indic-parler-tts

Model · Text to speech

Qwen3-TTS-12Hz-0.6B-Base

Qwen

Qwen3-TTS is a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Trained on over 5 million hours of speech data spanning 10 languages, Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control. This specific checkpoint is the 0.6B Base model, which is capable of rapid voice cloning from a user-provided audio input. To clone a voice and synthesize new content using the Base model, you can use the following code snippet: Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles to meet global application…

Model · Text to speech

Qwen3-TTS-12Hz-0.6B-CustomVoice

Qwen

Qwen3-TTS is a series of advanced multilingual, controllable, robust, and streaming text-to-speech models developed by the Qwen team. This specific checkpoint is the 0.6B CustomVoice variant, based on the 12Hz tokenizer. It supports 9 premium timbres and allows for fine-grained style control over target voices via natural language instructions across 10 major languages. To use Qwen3-TTS, you can install the qwen-tts package: For Qwen3-TTS-12Hz-0.6B-CustomVoice, the following speakers are supported. We recommend using each speaker’s native language for the best results: If you find Qwen3-TTS useful for your research, please consider citing

Model · Text to speech

VibeVoice-Realtime-0.5B

Microsoft

VibeVoice-Realtime is a lightweight real‑time text-to-speech model supporting streaming text input and robust long-form speech generation. It can be used to build realtime TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in ~300 ms (hardware dependent). ▶ Watch demo video (Launch your own realtime demo via the websocket example in Usage) Although the model is primarily built for English, we found that it still exhibits a certain level of multilingual capability—and even performs reasonably well in some languages. We provide nine…

Model · Text to speech

OmniVoice

K2 FSA

OmniVoice is a massively multilingual zero-shot text-to-speech (TTS) model supporting over 600 languages. Built on a novel diffusion language model-style architecture, it delivers high-quality speech with superior inference speed, supporting voice cloning and voice design. - 600+ Languages Supported: The broadest language coverage among zero-shot TTS models. To get started, install the omnivoice library: You can use OmniVoice for zero-shot voice cloning as follows: For more generation modes (e.g., voice design), functions (e.g., non-verbal symbols, pronunciation correction) and comprehensive usage instructions, see our GitHub Repository. You can directly discuss on GitHub Issues. You can…

Model · Text to speech

gepard-1.0

NineNineSix

GEnerative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue Gepard is a text-to-speech model built for real-time conversation. It starts speaking the moment text begins arriving, generating audio piece by piece instead of waiting for a full sentence — so it feels like a live voice, not a recording. It's a single language model that learned text and speech together, so the output carries natural rhythm and timing rather than the flat, stitched tone of older pipelines. The name evokes "Gepard"(/geh-PART/), German for cheetah — a nod to the model's low-latency, high-throughput streaming. - One clean pass per frame — the whole audio frame (32 orthogonal FSQ channels) is…

Model · Text to speech

csm-1b

Sesame

2025/05/20 - CSM is availabile natively in Hugging Face Transformers as of version 4.52.1 2025/03/13 - We are releasing the 1B CSM variant. The checkpoint is hosted on Hugging Face. CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes. A fine-tuned variant of CSM powers the interactive voice demo shown in our blog post. A hosted HuggingFace space is also available for testing audio generation. CSM supports full-graph compilation with CUDA graphs! CSM can be fine-tuned using Transformers' Trainer. Does this…