Devs Hunting for Good Local STT/TTS Models
Developers and language learners are struggling to find optimal, locally-run speech-to-text and text-to-speech models. They face challenges with model size, accuracy, features (like diarization), language support, and licensing restrictions, often requiring significant experimentation and fine-tuning. The desire for self-hosting and avoiding cloud APIs is a key driver.
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“yeah maybe it's worth a shot. what would you suggest? just running the problem bits through it or? Because I don't really like what it does when a whole long file is given to it, especially as I want to do more precision work with…”
A lot of us picking hardware for a local Assist pipeline end up guessing: does this SBC actually run faster-whisper medium in real time, or do I need to drop to tiny…
“Hey everyone, I’m trying to build a reliable, warm AI companion for my elderly dad. He’s an older Cantonese/Taishanese speaker. My mom passed away 1–2 years ago after years of a traumatizing terminal illness that really destroyed our family. Since then my dad has been depressed, and because of physical limitations from a stroke he doesn’t like leaving the house much. He spends a lot of time alone at home. I want something that can offer everyday conversation, practical advice, simple news explan”
“Totally fair point. We have a requirement to sign up because we have a transcription service that we provide. We don't want to provide unauthenticated access to use the transcription service. The focus of the project was on local-first transcription, but consistently a lot of users were having problems with local models: poor accuracy issues, not having enough computer specs to run the models, and latency issues, as you've noticed too. That's why the focus of the project has been on”
“> Tiron is an open-weights multi-speaker meeting transcription model. It jointly transcribes andattributes speech to speakers in a single decoding pass: for each 30-second audio window it emits an inline transcript with <|speakerN|> turn markers (up to 8 speakers per window) and <|t.tt|>timestamps. It now has been released for a few days, curious about initial experiences. Especially multi-lingual. submitted by /u/Balance- [link] [comments]”
“I do 100% agree with you, the way it speaks English really hurts my ears 😱. As I have just switched to it, I'm still trying to figure out what and if anything can be done to improve it.”
“VibeVoice-ASR-BitNet is a compressed variant of VibeVoice-ASR optimized for real-time inference on edge CPUs — no GPU required. Through heterogeneous quantization, the model is compressed from 4.62 GB to 1.58 GB while achieving 1.6–2.3× faster inference than Whisper.cpp with real-time capability (RTF < 1) on as few as 3 CPU threads. submitted by /u/Balance- [link] [comments]”
“When I released Inflect v2 last week, I thought most people would ask whether a TTS model this small actually sounded decent. Instead, I kept getting two questions: “Can I train it on my own voice?” “Can I move it to another language?” At the time, my answer was basically: technically yes, but not with any public workflow I’d trust someone else to use. So I spent the weekend building one. Quick context if you missed the original release: Inflect-Nano-v2: 3,966,721 parameters, 15.97 MB FP32 Infle”
“https://tr.me/MurmrVoxBot this may not exactly what you are asking but it might be work for you”
Quick pronunciation note before anything else: GOESB = “goose-bee.” Yes, it’s a bit silly. We’re fine with that. So here’s the actual story of why this exists: every time someone asks “what…
“How card can it be? You have a favorite IDE, right? Pick an AI service you can afford. If you are a student at an institution you probably get heavy discounts, if not pick the cheapest monthly tier on an AI service you respect. If you are not sure about committing to a subscription then just buy tokens on Deepseek.Find an agent plugin aka a harness for your IDE that doesn't require a subscription, unless of course you enjoy giving money away. Some agents are configured to work with particular pr”
“I work on on-device speech and wanted to see whether one Android phone could run the complete voice-agent loop without a server. This became a much longer side project than I expected. The current pipeline is: Silero VAD → Parakeet 120M speech recognition → FunctionGemma 270M tool routing → Android action → Pocket TTS After the models are downloaded, speech and commands stay on the phone. It can currently find and call contacts, play local music, and change media volume. The most interesting par”
Hi there, as I am trying to switch from english tts (alexa) to local speech with piper I face some issues. But first let me say, it’s impressive it’s even possible to…
“GigaChat Audio 10B is an audio-native LLM built on top of the GigaChat 3.1 Lightning text model. A Conformer speech encoder and a modality adapter feed audio embeddings directly into a Mixture-of-Experts decoder, so the model keeps the text quality of its base while adding speech understanding. Capabilities: audio question answering and classification, temporal grounding (localization in long audio, timestamped event descriptions, audio summarization with timestamps), tool-use, and text-only tas”
“Embedded SLM. I work in bay area and it’s wild how many open source fine tuned ~1B models are used for offline products.”
“There are some interesting speech-to-text models below 1B like MoonShine and Whisper that are pretty good. If you go higher up to 2.5B you can also use Canary or Qwen’s model.”
English and one voice only. That's why it can be so small.
