MOSS-TTS-Local-Transformer-v1.5 is continued from MOSS-TTS-Local-Transformer-v1.0. It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, and evaluation tables, please refer to the MOSS-TTS-Local-Transformer-v1.0 README. Compared with MOSS-TTS-Local-Transformer-v1.0, v1.5 focuses on the following improvements: - Stronger multilingual synthesis with language tags: when the language field is omitted, v1.5 may improve some languages and regress slightly on others compared with 1.0. When…
Open weights
apache-2.0
4.6B parameters
32,768 tokens
transformers
Higgs TTS 3 is built for voice chat: it speaks, not just reads. It turns model responses into expressive conversational speech across 100+ languages, with zero-shot voice cloning and inline control over emotion, style, prosody, pauses, and sound effects. Higgs autoregressive decoder consumes interleaved text and audio tokens. Audio is encoded by the Higgs Tokenizer into 8 codebooks at 25 fps, staggered via a delay pattern, then mapped to backbone hidden states through a multi-codebook fused embedding. Output codes pass through a multi-codebook fused head, are de-delayed, and decoded back to waveform. The model reaches single-digit WER/CER on 102 languages, which split into · Chichewa/Nyanja…
Open weights
other
4.7B parameters
32,768 tokens
transformers
A trustremotecode packaging of 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 automap 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 (higgsaudiov2tokenizer), loaded automatically on first use. Requires transformers >= 5.5. generatespeech 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…
Open weights
other
4.7B parameters
32,768 tokens
transformers
03/18/2025 – We are releasing our 3B Orpheus TTS model with additional finetunes. Code is available on GitHub: CanopyAI/Orpheus-TTS Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances. Check out our Colab (link to Colab) or GitHub (link to GitHub) on how to run easy inference on our finetuned models. Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By…
Access requested at publisher
apache-2.0
3.8B parameters
transformers
Check our open-source repository https://github.com/boson-ai/higgs-audio for more details! We are open-sourcing Higgs TTS 2, a powerful audio foundation model pretrained on over 10 million hours of audio data and a diverse set of text data. Despite having no post-training or fine-tuning, Higgs TTS 2 excels in expressive audio generation, thanks to its deep language and acoustic understanding. On EmergentTTS-Eval, the model achieves win rates of 75.7% and 55.7% over "gpt-4o-mini-tts" on the "Emotions" and "Questions" categories, respectively. It also obtains state-of-the-art performance on traditional TTS benchmarks like Seed-TTS Eval and Emotional Speech Dataset (ESD). Moreover, the model…
Open weights
other
5.8B parameters
2,048 tokens
transformers
svara-TTS is a developer-first multilingual TTS model for 19 languages (18 Indic + Indian English). Built on an Orpheus-style discrete audio token approach, it targets clarity, expressiveness, and low-latency on commodity GPUs/CPUs. It supports light-weight emotion/style control (e.g.,,,, ) and simple speaker identities (Language (Gender)), with zero-shot adaptation paths. Try it live on the Demo Space, or on Colab Deployment scripts and inference repo will be available soon. Watch our Github for updates - Place style/emotion tags at the end of the sentence: आज... सच में अच्छी खबर है — शाम को मिलते हैं! - Use punctuation to hint prosody (ellipses, commas, exclamation). - For technical or…
Open weights
apache-2.0
3.3B parameters
131,072 tokens
transformers
