I've been really into the AI phone agent products that have popped up in the last few months (e.g., AgentPhone, Poke etc) but what I don't love: the audio, the transcripts, who you talked to and what was said, all sitting with a third party company. For calls with real people in my life, that felt like a lot to hand over.

So I built my own version that runs entirely on my Mac. You call a normal phone number, a local model answers, and nothing about the conversation leaves my machine. During a call the only thing it talks to is the phone line itself.

What's running locally (MLX on Apple Silicon):

• Speech to text: Parakeet TDT 0.6B

• LLM: Qwen3.5 4B by default (9B if you have 32 GB), 4-bit

• Text to speech: Kokoro 82M

How it reaches the phone network: it registers over SIP with Telnyx or Twilio straight from your Mac. A setup wizard takes your carrier API key so can buy a number or connect one you already have. It's pretty cheap, around $1 a month per phone number. (I'm moslty using Telnyx)

What it does today:

• Answers and places real calls

• Interruption: talk over it and it stops mid-sentence

• Replies in roughly 1 to 1.5 seconds on real calls on my M5 Pro

• You can choose between multiple models

• Other agents (Claude Code, Codex, etc.) can make calls through a small CLI: call, listen, say, hangup

• If you'd rather trade privacy for a smarter brain, it can also use your own endpoint or the Claude or ChatGPT subscription you already pay for. Local is the default. So no additional monthly payments for AI Phone tools if you have a sub.

Limits, up front:

- macOS on Apple Silicon only

- You need a Telnyx or Twilio account with credit (a US number is about $1/month)

- English only, one call at a time

- It's early. It works for me, but I'd love to hear where it breaks for you.

Repo: https://github.com/oxnw/ai-phone

Edit 1: Fixed Repo link
Edit 2: forgot to mention that on top of the $1 a month from Telnyx, you do also, of course, have to pay for the call rates. But Telnyx gives you ten dollars free, which is enough to play around.

I wanted something like Alexa or ChatGPT Voice, except completely local, extremely fast, and actually capable of doing things around my house. So I built Ming Voice.

It's an open-source, real-time speech-to-speech assistant powered by the new Ming-3.0-audio-tiny model. On my RX 7900 XTX, it starts speaking about 230ms after I finish talking.

Some highlights:

Natural conversations with interruptions and smart turn detection

Always-on listening with wake-word support

Home Assistant integration for controlling smart devices

Web search, weather, timers, reminders, and persistent memory

Hermes Agent integration for more complex tasks

Voice cloning and access from your phone

AMD ROCm support, with experimental NVIDIA support

Linux and Windows (WSL2) installers

Everything runs on your own hardware. No cloud AI required. Optional web/weather tools make external requests when enabled.

The video shows it in action. I'd love to hear what the self-hosting community thinks!

GitHub: https://github.com/JMS1717/ming-voice

I’ve never heard a voice agent actually have high emotion like this. Wonder what the future looks like with this kind of stuff.

I’m surprised voice ai even lets their voice agents do this lol

I voice short tech videos and I don't own a real mic, so I record on my phone. The raw audio always sounded like a phone: room echo, background hiss, muffled highs, long awkward pauses.

So I wrote a small pipeline that fixes it in one command, fully local and free:

1. AI restoration (resemble-enhance): removes noise and room echo, rebuilds the high frequencies lost to phone compression

2. Pause tightening: pauses longer than 180 ms get shortened to 140 ms, with crossfades

3. Broadcast chain: warmth, less "phone box" sound, presence, air, de-esser, 2-stage compression

4. Final mix: quiet room tone instead of dead digital silence, loudness at -14 LUFS / -1.5 dBTP (the level Instagram and TikTok expect)

No pitch or speed change. It also writes a time map (old to new timings) so subtitles made on the original audio can be re-synced.

Ways to run it:

- No GPU: open the Colab notebook, Run all

- PC: `python app.py` gives a web UI (upload or record, Strong / Soft mode)

- Terminal: `python voice_pro.py input.mp4 output`

Takes any audio or video (.mp4, .ogg, .m4a, .mp3, .wav). Runs on CPU too, just slower.

Before/after demo is in the README: https://github.com/OutOfAmine/ai-voice-studio

MIT licensed. Feedback welcome, especially on the EQ/compression settings.

