Inkling
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.
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Qwen3-TTS
Qwen3-TTS is an open source series of advanced text-to-speech models developed by the Qwen team at Alibaba Cloud under the Apache-2.0 license, offering stable, expressive, and real-time speech generation with features such as voice cloning, voice design, and fine-grained control of prosody and acoustic attributes. The models support 10 major languages, including Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian, and multiple dialectal voice profiles with adaptive control over tone, speaking rate, and emotional expression based on text semantics and instructions. Qwen3-TTS uses efficient tokenization and a dual-track architecture that enables ultra-low-latency streaming synthesis (first audio packet in ~97 ms), making it suitable for interactive and real-time use cases, and includes a range of models with different capabilities (e.g., rapid 3-second voice cloning, custom voice timbres, and instruction-based voice design).
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Piper TTS
Piper is a fast, local neural text-to-speech (TTS) system optimized for devices like the Raspberry Pi 4, designed to deliver high-quality speech synthesis without relying on cloud services. It utilizes neural network models trained with VITS and exported to ONNX Runtime, enabling efficient and natural-sounding speech generation. Piper supports a wide range of languages, including English (US and UK), Spanish (Spain and Mexico), French, German, and many others, with voices available for download. Users can run Piper via the command line or integrate it into Python applications using the piper-tts package. The system allows for real-time audio streaming, JSON input for batch processing, and supports multi-speaker models. Piper relies on espeak-ng for phoneme generation, converting text into phonemes before synthesizing speech. It is employed in various projects such as Home Assistant, Rhasspy 3, NVDA, and others.
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Nemotron 3.5 Transcribe
NVIDIA Nemotron 3.5 Transcribe is an open-weight speech and audio model for developers building transcription and audio-processing systems. It converts recordings into text and supports verbatim or edited transcription, contextual vocabulary, speaker attribution, word-level timestamps, speech translation, language identification, and audio question answering. Its language matrix covers 40 languages, with task coverage varying by language. The model can process recordings up to one hour and is designed for offline inference using compatible NVIDIA GPU hardware and supported runtimes. Teams can integrate it into speech interfaces, meeting and media processing, customer-service analytics, translation tools, and other audio-understanding applications. Model weights and a fine-tuning toolkit are released under the OpenMDW-1.1 license; hardware and serving requirements depend on deployment.
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