“Been working on a local voice assistant for a while and the wake word is the part I keep getting stuck on. Not the easy version where you pick one of the pretrained models, I mean the user types in whatever phrase he wants and it just has to work. Offline, CPU only, since most machines I care about have no usable GPU. Right now I run a Vosk recognizer with a grammar that only knows my phrase and unk. That part is cheap and fires fine. The problem is the second step where I check if the hit was r”
absolutely amazing! What hardware was the model trained on?
“conversation: https://chatgpt.com/share/6a63d811-80e4-83eb-a5a3-10e06db399f1 submitted by /u/erfrischungsgetraenk [link] [comments]”
“When ran on Hugging Face's CPU Upgrade (8 vCPU), the Micro model is 0.159 RTF, while the Nano model is 0.093 RTF (10x faster than real-time). It's extremely lightweight!”
“Sub-4M params with usable speech is wild. Curious how it handles prosody on longer sentences — most tiny TTS models sound fine on short prompts but fall apart past 2-3 sentences. Have you tested it with paragraph-length input?”
“A sub-10M local STT model is actually one of the projects I’m considering next. I don’t want to promise it yet, but what hardware and use case would you need it for?”
“Pretty cool work! To what extent does your model depend on espeak-ng? If the dependency is minimal or nonexistent, I’d be interested in adding the model to audio.cpp.”
“Depending on how good this model performs, I might do a v3 expansion update. After this project, I might try doing a larger TTS model, or I'll try a different ML type, like STT.”
“holy shit this looks INSANE! is this fully free use cuz im very much considering using this (and hopefully v3) in a local, mobile ai assistant project im making (think google assistant, gemini and apple intelligence but like local) cuz having such a tiny model with a negligible footprint create extremely viable speech is insane. it does have it's kinks but who cares cuz holy FUCK. anyway best of luck with this, genuinely hope the best for the project cuz it deserves to pop off”
Sick. Running this on my ''97 power Macintosh with 32mb ram at 100 tok/s
“Not one at the moment - the current model is released in PyTorch. But I'm planning on doing it really, really soon! In about 3 days or maybe earlier, I'm likely gonna release a patch introducing different file types like ONNX and quantizations, alongside some minor bug fixes if I discover them.”
Wow, this is legit! Nice work, what are you planning on next?
Very well done! Thank you for sharing this with the community.
Could you make a speech to text the same size, that’s what I need
“I’ve spent the past month trying to find the point where an extremely small TTS model stops feeling like a size experiment and starts feeling genuinely useful. Today I’m releasing Inflect v2 , with two complete local text-to-speech models: Inflect-Nano-v2: 3.96M parameters, 15.97 MB FP32 Inflect-Micro-v2: 9.36M parameters, 37.53 MB FP32 These are total inference parameter counts , not acoustic-model-only numbers. Text processing, timing prediction, speech generation, and the waveform decoder are”
“Average speaking speed is 150 words per minute. Typing is 40. So up to 3x faster. A 2016 Stanford study backs this up too, speech came out 3x faster than typing. After that I read a bunch of developer comments saying that once you factor in editing time, the gap drops closer to 2x. Not a scientific paper, but still a real gain. If anyone's been using voice prompts for a while, curious to hear what you've noticed. submitted by /u/foric0 [link] [comments]”
I’m experimenting with local voice, I have an three M5 echo’s (two flavors) and a Voice Preview Edition. I did some experimentation with an Ollama model but I can’t run it on…
“We trained diffusion-gemma-asr, an open-source speech recognition model that is 15x faster than Whisper, based on DiffusionGemma and Whisper Small.Instead of generating text one token at a time like Whisper, in diffusion-gemma-asr we transcribe by denoising an entire transcript in parallel.To enable the model to learn, we don’t touch DiffusionGemma in training, rather we use a frozen whisper-small’s encoder for acoustic features, and train only a 42M parameter adapter, about 0.16% of the entire”
“Hello, so I have been playing around with GPT Live for the past few days, and everything is going well, but when I ask it to change its accent or its speech rate, it doesn’t seem to work. Am I asking it something it Can’t do, or is there another problem? I’m on the free tier by the way. If anyone can offer any assistance or advice, it would be much appreciated. Thank you so much. submitted by /u/CuriousMind583 [link] [comments]”
“Completely local setup using 4x 5060 ti (16GB each) and 1x 5070 (12GB) to run huggingface/speech-to-speech and Minachist/Qwen3.6-27B-INT8-Autoround. I have always found the Feynman technique to be incredible useful for studying and solidifying your understanding of a concept. Explaining a concept out loud makes it brutally clear where you lack in your understanding. The prompt I have configured has the AI ask me to explain a particular concept that I am studying. Once I explain the concept, the”
“I am also getting this error, which seems to be because of a broken link https://gitlab.com/api/v4/projects/67994333/packages/generic/whisper-cpp/1.8.3-st.2/whisper-cpp-linux-x64-blas-legacy-legacy.tar.gz the correct one should be https://gitlab.com/api/v4/projects/67994333/packages/generic/whisper-cpp/1.8.3-st.2/whisper-cpp-linux-x64-blas-legacy.tar.gz It has legacy-legacy when it should be one legacy storyteller-1 | [2026-07-15 22:21:28.450 +0000] ERROR: Encountered error while running task &q”
“For a while now I've been maintaining tts-bench ( https://github.com/5uck1ess/tts-bench ) and a blind voting arena ( https://5uck1ess-tts-arena.hf.space ) where people A/B test open text-to-speech (TTS) models without knowing which is which. One problem I've always wanted to solve: having the agent talk to me (or actually call me) after a task is done. /voice (claude code feature) or any dictation (wispr flow or etc) is one-directional. So this project is a bi-directional conversation: h”
“Hi, Has there been any improvement to Voice models (like RVC) in the last two years, or has nothing changed? Thanks! submitted by /u/Iwishlife [link] [comments]”
“I'm still in the process of just switching over my convoluted chain of audio forks and whatnot to this and you've already got a major update. Wish I had something more concrete to offer than some praise for the project, but it really is fantastic work that's taking care of a major issue within the larger ecosystem.”
“MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as [S01] , [S02] , and beyond. Introduction MOSS-Transcribe-Diarize 0.9B turns real-world long-form audio into structured, speaker-aware transcripts in one pass. Instead of stit”
“I finally found time to play around with speech to prompt in open web UI. I must say that it works much better than I hoped for. Though it works great with shorter messages I tried to dictate a text that my agent should correct. The message was something around 1 1/2 minutes long and it just completely ignored the fact that I recorded that. Is there some kind of maximum or length that I’m not aware of? submitted by /u/Br0lynator [link] [comments]”
“I'm curious about your opinion on whether OpenAI managed to beat Sesame's solutions when it comes to voice communication with LLMs? submitted by /u/gordriver_berserker [link] [comments]”
“I just pushed a new audio.cpp update with streaming support and 4 ASR models: Nemotron 3.5 ASR, Higgs Audio STT, VibeVoice ASR, and Hviske ASR (da only). Overall 1.07x to 2.41x faster than Python. I decided to drop Parakeet-TDT since good implementations already exist, and I find the Nemotron model more interesting. Streaming support is also starting to land in audio.cpp. Right now, I added initial streaming support for two models that naturally fit streaming usage: Nemotron ASR and VoxCPM2 . Ne”
“They are advertising that it has temporal awareness here. I think that's really interesting if this is actually accurate. How it is doing that... Is it reading a timer behind the scenes, or is it actually aware of the time passage.”
“UAE has lots of money and is a niche no big lab* is working on. It does make sense to train a dedicated model for them.”
“We just open-sourced Gepard 1.0 , a TTS model built for real-time conversation. It’s streaming-first: audio starts the moment text arrives, generated frame by frame instead of waiting for a full sentence. - ~555M params : Qwen3.5 0.8B backbone (14 layers) + Nemo NanoCodec (FSQ, 22.05kHz) - ~20 x RTF , ~50ms TTFA on one RTX 5090 via vLLM - Up to 256 parallel sequences on a single RTX Pro 6000 Balckwell with 96GB VRAM - Zero-shot voice cloning from a few seconds of reference - Languages: English (”
“I do this every few months to see what's actually worth self-hosting. This round the new arrival is Kyutai's Pocket TTS and it's the first CPU-friendly model I've seen that does zero-shot voice cloning. Which is a big deal if you've ever wanted to run a home reader in your own voice, or your partner's, or a narrator you like. Test box: 4 CPU cores, 16GB RAM, no GPU. Same class of hardware most of us are running here. What I tested: 6 configs from 4 model families across t”
“Sharing a CPU TTS benchmark with objective MOS scores in case it's useful for anyone evaluating small TTS models. Adding this because Kyutai's Pocket TTS is architecturally different from the others in the field and I hadn't seen a head-to-head with it yet. Models: Kokoro 82M (PyTorch and ONNX Runtime, StyleTTS2-inspired) Supertonic 3 at 2 and 5 flow-matching steps (Vector Estimator backbone) Inflect-Nano-v1 (4.6M param FastSpeech-style, tiny end of the spectrum) Pocket TTS (~100M pa”
“Kyutai dropped Pocket TTS a bit ago and I've been sitting on it for a benchmark. Finally ran it head to head against the three CPU TTS models that have been getting attention (Kokoro 82M, Supertonic 3, Inflect-Nano-v1). 180 timed runs, 36 audio samples, objective MOS scores via UTMOS. Short version: Pocket TTS is the slowest of the six configs I tested, and it's still the most interesting model in the field. Here's why. What Pocket TTS actually is: It's a ~100M param streaming la”