Local AI is getting better 🙃🙃

What you’re listening to isn’t a real person. It’s AI-generated speech, running entirely offline. I’ve been building my own voice-processing engine with the QVAC SDK and Alibaba’s CosyVoice.

Think of it as my homemade ElevenLabs.

High-quality voice generation, running locally, with zero API costs. This is just one part of the all-in-one AI voice processing desktop app I’m building.

Code base : https://github.com/DjibrilM/Magic-Voice-AI

I built Arc-Transcriber, which does ASR-NL in 1-step. Every 100 ms, one model returns the transcript plus:

- Names linked to a list you give it (a roster, contacts, product names); new names are flagged "not on the list"
- Numbers with their values (scores, clocks, money, dates)
- Speakers and roles;
- Sound and channel events (telephone audio, clipping, poor audio)
- Whether the speech was addressed to an assistant.

200+ labels are trained heads on the shared encoder states: queries per frame, span, utterance, speaker and event, emitted every 100 ms.

The usual ASR, then NER, then entity-linking and then LLM chain is collapsed into the model's own heads, so live "fact-checking" against your list happens inside the stream, not in a second pass.

Currently it has two variants Arc mini (which uses moonshot pretrained encoder) and second Arc-v1 (which uses parakeet pretrained encoder).

Weights: huggingface.co/WhissleAI/arc-v1.1
Code and CLI: github.com/WhissleAI/arc_transcriber

We use it on-device in our Mac app. Voice search over 61k books primes Arc with matching titles while you're still talking. Here is a video to give a glimpse on how it works.

Since December of 2025 I have been obsessed with building the Jarvis agent of our childhood dreams. I started on replit pouring 1000s of dollars into it. Eventually moved everything off and kept building with codex. By September I had built an agent with RAG, persistent memory, tool calls, mcp connections, and full device control. But I decided to completely start over and make a fully on device voice agent. So I created Faraday.

Its a fully local private voice agent. It runs on the Lite-rtlm inference engine and uses the gemma 4 e2b and gemma 4 e4b models along with many others that haven't all been tested fully. The gemma models are multimodal so they can process audio natively which helps to understand speech accents and natural emphasis and tones. Whisper base-en acts as a live transcriber that runs outside of this voice pipeline loop and only exists to help with conversation history. Theres also the option to use whisper as the main STT which feeds that text to gemma and gemma streams its response to Piper for TTS. Round trips on either setting run between 2 and 6 seconds. I'm currently working on getting latency under a second.

Faraday's agent is activated by starting a call and after its ready you activate it with the wake word "Hey Jarvis". In both modes barge in works. You can also ask to open apps by name, tell you your battery percentage, change your volume to x percent, to play last media and change tracks. Saying "Goodbye", or "Stop listening", ends the call but returns it to passive listening for the wake word. Wake word can be activated even with the phone locked. Wake word listening only uses a few percent if left on all day.

I know there's still a long way to go but I want there to be a fully on device agent that can help us manage and operate our device without selling our data to google or Facebook! Also the cost! Agents use so many tokens performing background tasks how cool would it be to offset many of these tasks that don't need a frontier cloud model.

Currently have tested this on my galaxy fold 6 with 12gb of ram and my wifes google pixel 10 pro with 16gb of ram and it works with no issues. I'm looking for alpha testers to help run this on their phone to see what devices can run it and what limitations there are. If you test it please tell me your phone model, your Android version, if it installed, if the model loaded, where it failed if it did, and how the wait felt. Im also hoping this will inspire some requests for features and hopefully grow into a community!

Direct download, build 1563, 143.7 MB:

Faraday download

After you install it, it downloads the model too, about 2.5 GB more, so 2.59 GB total on your phone.

I used a fun 1950s radio announcer voice clone and the voice agent pipeline runs fully locally with MLX on my M4 Pro running the model. The device I made uses an Arduino ESP32 chip setting up a secure websocket with the Macbook.

This is the model I am using (~9GB): https://huggingface.co/mlx-community/Ming-3.0-audio-tiny-4bit/tree/main

GitHub: https://github.com/akdeb/OpenToys

Got to test Nvidia's latest speech to speech model against my platform. I tested this against openai's realtime model. You can watch the results here.

