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Quyashbek/karakalpak-omnivoice

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

Karakalpak OmniVoice - Fine-tuned TTS Model

A high-quality Text-to-Speech model fine-tuned for the Karakalpak language using the powerful OmniVoice architecture.

Model Description

This model was fine-tuned on approximately 6 hours of Karakalpak speech data to support the development of speech technologies for the Karakalpak language (a Turkic language spoken in Uzbekistan and Karakalpakstan).

Key Features

—Supports Karakalpak language with natural prosody
—Built on OmniVoice (Qwen3-0.6B backbone)
—Fine-tuned for voice cloning and high-quality synthesis
—Suitable for education, research, and real-world applications

Training Details

Parameter	Value
Base Model	k2-fsa/OmniVoice
Dataset	~6 hours Karakalpak speech
Training Steps	5,000
Final Eval Loss	4.055
Training Time	~51 minutes
Framework	Accelerate + PyTorch 2.6

Training Infrastructure

—GPUs: 2× NVIDIA L40S (96GB total VRAM)
—Institution: New Uzbekistan University
—Lab: Xalq Banki AI Lab

Usage

python

import soundfile as sf
import torch
import numpy as np
from omnivoice import OmniVoice

# ================== CHANGE ONLY IF NEEDED ==================
model_name = "Quyashbek/karakalpak-omnivoice"   # Your HF model
text = "Algoritm keń mániste tek kompyuterge tiyisli termin bolmay"
# ==========================================================

print("🔄 Loading model from Hugging Face...")
model = OmniVoice.from_pretrained(model_name)

print(f"✅ Model loaded! Sampling rate = {model.sampling_rate} Hz")

print("🎤 Generating speech...")
audio = model.generate(
    text=text,
    speaker="default",
    temperature=0.7,
    top_p=0.9,
    seed=42
)

# Fix audio format
if isinstance(audio, torch.Tensor):
    audio = audio.squeeze().cpu().numpy()

audio = np.asarray(audio).squeeze().astype(np.float32)

# Save
output_file = "karakalpak_output.wav"
sf.write(output_file, audio, model.sampling_rate, subtype='PCM_16')

print(f"✅ Success! Audio saved as → {output_file}")
print(f"   Duration: {len(audio) / model.sampling_rate:.2f} seconds")