How to Run IndicF5 Locally: Voice Cloning for 11 Indian Languages

IndicF5 is a 0.35 billion parameter open weight text-to-speech model from AI4Bharat, released March 11, 2025. It clones a voice from a short reference recording and its transcript, then speaks new text in that voice across 11 Indian languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu. The weights are MIT licensed and install with a single pip command.

Written by Priya Raghunathan, local-AI hardware reviewer. I check parameter counts and hardware requirements against the primary source before recommending an install path.

A new top-20 entrant on Hugging Face's trending list

IndicF5 entered the top 20 of Hugging Face's text-to-speech trending list. At time of writing it has 221 likes and 27,392 downloads on the Hugging Face repo, plus 135 stars on the AI4Bharat/IndicF5 GitHub repo it installs from and a demo Space, ai4bharat/IndicF5, with 43 likes if you want to hear a sample before installing anything.

Specs at a glance

Hardware: what 0.35B parameters actually costs you

AI4Bharat hasn't published a VRAM table for IndicF5, so the honest way to size hardware is to work from the weights themselves. The safetensors checkpoint stores its 350.7 million parameters in F32, which puts the weights at about 1.4GB (four bytes per parameter). That's one of the smallest models we've covered here: a 4GB GPU has room to spare, and because the checkpoint is small enough to sit comfortably in system RAM, CPU inference is realistic too if you only need occasional clips rather than a live pipeline.

If you're deciding what to build a local-AI box around, see our guide on setting up local AI on 8GB of VRAM, a tier IndicF5 runs on with plenty of headroom for other models alongside it.

How voice cloning works here

IndicF5 is built on F5-TTS and works the way most open voice-cloning models do: you give it a short reference clip, the exact transcript of what's said in that clip, and the new text you want spoken. The model copies the reference voice's timbre and delivers the new text in it. There's no text-only mode and no pre-built speaker list, the reference clip is what defines the voice for that generation.

What sets it apart from most cloning models covered here is language coverage. It was trained on 1,417 hours of speech across the Rasa, IndicTTS, LIMMITS, and IndicVoices-R datasets, and handles 11 Indian languages in one checkpoint rather than treating them as an afterthought bolted onto an English-first model.

Install it

IndicF5 installs straight from its GitHub repo with pip, inside an isolated conda environment.

Generate your first clip

Load the model through transformers with trust_remote_code enabled, then call it with the text to speak plus a reference clip and its transcript. IndicF5 hands back raw audio at 24,000Hz.

from transformers import AutoModel
import numpy as np
import soundfile as sf

model = AutoModel.from_pretrained("ai4bharat/IndicF5", trust_remote_code=True)

audio = model(
    "नमस्ते, आप कैसे हैं?",
    ref_audio_path="reference.wav",
    ref_text="This is the exact transcript of reference.wav.",
)

if audio.dtype == np.int16:
    audio = audio.astype(np.float32) / 32768.0
sf.write("output.wav", audio, samplerate=24000)

FAQ

What is IndicF5 used for?

Cloning a voice from a short reference clip and speaking new text in that voice, across 11 Indian languages in one checkpoint. It's built for dubbing, narration, and accessibility work where the target language is Indian rather than English.

How many parameters does it have?

0.35 billion parameters, per the safetensors metadata in the Hugging Face API. The F32 checkpoint totals roughly 1.4GB.

What GPU do I need to run it?

A 4GB card covers it comfortably. At 350 million parameters the checkpoint is small enough that CPU inference is realistic too for occasional use, though AI4Bharat hasn't published an official VRAM figure.

Which languages does it support?

Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu, all from the same checkpoint.

Can I use it commercially?

Yes. Both the code and the model weights are licensed under MIT, with no separate paid tier required for commercial use.

Where to go from here

• Building a local-AI box around a small model like this? How to set up local AI on 8GB of VRAM covers a hardware tier IndicF5 runs on with room to spare.
• Need cloning without the Indian-language focus? Run Breeze TTS 2 locally covers another open cloning model, and run Kokoro-82M locally covers a smaller, cheaper-to-run alternative.
• Want to compare it against other open TTS models first? Browse the local AI model directory to see specs side by side before you commit disk space to a download.