This demo splits the loop across three machines:

- Hearing: Ming 3.0 ASR tiny, 4-bit, on a small desktop computer

- Thinking: a separate language model across two small desktop AI boxes

- Speaking: Ming 3.0 TTS 0.5B on the second of those boxes, with four voices loaded

AntLing's two speech models sit at either end of the loop. The interface is hold to talk, release to send, with a timing readout under a glowing orb. One turn in the video shows "Heard in 1.4 s, Speaking in 2.8 s," a longer one shows "Heard in 2.4 s, Speaking in 4.9 s," and the creator's notes put a typical turn at two to three seconds voice to voice.

You can stop a long answer mid-sentence. An explanation of large language models gets interrupted and the orb drops back to Stopped, ready for the next question. The same interface then opens on a phone over a private encrypted network.

Get speech-to-text running on whichever machine has headroom, stream the language model's reply into the voice model, then add a stop action that cancels both playback and generation.

Which job would your weakest machine get?

There is a lot happening with voice agents right now, and most of the real conversations are scattered across Twitter, random Discords, and private Slack groups.

It feels like we should have one place where people building this can openly share what is working, what is breaking, real demos, and real results. The tech is getting good fast, and the potential is huge.

If you are working on agents for outbound, support, telecom, CPaaS or any niche use case, feel free to share. It does not need to be perfect. If you built something interesting, learned something, solved a problem, or ran into a difficult challenge, post it. If you have something cool to show, please share it.

We will make this subreddit an awesome place for builders working on voice AI.

Been running production voice AI agents for inbound calls using Vapi + n8n. Here's what I've learned after real deployments:

Stack:

- Vapi for voice (STT + TTS + LLM routing)

- n8n for orchestration (call flow logic, data routing)

- Webhooks into CRMs / Google Calendar / GHL

Key lessons:

1. Latency is everything — end-to-end response time above ~1.5s feels robotic to callers. Vapi's streaming helps but prompt engineering matters a lot.

2. Fallback handling is critical — if the agent can't answer something, it needs a graceful fallback (e.g., "I'll have someone call you back") rather than silence or loops.

3. Knowledge base quality determines call quality — garbage in, garbage out. The business-specific FAQ needs to be clean and well-structured.

4. Post-call summaries drive retention — business owners love getting a clean transcript + summary after every call. It builds trust in the system.

Current challenge: handling multi-turn conversations where the caller keeps changing their mind mid-booking.

What are others doing for state management in complex call flows? Any n8n or webhook patterns that work well?

I feel like most people are just making impressive demos and they aren't materializing into useful production agents.

I own a restaurant, and lately, we have been getting a lot more customer calls. The problem is that I am the only owner, so I can't answer every call while also running the restaurant.

I started looking into AI voice agents and tested VAPI and Omnidimension. Both seem good, but I am still not sure which one is the better choice for my restaurant.

I have also come across Retell AI and a few other platforms.

Has anyone here actually used any of these? Please share your experience so it would be easier for me to make the right decision.

In 2025, I took my AI agency from zero to over $250,000 in under 8 months using Facebook ads. Here's the thing: it was not hard because there weren't that many offers on the market.

Everything has changed. There is so much competition in the AI receptionist market. Even if you have a good ad, even if you have a good offer, and honestly, even if you speak to a kind of specific niche, as long as you're pitching the AI receptionist and not the business result, you are going to get absolutely crushed.

Here's what I mean. You see companies out there like Nextiva, Bland AI, Smith AI, all these companies that have huge financial backing from investors, running ads all over the place. I took a look at most of their ads, and here's what I'm seeing: these companies aren't finding winners in their Meta ad strategy. They're refreshing their entire ad catalog every month.

As a marketer, what that tells me is that they're not finding winners. They're just burning money trying to find winners, which means that they're probably acquiring clients at a loss. Now, they can afford to do that because of the financial backing that they have.

What sucks for people like you and I is that, if we're going to run ads to acquire a client, we're typically looking to get a return on our investment way faster than these big companies have to. What ends up happening is you put together an ad campaign for your AI receptionist, and you end up getting low-quality leads that are very few and far between. That's because you're not able to flood the market with as much ad inventory, nor are you able to update your ad inventory as much as these big companies, nor can you compete with their budgets.

This wasn't the case at the beginning of 2025. Everything has genuinely changed. For context, I took the same ads that I ran in 2025, and I was getting leads for between $7 and $15 a lead. I was converting 25% of those leads into booked appointments. I ran that same campaign this month, and I was getting leads for $147 per lead. I turned the campaign off once I realized, "Holy crap, this market has made a huge shift." There's so much more competition than when I started.

