gigaam-v3-e2e-rnnt-gguf is an open-source Russian ASR model using RNN-T decoding, distributed in GGUF format for local inference. Below are 7 voice, tts & speech apps with similar functionality to Gigaam V3 E2e Rnnt, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
gigaam-v3-rnnt-gguf is an open-source automatic speech recognition model for Russian, distributed in GGUF format for use with transcribe.cpp and similar tools. It uses RNN-T decoding and supports quantized variants for efficient offline transcription.
gigaam-v3-e2e-ctc-gguf is an open-source Russian automatic speech recognition model using CTC decoding, distributed in GGUF format for use with local inference tools. It supports quantized variants for efficient offline transcription.
gigaam-v3-ctc-gguf is an open-source Russian ASR model using CTC decoding, distributed in GGUF format for use with local inference tools. It supports quantized variants for efficient offline transcription.
Fun-ASR-Nano-2512-gguf is an open-source automatic speech recognition model distributed in GGUF format for local inference. It is designed for efficient offline speech-to-text transcription and supports quantized variants.
nemotron-3.5-asr-streaming-0.6b-gguf is an open-source automatic speech recognition (ASR) model supporting 28 languages. It enables developers to transcribe audio streams into text efficiently, with support for streaming and cache-aware inference. Ideal for building multilingual speech-to-text solutions.
Voxtral-Mini-4B-Realtime-2602-gguf is an open-source automatic speech recognition (ASR) model supporting 13 languages. It enables developers to transcribe audio to text locally using the GGUF format and integrates with tools like transcribe.cpp. Ideal for building multilingual ASR solutions.
Qwen3-ASR-0.6B-gguf is an open source automatic speech recognition model supporting over 30 languages. It is designed for developers and researchers needing accurate speech-to-text transcription in multilingual contexts.
