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silero-vad-sherpa
Silero VAD served through the sherpa-onnx backend. Uses the same ONNX weights as the dedicated silero-vad backend, loaded through sherpa-onnx's C VAD API. Pairs with the sherpa-onnx ASR entries for round-trip audio pipelines.
Repository: localaiLicense: mit
voxcpm-1.5
VoxCPM 1.5 is an end-to-end text-to-speech (TTS) model from ModelBest. It features zero-shot voice cloning and high-quality speech synthesis capabilities.
Repository: localaiLicense: apache-2.0
neutts-air
NeuTTS Air is the world's first super-realistic, on-device TTS speech language model with instant voice cloning. Built on a 0.5B LLM backbone, it brings natural-sounding speech, real-time performance, and speaker cloning to local devices.
Repository: localaiLicense: apache-2.0
vllm-omni-qwen3-tts-custom-voice
Qwen3-TTS-12Hz-1.7B-CustomVoice via vLLM-Omni - Text-to-speech model from Alibaba Qwen team with custom voice cloning capabilities. Generates natural-sounding speech with voice personalization.
Repository: localaiLicense: apache-2.0
vibevoice-cpp
VibeVoice Realtime 0.5B (C++ / GGML, Q8_0) - native C++ port of Microsoft VibeVoice
via the vibevoice-cpp backend. 24kHz mono TTS with voice cloning from a single
reference voice prompt. Default voice prompt: en-Carter_man.
Repository: localaiLicense: mit
vibevoice-cpp-asr
VibeVoice ASR 7B (C++ / GGML, Q4_K) - long-form speech-to-text with speaker
diarization. Returns per-speaker JSON segments with start/end timestamps.
English-only. ~10 GB download.
Repository: localaiLicense: mit
qwen3-tts-cpp-customvoice
Qwen3-TTS 0.6B Custom Voice (C++ / GGML) — text-to-speech with voice cloning support.
Generates 24kHz mono audio with optional reference audio for voice cloning via ECAPA-TDNN speaker embeddings.
Supports 10 languages (en, zh, ja, ko, de, fr, es, it, pt, ru).
Repository: localaiLicense: apache-2.0
qwen3-tts-1.7b-custom-voice
Qwen3-TTS is a high-quality text-to-speech model supporting custom voice, voice design, and voice cloning.
Repository: localaiLicense: apache-2.0
qwen3-tts-0.6b-custom-voice
Qwen3-TTS is a high-quality text-to-speech model supporting custom voice, voice design, and voice cloning.
Repository: localaiLicense: apache-2.0
fish-speech-s2-pro
Fish Speech S2-Pro is a high-quality text-to-speech model supporting voice cloning via reference audio. Uses a two-stage pipeline: text to semantic tokens (LLaMA-based) then semantic to audio (DAC decoder).
Repository: localaiLicense: fish-audio-research-license
vibevoice
Repository: localaiLicense: mit
liquidai_lfm2-350m-extract
Based on LFM2-350M, LFM2-350M-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
Use cases:
Extracting invoice details from emails into structured JSON.
Converting regulatory filings into XML for compliance systems.
Transforming customer support tickets into YAML for analytics pipelines.
Populating knowledge graphs with entities and attributes from unstructured reports.
You can find more information about other task-specific models in this blog post.
Repository: localaiLicense: lfm1.0
liquidai_lfm2-1.2b-extract
Based on LFM2-1.2B, LFM2-1.2B-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
Use cases:
Extracting invoice details from emails into structured JSON.
Converting regulatory filings into XML for compliance systems.
Transforming customer support tickets into YAML for analytics pipelines.
Populating knowledge graphs with entities and attributes from unstructured reports.
Repository: localaiLicense: lfm1.0
kokoros
Kokoros is a pure Rust TTS backend using the Kokoro v1.0 ONNX model (82M parameters).
Fast, streaming TTS with high quality. American English with af_heart voice.
Repository: localaiLicense: apache-2.0
kitten-tts
Kitten TTS is an open-source realistic text-to-speech model with just 15 million parameters, designed for lightweight deployment and high-quality voice synthesis.
Repository: localaiLicense: apache-2.0
speechbrain-ecapa-tdnn
Speaker (voice) recognition with SpeechBrain's ECAPA-TDNN trained
on VoxCeleb. 192-d L2-normalised embeddings, ~1.9% Equal Error
Rate on VoxCeleb1-O. APACHE 2.0 — commercial-safe.
The checkpoint is auto-downloaded from HuggingFace on first
LoadModel (no separate weight file in gallery `files:`). Points at
the upstream SpeechBrain HF repo directly — same bytes every
deployment.