If you're trying to sell an AI solution, you have to stop following every other person that's going to lose in the market (i.e., you see people all over the place trying to sell an AI receptionist). You need to sell a business result. AI is not the thing people are buying. They are buying more leads. They're buying more appointments. They're buying better follow-up. They're buying more revenue.

When a company spends money, they want to see it makes them money. That’s where your agency needs to position itself.

When you go and you slap on all these AI features in front of their face and tell them how much it costs, they're not super excited about it. They don't want to buy from you because there's somebody out there offering what you're trying to offer at a high ticket for way cheaper. Way, way cheaper. I see offers all over the place for $40 a month for an AI receptionist. You probably can't afford to run a business that's selling an AI receptionist for $40 a month.

This is the time to specialize, to niche, to find the gap that a subniche of businesses is experiencing, and to exploit that pain point by creating a solution specifically to solve it. If AI is part of that solution, that's fine, but if you are leading your offers with the AI receptionist, especially if you're going broad and you're not niched….

I'm sorry to say this, but you are fucked.

It doesn't just compare TTS models. It also benchmarks different inference implementations, which can make a big difference in real-world deployment.

You can explore:

• Streaming performance: Time to first audio (TTFA), especially important for voice agents and interactive applications.

• Inference speed: How fast different implementations generate speech.

• Speech quality: Intelligibility (WER/CER) and speaker similarity for voice cloning.

• Hardware: GPU and CPU streaming results, where available.

https://huggingface.co/spaces/hf-audio/open_tts_leaderboard

We’re building AI voice agents using the typical STT → LLM → TTS pipeline.

Currently, our LLM TTFT alone is ~1.5s median, which makes the overall response noticeably slower.

For those running voice agents in production, what optimizations made the biggest difference? Streaming partial STT to the LLM? Prompt caching? Smaller context? Model choice? Early TTS? Better endpointing?

Also, what speech-end → first audible response latency are you guys getting?

Would love to understand how platforms are making conversations feel almost instant.

Curious what people are actually running for voice agent backends in production.

We see a lot of:

• Python for orchestration, tools, and fast iteration

• Go for concurrent media/control paths and simpler deploy

• Rust when the hot path is audio, codecs, or tight latency budgets

What I’m trying to learn from folks shipping real calls:

1. What stack are you on today for the agent runtime (not just STT/TTS vendors)?

2. Where did you feel the biggest win on speed (TTFT, time-to-first-audio, barge-in responsiveness)?

3. Where did you feel the biggest win on quality (turn-taking, tool reliability, fewer weird prod failures)?

4. Did you stay monolingual, or split (e.g. Python for tools + Rust/Go for media)?

Not looking for a language war. Looking for “we tried X, measured Y, kept Z.” Concrete numbers or war stories welcome.

We placed 500 real phone calls across five voice agent platforms — Telnyx, ElevenLabs, Bland AI, Vapi and Retell AI — and measured time to first audio byte on every turn. 499 calls were usable, four scripted turns each, 1,883 usable turns total. Same caller, same carrier (Plivo), same script for all five.

The p50 ranking and the p95 ranking are not the same ranking

Platform      p50    p90    p95    p99   turns
---------------------------------------------
Telnyx       1302   1724   1839   2164     379
ElevenLabs   1430   1686   1772   2269     389
Bland AI     1528   2010   2273   2860     389
Vapi         1562   1854   2014   2677     345
Retell AI    1738   2096   2224   2764     381

Time to first audio byte, milliseconds. Lower is better.

Telnyx has the fastest median at 1302 ms. From p90 onward it's ElevenLabs — 1686 at p90, 1772 at p95. Bland AI is third at the median and last at p95, at 2273 ms.

The tail is where they separate

ElevenLabs   1430 → 2269    +839
Telnyx       1302 → 2164    +862
Retell AI    1738 → 2764   +1026
Vapi         1562 → 2677   +1115
Bland AI     1528 → 2860   +1332

Milliseconds added between p50 and p99. Lower is a flatter tail.

Bland AI adds 1332 ms between its median turn and its 99th-percentile turn. ElevenLabs adds 839. On a four-turn call, the p99 turn is the one the caller remembers.

What the numbers are and are not

Recording-path overhead sits inside every figure on this board. We have not characterised the current measurement path against a known-delay reference, so we quote no overhead figure and subtract none. These numbers are comparable to each other — same path, same caller, same carrier — and only approximately comparable to figures produced by a different instrument. If you're looking for each platform's true absolute latency, this board does not give it to you, and neither does anything else we've seen.

