I am stuck. I want to make a local voice gen solution for a project for Indic languages. I tried Parler-TTS, it give Hindi in Bihari accent. I tried Chatterbox: the quality is good but the watermark it gen kills all the pauses in naration. IndicF5 is drunk reverse. My project is not that i record and use later for assembly; it is record, immediately to timeline, marry with graphics and publish video. Can someone suggest a better solution? My volume to voice gen is very high; can’t survive with token purchase so want a local run solution.
You can try this AI Tool
Yes, this is option and many like this. When they shut shop or the lifetime turns into 250k tokens limit per month is the issue. i have come across 2-3 other sol like this. someone suggested coqui xtts, will look at that. thanks for the link.
For local Hindi TTS, your best bets in order of readiness:
Ready to use now: Silero Indic TTS — 9 Indic languages, 17 speakers, runs locally via PyTorch with romanization handled through aksharamukha. Also check Indic-Parler-TTS by ai4bharat — 16 Indian languages, MOS-evaluated by native speakers, solid community backing.
Worth evaluating: XTTS-v2 Hindi fine-tune has improved prosody, but be aware of this open bug where Hindi support breaks on local installs despite working on the demo. Coqui itself has shut down, so upstream fixes are unlikely. Qwen3-TTS is a newer option from Alibaba — fully local, 10 languages, 12Hz audio tokenizer.
For the video pipeline: The Sportmaster Lab dubbing pipeline writeup is the closest reference to what you’re building — covers the full assembly workflow with neural TTS. Yandex’s neural dubbing architecture is worth reading for voice/intonation preservation design.
For benchmarking your choices: This open TTS model survey and NtechLab’s architecture comparison will help you pick between them objectively. Community threads on XTTS hallucination issues (nonsense words, extra syllables) are essential reading before committing.
Pro tip: If you go the XTTS route, train your own voice with this VITS Hindi training repo instead of relying on the broken upstream Hindi config — it uses the Coqui framework but sidesteps the language code bug entirely.
Three failures in three weeks, all on local Indic TTS.
Your stack hit:
Parler — neutral Hindi in, Bihari out
You called it a wall. Said you’re stuck. Fair — but every Indic creator who tested those three hit the same wall in the same order.
It’s a known shape.
Two of those failures have one-line fixes. The third has a one-paragraph fix.
Most of these aren’t broken models. They’re models with rules nobody printed on the box. Like a film camera that “stops working” the second you point it at the sun.
Pick the row that matches your day
Want the Chatterbox quality back, today?
→ Switch to petermg/Chatterbox-TTS-Extended
Community fork built for audiobook-makers. Has a literal “Disable watermarking” toggle in the UI. Your pause problem was the Perth watermark fighting silence frames — flip the switch, it dies. Same model, MIT licensed. Resemble themselves told a user on GitHub cloned-voice use is fine.
Want IndicF5 quality without the drunk output?
→ Read F5-TTS issue #1204
Your reference audio was probably noisy. The issue describes your exact symptom in plain words: “25-second output from an 11-second reference.”
Three rules to fix it:
Record a 10–12 second sample in a quiet room — no fan, no AC
Transcribe what you said literally into the text field
Keep generated chunks under 30 seconds
Don’t want to debug another model?
→ Try Hear2Read NG
Built by the blind-user community. Free. Runs on a potato CPU. Ships Telugu, Hindi, and 9 more Indic languages. Installs as a Windows SAPI voice — any Windows app pipes it to WAV. Pair with Balabolka and it batches whole scripts in seconds.
Won’t sound neural-shiny. Will never glitch.
You’re in AP — you probably need Telugu
All three above ship Telugu (Hear2Read native, IndicF5 native, Chatterbox via the multilingual model).
Indic Parler-TTS also has it. But pin the speaker description hard:
“Anjali speaks with neutral pitch in a clear close-sounding environment.”
Or it falls back to the same Bihari cluster you just escaped.
What I actually run — IndicF5 and Chatterbox-Extended in parallel. F5 for clean output when I have time to record a fresh reference. Extended for batch days when I drop a 30-min script and walk away.
The closet thing is real. First time I tried F5 at a desk with the laptop fan in the room, got the exact drunk output you described. Closet, USB mic, water bottle on the floor for sound diffusion. Reads 30 minutes without drift now.
🎭 The deeper kit — open if you want options past the top three
Bob-Ross note: every model below can also fail the way your original three did. Every failure has a one-line cause. You’ll learn faster by trying one and watching it break than by reading reviews.
ai4bharat/IndicF5 — Telugu, Hindi, 9 more. The native option.
Drunk-output cause: noisy reference. Fix: clean closet recording + literal transcript.
Veena by Maya Research — India’s first SOTA Hindi+English TTS.
4 voice tokens (
<spk_kavya>,<spk_agastya>,<spk_maitri>,<spk_vinaya>). 4-bit quantization runs fine on a 12GB card. Telugu’s on the roadmap, not shipped yet. ONNX + GGUF builds exist for CPU. Apache 2.0.
svara-TTS — newest entry, October 2025.
19 Indian languages including Telugu. Emotion tags:
<happy><sad><anger><fear>. Zero-shot cloning, code-switching aware. Built on Orpheus.
The RVC sidestep — Applio
Record yourself reading the script badly in any quality. Convert your timbre to a target voice. The pauses come from your breathing — there’s nothing for a TTS decoder to hallucinate. This is how the AI-cover scene has been shipping thousands of hours of vocal content for two years.
RTX 20-series + 8GB for training your own voice. CPU + 4GB for inference using someone else’s. Built-in realtime mode + a TTS-then-RVC chain inside the same app.
🪤 The unsolicited price check — open only if your volume math interests you
You said volume is high. Worth a quick reality check before another weekend goes into debugging.
Sarvam Bulbul v3 — India’s voice AI lab.
Pricing: ₹30 per 10,000 characters
| Volume | Cost |
|---|---|
| One 10-min Telugu script (~8K chars) | ~₹30 (~$0.36) |
| 1–3 videos daily, monthly total | ~₹900 |
| Free credits on signup | ₹1,000 |
Now compare:
RTX 3060 drawing ~170W × 8 hr/day at Indian electricity rates ≈ ₹325/month just for power
Plus debugging hours
Plus model-hopping weekends
The honest split:
Local wins on privacy and infinite scale.
API wins on time-to-publish.
Worth running the math against your actual publishing volume before committing to either.
Three different bugs. Three different fixes.
The wall was always made of paper.
!