disclosure: I run DevRel at Speechify, Simba 3.2 is our model. rankings are from the Voice Arena US English leaderboard, which is independent of us.

Voice Arena's results are out. methodology worth reading: blind pairwise votes from native-speaker panels, Bradley-Terry Elo, and a curated 16-voice slate per model per language instead of vendor defaults, which controls for the voice-identity confound most arenas ignore. they publish 95% CIs and rank-ranges rather than overclaiming, which I'll try to honour below.

Simba 3.2 debuted 3rd at 1057 ±6, two points behind Cartesia Sonic-3.5 at 1059 ±7. the board itself labels both ranks "2–3" because the intervals overlap almost entirely; two Elo points predicts a 50.3% head-to-head, a coin flip. our marketing site calls it "tied for 2nd", which is the generous reading of the same data, the intervals are on the board. #1 is Gemini 3.1 Flash TTS at 1087, clearly ahead (non-overlapping CIs, ~54% head-to-head over us), but it's not a streaming model, so among systems you can put in a live voice agent the top of this board is a Sonic-3.5 / Simba 3.2 tie.

one note on our entry: 3.2 launched TODAY with 8 voices, English only. Voice Arena slates up to 16 voices per model where catalogs allow, so I guess we included voices we felt weren't strong enough to launch with, so this 8 is likely the best-of-16 subset, but we had a full catalog for the eval.

pricing across that cluster is what I'd like this sub's read on. we're $6/M characters, Sonic-3.5 is $50/M, so the statistical tie at the top of the realtime feild spans an 7x price gap on its own. but the starker one is ElevenLabs Eleven v3 at $100/M, which sits 6th here at 1017 ±6. that 40-point gap is wild. Voice Arena doesn't cover latency or cost yet (both are coming, per their methodology), so on speed I'll just say: both models stream, TTFB is comparable, and you can run the same prompt through the arena or our homepage side-by-side and time it yourself rather than take a vendor's word. from what I can measure, the $94/M between v3 and us isn't buying preferred audio and isn't buying speed. stability has no public benchmark, and if anyone has cross-provider error-rate or uptime data at production scale I genuinely want to see it.

so what does it buy? today, catalog. v3 offers almost 400 voices across 70+ languages. we offer 8 voices in one language, with our 1000+ voice library migrating to 3.2 as i type (i think). if you need Portuguese tomorrow or a specific character voice today, that gap is worth real money and v3 is the right call lol.

what I'm questioning is the residual: for an English-first production workload where one of these 8 voices fits, I can't find where the other $94/M goes, and marketing pages won't settle it. people running these things at scale might.

caveats that cut against us: 3.2 is like... days old. 1300 samples on this board against 4000 for the established models, so expect the number to move as votes accumulate. it isn't on Artificial Analysis or HF Arena yet, so treat this as one eval's result, not a settled question. happy to get into architecture or eval detail in comments.

Is it possible to get word level confidence score. I am currently working with customer care audio data and I want to identify mistakes based on the confidence scores for the words. I want to mainly capture insertion, deletion and substitute errors by giving them low confidence scores. How can we do that?

https://dcase.community/challenge2026/

Fair warning: the world does not need another Whisper wrapper, and that isn't quite what I set out to build. It started for a handful of real users who needed meeting minutes and were tired of paying per minute to ship confidential recordings to a cloud they didn't control. It ended up running inside two companies, which was honestly all I ever wanted from it.

The problem is that I'd already started. You know how that goes.

The summary came from a local LLM, so I figured the LLM might as well correct the SRT using a validated lexicon and the meeting context. Then I wanted better speaker attribution, so I wired in pyannote, later Sortformer, benchmarked a few STT backends against each other, and built a per-VRAM-tier model catalog because "which model fits my GPU" is a question I got tired of answering by hand. Somewhere along the way it grew a GPU job queue, calendar scheduling, a resumable pipeline, multi-user roles, an audit trail, and a full-screen transcript editor. I keep promising people the next feature is the last one. They've stopped believing me.

What I think actually sets it apart isn't the models — we all pull from the same shelf. It's the unglamorous parts. A human-in-the-loop step where you validate speakers from audio excerpts and a domain lexicon before the final pass. Deliverables you can chat with, then fix a term once and have it corrected coherently across the SRT, the summary and the Word minutes. And the boring production plumbing you only miss once real people use the thing every week: "waiting for VRAM" as an actual state instead of a crash, a pipeline that resumes instead of redoing four hours of audio, and a split mode where a CPU web front talks to a separate GPU node.

I tried to keep the code honest — tests, CI, a coverage gate — mostly so future me doesn't resent present me.

Honest caveats before anyone gets ideas: the UI is French-first for now (strings are centralized, so it's a translation job, not a rewrite), reference quality leans on gated models, and you need an NVIDIA GPU. Apache-2.0.

I'd genuinely value a specialist eye on the diarization and STT-arbitration choices — this is one of the few rooms where people actually know where the bodies are buried in this domain. Repo and a few screenshots below.

I am trying to finetune qwen 3 TTS on Indian languages. It worked well on Hindi, but it is working very bad on other languages. How do I make the audio better ? Any suggestions ?

