SPRING_F5: Fine-tuned F5-TTS for 23 Indian Languages & English

SPRING_F5 is a multilingual text-to-speech (TTS) model based on F5-TTS, fine-tuned to support 23 Indian Language & English.

Supported Languages

SPRING_F5 supports the following 24 languages:

• Assamese, Bengali, Bodo, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Punjabi, Sanskrit, Santali, Sindhi, Tamil, Telugu, Urdu, Dogri, Rajasthani and English

🚀 Installation

We recommend using a dedicated Conda environment.

conda create -n springf5 python=3.10 -y
conda activate springf5
pip install git+https://github.com/ArigalaAdarsh/SPRING_F5.git

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

# Load INF5 from Hugging Face
repo_id = "SPRINGLab/SPRING_F5"
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)

# Generate speech
audio = model(" 2026 లో ఈ project లో మా ultimate goal ఏంటంటే, ఒక Telugu speaker తన daily conversation లో naturally English words ఉపయోగించినప్పుడు, మన T T S system కూడా అదే style ని understand చేసి, unnecessary pauses లేకుండా చాలా smooth మరియు natural speech ని generate చేయాలి.", 
            ref_audio_path="prompt_audios/example1_te.wav",
            ref_text="వందేభారత్ రైలు ఆధునిక భారతదేశం యొక్క వైభవోపేతమైన చిత్రాలలో ఒకటిగా ఉంది",
            lang='te'  # Language ID is used for number-to-Indic word conversion.
                  )

# Normalize and save output
if audio.dtype == np.int16:
    audio = audio.astype(np.float32) / 32768.0
sf.write("generated_audios/outputs/example.wav", np.array(audio, dtype=np.float32), samplerate=24000)

Training Details

SPRING_F5 was trained using 2 × NVIDIA H200 GPUs for approximately two weeks.

We would like to sincerely thank C-DAC for providing the computational resources required to train this model.

We used the F5-TTS Base configuration, with approximately 330M parameters, following the model configuration described in the original F5-TTS work.

Training Data

The model was trained on approximately 3,220 hours of high-quality speech data collected from the following datasets:

• Rasa
• IndicTTS
• IndicVoices-R

These datasets provide diverse multilingual speech data covering the Indian languages supported by SPRING_F5.

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References

We would like to express our sincere gratitude to the authors and contributors of F5-TTS for their valuable contributions to text-to-speech research and for providing the foundation for this work.

SPRING_F5 builds upon the F5-TTS architecture and training methodology and extends it toward multilingual speech synthesis for Indian languages.

@misc{spring_f5_2026,
  author       = {Adarsh Arigala},
  title        = {SPRING_F5},
  year         = {2026},
  url          = {https://github.com/arigalaadarsh/SPRING_F5},
}