Repository: localaiLicense: apache-2.0
wespeaker-resnet34
Speaker recognition with WeSpeaker's ResNet34 trained on VoxCeleb,
exported to ONNX. 256-d embeddings, CPU-friendly — avoids the
PyTorch runtime entirely (onnxruntime only). APACHE 2.0.
Pair with the `speaker-recognition` backend's OnnxDirectEngine.
Use when ECAPA-TDNN's torch dependency is undesirable (small
images, edge deployments).
Repository: localaiLicense: cc-by-4.0
e-n-v-y_legion-v2.1-llama-70b-elarablated-v0.8-hf
This checkpoint was finetuned with a process I'm calling "Elarablation" (a portamenteau of "Elara", which is a name that shows up in AI-generated writing and RP all the time) and "ablation". The idea is to reduce the amount of repetitiveness and "slop" that the model exhibits. In addition to significantly reducing the occurrence of the name "Elara", I've also reduced other very common names that pop up in certain situations. I've also specifically attacked two phrases, "voice barely above a whisper" and "eyes glinted with mischief", which come up a lot less often now. Finally, I've convinced it that it can put a f-cking period after the word "said" because a lot of slop-ish phrases tend to come after "said,".
You can check out some of the more technical details in the overview on my github repo, here:
https://github.com/envy-ai/elarablate
My current focus has been on some of the absolute worst offending phrases in AI creative writing, but I plan to go after RP slop as well. If you run into any issues with this model (going off the rails, repeating tokens, etc), go to the community tab and post the context and parameters in a comment so I can look into it. Also, if you have any "slop" pet peeves, post the context of those as well and I can try to reduce/eliminate them in the next version.
The settings I've tested with are temperature at 0.7 and all other filters completely neutral. Other settings may lead to better or worse results.
Repository: localaiLicense: llama3.3
steelskull_l3.3-shakudo-70b
L3.3-Shakudo-70b is the result of a multi-stage merging process by Steelskull, designed to create a powerful and creative roleplaying model with a unique flavor. The creation process involved several advanced merging techniques, including weight twisting, to achieve its distinct characteristics.
Stage 1: The Cognitive Foundation & Weight Twisting
The process began by creating a cognitive and tool-use focused base model, L3.3-Cogmoblated-70B. This was achieved through a `model_stock` merge of several models known for their reasoning and instruction-following capabilities. This base was built upon `nbeerbower/Llama-3.1-Nemotron-lorablated-70B`, a model intentionally "ablated" to skew refusal behaviors. This technique, known as weight twisting, helps the final model adopt more desirable response patterns by building upon a foundation that is already aligned against common refusal patterns.
Stage 2: The Twin Hydrargyrum - Flavor and Depth
Two distinct models were then created from the Cogmoblated base:
L3.3-M1-Hydrargyrum-70B: This model was merged using `SCE`, a technique that enhances creative writing and prose style, giving the model its unique "flavor." The Top_K for this merge were set at 0.22 .
L3.3-M2-Hydrargyrum-70B: This model was created using a `Della_Linear` merge, which focuses on integrating the "depth" of various roleplaying and narrative models. The settings for this merge were set at: (lambda: 1.1) (weight: 0.2) (density: 0.7) (epsilon: 0.2)
Final Stage: Shakudo
The final model, L3.3-Shakudo-70b, was created by merging the two Hydrargyrum variants using a 50/50 `nuslerp`. This final step combines the rich, creative prose (flavor) from the SCE merge with the strong roleplaying capabilities (depth) from the Della_Linear merge, resulting in a model with a distinct and refined narrative voice.
A special thank you to Nectar.ai for their generous support of the open-source community and my projects.
Additionally, a heartfelt thanks to all the Ko-fi supporters who have contributed—your generosity is deeply appreciated and helps keep this work going and the Pods spinning.
Repository: localaiLicense: llama3.3
ultravox-v0_5-llama-3_1-8b
Ultravox is a multimodal Speech LLM built around a pretrained Llama3.1-8B-Instruct and whisper-large-v3-turbo backbone.
See https://ultravox.ai for the GitHub repo and more information.
Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special <|audio|> pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual.
In a future revision of Ultravox, we plan to expand the token vocabulary to support generation of semantic and acoustic audio tokens, which can then be fed to a vocoder to produce voice output. No preference tuning has been applied to this revision of the model.
Repository: localaiLicense: mit
pygmalionai_eleusis-12b
Alongside the release of Pygmalion-3, we present an additional roleplay model based on Mistral's Nemo Base named Eleusis, a unique model that has a distinct voice among its peers. Though it was meant to be a test run for further experiments, this model was received warmly to the point where we felt it was right to release it publicly.
We release the weights of Eleusis under the Apache 2.0 license, ensuring a free and open ecosystem for it to flourish under.
Repository: localaiLicense: apache-2.0
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