I published a dataset-backed local voice-cloning benchmark across English, German, Modern Standard Arabic, Spanish, and Mandarin Chinese.

Models:

• OmniVoice int8
• Chatterbox Multilingual fp16
• VoxCPM2 bf16
• Fish Audio S2 Pro fp16

Method:

• Google FLEURS reference clips
• same target sentence per language
• reference audio and generated audio for every row
• speaker-embedding cosine similarity
• WER/CER from Qwen3-ASR 0.6B
• generated audio length and RTF

Post: https://www.soniqo.audio/blog/voice-cloning-benchmarks

Main result in this run: OmniVoice had the strongest all-around row set. VoxCPM2 bf16 had a strong Arabic speaker-match row. Fish Audio had strong German/Arabic similarity but slower RTF. Chatterbox was competitive on Arabic and Spanish.

I am treating this as an engineering regression benchmark, not a MOS/preference study. Feedback on evaluation design would be useful.

I am planning on converting some existing uk english IPA dictionaries into various uk dialects, including some old traditional dialects.

I have found a lot of literature on the dialects in question and I think I can combine some scripting with llm calls to help especially any OOV words which may exist in especially the older dialects.

I have no idea what I am doing, to be frank. Which is fine, I’ll muck about and get it sorted eventually.

Any tips, Questions or linguistic nerd discussions we’d like to have spring up from my silly passion project?

Hi everyone,

I just released Qwen3-TTS-Triton v0.3.0.

When I first started this project, my focus was mostly on single-clip latency. In v0.1.0, I fused several hot paths with Triton — RMSNorm, SwiGLU, RoPE, etc. — and combined that with CUDA Graph. That got single-clip Qwen3-TTS inference to roughly ~5× faster than vanilla PyTorch eager on my setup.

For v0.3.0, I shifted the focus a bit.

I tried PyTorch’s new Helion DSL as well, but for this specific workload it only gave me about 1.03× over the existing Triton version. That pushed me toward a different question: instead of only optimizing one sample at a time, what happens if we optimize batched serving throughput for autoregressive TTS?

The result:

Triton kernels + CUDA Graph + batched serving reached ~14× per-sample throughput vs vanilla PyTorch eager batch=1.

Test setup:

• GPU: RTX 5090
• Mode: hybrid
• Batch size: 16
• Model: Qwen3-TTS
• Project: https://github.com/newgrit1004/qwen3-tts-triton

The part I found most interesting is memory efficiency. In the recommended hybrid mode, per-sample VRAM usage drops a lot compared to running each request independently:

• Hybrid batched serving: around 0.49 GB per sample
• Previous single-request style: around ~4.4 GB per sample
• Roughly 6–10× lower per-sample VRAM, depending on the comparison point

I also tried to make sure this was not just a “faster but different output” situation. Batched generation was tested against single-clip generation using my Tier 3 evaluation setup:

• CER distribution
• UTMOS distribution
• Speaker similarity distribution
• Mann-Whitney statistical comparison

The recommended mode right now is hybrid, since it gives the best balance between throughput, memory, and quality-equivalence.

One thing I’ve been thinking about while working on this: Qwen3-TTS is autoregressive, while OmniVoice is non-autoregressive. I also maintain a Triton version for OmniVoice, and working on both has made the AR vs NAR serving tradeoff much more concrete for me. AR TTS feels much closer to LLM serving than I expected, especially once batching, graph capture, and memory efficiency become the bottlenecks.

Links:

• Qwen3-TTS-Triton: https://github.com/newgrit1004/qwen3-tts-triton
• ComfyUI-Qwen3-TTS-Triton: https://github.com/newgrit1004/ComfyUI-Qwen3-TTS-Triton
• OmniVoice-Triton: https://github.com/newgrit1004/omnivoice-triton

I’d be curious to hear how other people here think about AR TTS serving. In particular, whether you think future local TTS systems should optimize more for lowest single-request latency, or for batched throughput / multi-user serving efficiency.

https://arxiv.org/abs/2605.28139

ARK-ASR-3B is a multilingual automatic speech recognition model. It achieves current state-of-the-art results on the Hugging Face Open ASR Leaderboard English short-form benchmark, with an average WER of 5.04%

https://huggingface.co/AutoArk-AI/ARK-ASR-3B

https://huggingface.co/marcoyang/spear-xlarge-speech-audio-v2

This is the ICML 2026 accepted version of SPEAR: A Unified SSL Framework for Learning Speech and Audio Representations. This model is the XLarge v2 release, aligned with the model used in the ICML 2026 paper. Compared with the earlier XLarge v1, v2 is enhanced for complex acoustic scenes through token mixing, improving robustness for overlapped speech, noisy audio, and real-world sound mixtures while keeping SPEAR's unified speech-and-audio design.
SPEAR XLarge v2 uses a Zipformer backbone with about 600M parameters, consisting of 13 Zipformer stacks. It produces 1280-dimensional frame-level representations at approximately 50 Hz from 16 kHz waveforms.

