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
Fish Audio S2 Pro is a leading text-to-speech (TTS) model with fine-grained inline control of prosody and emotion. Trained on over 10M+ hours of audio data across 80+ languages, the system combines reinforcement learning alignment with a dual-autoregressive architecture. The release includes model weights, fine-tuning code, and an SGLang-based streaming inference engine. S2 Pro builds on a decoder-only transformer combined with an RVQ-based audio codec (10 codebooks, ~21 Hz frame rate) using a Dual-Autoregressive (Dual-AR) architecture: - Slow AR (4B parameters): Operates along the time axis and predicts the primary semantic codebook. - Fast AR (400M parameters): Generates the remaining 9…
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other
4.6B parameters
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
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…
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other
5.8B parameters
2,048 tokens
transformers
This repository contains a self-contained pure-MLX int8 conversion of Step-Audio-EditX for local voice cloning and expressive audio editing on Apple Silicon. All pipeline components are stored as.safetensors — no PyTorch, ONNX, or NumPy files are required at inference time. This bundle is self-contained — all weights are packaged in one repository. Five-stage pipeline, all running pure MLX with bf16 activations: 1. Step1 LM (3.5B params, int8) — autoregressive dual-codebook token generation 2. CampPlus (bf16) — speaker embedding extraction from reference audio 3. Flow conditioner (bf16) — conditions generation on speaker embedding 4. Flow model (int8) — flow-matching mel spectrogram…
Open weights
apache-2.0
3.5B parameters
mlx