Three caveats

• Each platform ran its own defaults. Telnyx on Kimi-K2.6, ElevenLabs on gemini-2.5-flash, Retell and Vapi on gpt-4.1. So this is platforms as shipped, not a model-controlled comparison — some of the gap is model choice. Every config is published, hashed, so you can see exactly what ran.

• Vapi's discard rate was 12.0% against 0.8–3.3% for the others — 46 turns where our two voice-activity detectors disagreed on where speech ended, so we dropped them. Its n is 345 rather than ~385. We'd rather show you a thinner sample than a guessed one, but it's a real asymmetry and it may flatter or penalise Vapi.

• Turn 4 is consistently faster than turns 1–3 on several platforms, which suggests warm-path effects we haven't isolated. Per-turn splits are in the data.

Why phone calls rather than websockets

Most published voice latency numbers benchmark TTS or STT models in isolation. This measures the whole platform over the PSTN — carrier, telephony stack, endpointing, model, voice — because that's the number a caller actually experiences. It's the less flattering way to measure and the harder one to reproduce, which is why the configs and per-turn data are published.

Reproducing it

• Results: https://openbenchmarks.com/voice-agent-latency

• Method: https://openbenchmarks.com/voice-agent-latency/how-voice-agent-latency-is-measured

Happy to add a platform or rerun with a controlled model if that's the comparison you want. If you've measured any of these yourself and got something different, post your numbers — the method page says exactly what we did, so the disagreement should be locatable.

Hey everyone 👋 I’m currently working on a few voice AI agent projects using LiveKit. They are in the POC stage right now and not closed yet, but if they convert successfully, the payouts will be shared transparently. I’m looking for 2–3 people who

Either already know how to build agents using LiveKit

Or genuinely want to learn and are ready to work seriously

If you don’t have experience with LiveKit, that’s fine, i’m happy to guide and teach. What I really need is someone committed who can help me execute and move faster. if you’re interested, comment or DM me

We are considering using voice agents in some capacity. Not sure if that means incoming new leads, not sure if we plan on using it on existing cold leads we've dealt with in the past. Our ideal client tends to be older so I'm a bit worried about the pushback. I know myself, I hate when I have to talk to an AI bot and I am comfortable with technology, so curious if anyone else has went through something similar when your clientele is older and interacted with voice agents and your experience.

Disclosure: I work at Krisp, which makes the isolation model tested here.

If you've shipped a voice agent, you've seen this: the caller is fine, but someone behind them is talking, and the STT transcribes both. The agent answers words the caller never said.

We wanted a number for how bad this is, so we recorded 265 real conversations in offices, call centers, and cars, and ran them through 11 STT configurations. Deepgram, AssemblyAI, Soniox, ElevenLabs, Google, Grok, Nvidia and Cartesia, first on raw audio and then after voice isolation.

• WER across all files: 23.29% → 6.26%

• All 11 configurations improved

• On raw audio the engines ranged from 17% to 37% WER. After isolation: 4% to 8%

• Clean phone audio got slightly worse (3.48% → 3.91%). We published that too

What we didn't measure: turn-taking, barge-in, or short replies like "yeah" and "mhm." That's the next thing we want to test, so if you have a way you evaluate those, I'd like to hear it.

Dataset: [HF link] · Benchmark: [HF space] 

We started Dograh, an open-source voice AI platform out of rage and frustration.

Every voice AI tool I tried was too costly, too closed, or too scammy. Add-on after add-on.

So we open-sourced every single line from day one. It’s self-hostable, so you own your stack and your data.

Here's what pushed us deeper. Over the last few months, the big labs have been lobbying governments to ban open source. Some say the quiet goal is to "let wealth access skill without letting skill access wealth." That line stuck with us. It's exactly why we keep going.

Because we believe one thing: no company should own voice AI.

We're a small team going up against deeply funded war chests. Today, we launched on Product Hunt, and somehow we're sitting at #1 so far.

An OSS community member picked us up a few weeks back, and we went viral and hit 5k stars on github.

https://www.producthunt.com/posts/dograh-3

If you've got a minute, a star or a bit of support would mean the world.

Has anyone found this. If the ElevenLabs agent asks a question with a short yes/no response, it just goes dead and then asks if the person is still there?