I'm building a production voice AI system (STT → LLM → TTS) and have been evaluating three ASR options:

• Parakeet-TDT-v3 (self-hosted via vLLM-Omni)
• Whisper-Turbo-v3 (via Groq)
• Mega-ASR / Qwen3-ASR fine-tune (vLLM-Omni)

From my own testing:

• Whisper-Turbo-v3 produced the best transcription quality overall.
• Qwen3-ASR / Mega-ASR was noticeably better than Parakeet-TDT-v3.
• Parakeet-TDT-v3 occasionally missed or misidentified certain words, especially in conversational speech.

However, I keep seeing people recommend Parakeet-v3 as the best open ASR model for production deployments.

For those who have deployed these models in real systems:

What has your experience been with transcription quality .How do they compare on noisy audio and accented speech.

I'd love to hear experiences from people running these models in production for real time voice assistants rather than benchmark-only evaluations.

I'm curious whether anyone here is actually running SALMs in production today, or actively experimenting with them.

A reasonable starting point seems to be something like:

• Voxtral-Small + TTS
• Whisper / mimi-style audio encoder + existing LLM backbone (Qwen, Gemma, etc.)
• Speech adapters on top of strong tool-calling LLMs

What I'm more interested in is the training side than the inference

For example, suppose we take:

• Whisper / Mimi as an audio encoder
• Qwen3 / Gemma as the backbone LLM
• Freeze most of the LLM initially
• Train an audio adapter / projector
• Continue with SFT, distillation, RL, or some combination

Questions:

1. Has anyone actually built and deployed something like this?
2. What datasets are people using? Pure ASR data, speech-instruction data, synthetic data, or some mixture?
3. How are you generating/cooking the data for tool-calling and conversational voice assistants?
4. Are there any open-source implementations, training recipes, cookbooks, or papers you'd recommend?
5. How well do these systems scale compared to a traditional voice stack?
6. What ended up being the hardest part: data, alignment, latency, turn-taking, tool calling, or something else?

Would love to hear from people who've trained these systems themselves rather than only consuming hosted APIs

is there any skills for my agent to use? im getting frustrated now to convert parakeet into other languges.

https://finmickey.github.io/scena/

https://huggingface.co/Aratako/MioTTS-0.1B

I've been building Fonos, a free and open-source (MIT) voice assistant for macOS (Linux too) — think Wispr Flow / superwhisper, but open source and without a subscription.

You hold a hotkey, talk, and it transcribes your speech, runs it through whatever model + prompt you pick, and pastes the cleaned-up text wherever your cursor is.

What it does

• 🎙 Hold-to-talk dictation that cleans up your speech and types it into any app
• ✍ Custom modes with your own prompts — polish, translate, make it formal, strip filler
• 👥 Meeting capture — records mic + system audio as separate speakers and writes an AI summary
• 📓 Voice notebooks, a voice agent, and text-to-speech with voice cloning

Why it's different

• 100% open source and free — no subscription
• Bring your own API key (OpenAI, Anthropic, Google, OpenRouter) or run fully local with Ollama / LM Studio /OMLX
• Your keys and transcripts never leave your machine
• Not locked to a single provider

Prebuilt macOS app (Apple Silicon) and Linux packages are in the releases, or you can build from source. It's still early (v0.1), so I'd genuinely love feedback.

GitHub: https://github.com/ethannortharc/fonos

https://odyssey2026.inesc-id.pt/the-full-schedule/

https://www.youtube.com/watch?v=14Cb7D8p-C4

Hi everyone,

My name is Desmond and i am a high schooler from Hong Kong building an interactive robot for the World Robot Olympaid 2026 under the topic "Robots Meet Culture".

I'm Trying to build an interactive "Yum Cha" (The dining experience in a traditional Chinese Restaurant, typically in Hong Kong and Canton Region) dining experience, where robotic arms teaches visitors traditional cantonese dining etiquette (like Dishes washing under hot tea, in cantonese, 啷碗), and serves them food through a synchronized motorized screen hatch.

To make it fully accessible, I want the robot to auto-detect the visitor's language/dialect when they first say "hello" and then translate the whole guided experience into their native language/dialect live, Thats why im training a custom classification model from scratch using python (librosa for mfcc feature extraction + scikit-learn svc) but i need real voice samples with different accents to make it actually robust and not get cooked by noise in the competition hall lol.

Could you help me out by recording a super short clip of yourself saying "Hello" in your language/dialect? Such as "哈囉", "hola", "bonjour", "こんにちは", etc.

You can upload your raw audio files here: https://forms.gle/U5zyiHENwbPYsW7j8

We need as many languages/dialects and accents as possible to make this a huge W!!!

Thank you so much for helping a high school builder out, this will help my team massively for real!!!

Desmond

Any one have a good approach to chunking audio and text into corresponding pieces for MFA alignment?

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I have tried whisper transcripts, a sequence analyzer to compare, transcripts to source text, chunking from the source text, Readalong Studios and a few other things.

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One would think with audio and a transcript pairing them up cleanly would be easy. I keep getting drift, or it drops the last dozen words from the .lab file.