121 / CUDA 13) as an OpenAI-compatible TTS API server.
- Dockerfile targeting nvidia/cuda:13.0.2-base-ubuntu24.04 with ARM64 cu130 PyTorch wheels
- Patch for max-seq-len support to handle long reference audio without crashes
- OpenWebUI + SillyTavern compatible API endpoints (/v1/models, /v1/audio/voices, /speakers)
- Voice management: auto-generate voices.json from speaker reference audio files
- Auto-transcription helper script for generating reference text from audio
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
