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Govtûgô: Automated Multi-Speaker Kurdish ASR Model

سیستەمی خۆکاری دەنگ-بۆ-دەق بە زمانی کوردی (سۆرانی)

Model Summary

Govtûgô-ASR-Sorani is a state-of-the-art Automatic Speech Recognition (ASR) model fine-tuned for Central Kurdish (Sorani / سۆرانی), developed by RegaLabs. Built on the Qwen3-ASR architecture, the model is trained on more than 500+ hours of Kurdish speech datasets to deliver high-accuracy speech transcription across diverse acoustic environments, including multi-speaker meetings, spoken dialogue, clean studio recordings, and telephony audio.

Model Details

Developed by: RegaLabs
Architecture: Qwen3ASRForConditionalGeneration (24 encoder layers, 16 attention heads, 1024 d_model)
Processor Class: Qwen3ASRProcessor (128 mel bins, 16 kHz sampling rate, 50 window size)
Precision / Data Type: bfloat16
Training Dataset: 500+ hours of Central Kurdish speech
Format: SafeTensors standalone model weights
Language: Central Kurdish (Sorani / کوردیی ناوەندی / ckb) only

Intended Uses & Limitations

Primary Intended Uses

Multi-speaker meeting and dialogue transcription in Sorani Kurdish.
Media captioning and automated subtitle generation (SRT / VTT).
Kurdish voice dictation, conversational AI, and audio indexing.

Out-of-Scope & Limitations

Language Scope: This model is designed and trained strictly for Central Kurdish (Sorani / ckb) only. It is not intended for or trained on other languages or non-Sorani dialects (such as Kurmanji, Arabic, Persian, or English).
Extreme Overlapping Speech: Heavy multi-speaker overlap requires upstream speaker diarization (e.g., Pyannote) to partition speech turns prior to inference.

Quickstart / How to Use

The model is natively supported in Hugging Face transformers ($\ge$ 5.16.0).

import torch
import librosa
from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration

model_id = "RegaLabs/Govtugo-ASR-Sorani"

# 1. Load processor and model
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3ASRForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# 2. Load audio and resample to 16 kHz
audio, sr = librosa.load("kurdish_sample.wav", sr=16000)

# 3. Apply conversation chat template
messages = [
    {"role": "system", "content": ""},
    {"role": "user", "content": [{"type": "audio", "audio": audio}]}
]
text_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

# 4. Generate transcription
inputs = processor(text=text_prompt, audios=audio, return_tensors="pt", sampling_rate=16000)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.no_grad():
    generated_ids = model.generate(**inputs, max_new_tokens=256)

# 5. Decode output
transcription = processor.batch_decode(
    generated_ids[:, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)[0]

print("Transcription:", transcription)

Access & Gating Policy

This repository is gated with manual approval:

Click Request Access above.
Provide your name, organization/affiliation, and intended use case.
Access requests are reviewed and approved by the RegaLabs team.

Citation

@misc{govtugo2026kurdish_asr,
  title        = {Govtûgô: Automated Kurdish Speech-to-Text Model},
  author       = {{RegaLabs Team}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/RegaLabs/Govtugo-ASR-Sorani}}
}

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Safetensors

Model size

2B params

Tensor type

BF16