higgs-audio-v3-tts-4b-transformers

A `trust_remote_code` packaging of [`bosonai/higgs-audio-v3-tts-4b`](https://huggingface.co/bosonai/higgs-audio-v3-tts-4b) that loads with plain 🤗 transformers (no SGLang). The weights are the original checkpoint, copied unchanged; only a small `modeling_*.py` / `configuration_*.py` pair and an `auto_map` were added. The model is a standard **Qwen3-4B** backbone plus a fused multi-codebook audio embedding/head. Reference-audio encoding and waveform decoding use the transformers-native [`bosonai/higgs-audio-v2-tokenizer`](https://huggingface.co/bosonai/higgs-audio-v2-tokenizer) (`higgs_audio_v2_tokenizer`), loaded automatically on first use. Requires `transformers >= 5.5`. import torch import torchaudio from transformers import AutoModelForCausalLM, AutoTokenizer repo = "multimodalart/higgs-audio-v3-tts-4b-transformers" tokenizer = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained( repo, trust_remote_code=True, dtype=torch.bfloat16 ).to("cuda").eval() wav = model.generate_speech("Hello, this is Higgs Audio running on transformers.", tokenizer) torchaudio.save("out.wav", wav.unsqueeze(0), model.config.sample_rate) ref, sr = torchaudio.load("reference.wav") wav = model.generate_speech( "Now speaking in the cloned voice.", tokenizer, reference_audio=ref, reference_sample_rate=sr, reference_text="optional transcript of the reference clip", temperature=0.7, top_p=0.95, ) torchaudio.save("clone.wav", wav.unsqueeze(0), model.config.sample_rate) `generate_speech` returns a mono 24 kHz waveform as a CPU float32 tensor `[L]`. - Generation uses Higgs' delay pattern across 8 codebooks (vocab 1026, incl. BOC/EOC specials); de-delay + decode are handled internally. - The codec runs in fp32 (decode is unstable in bf16); the LM backbone runs in the dtype you load it in (bf16 recommended). - License: research/non-commercial, inherited from the upstream checkpoint — see [`LICENSE`](LICENSE).