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RsGoksel_ITU_Mainframe-balanced-ct2
RsGoksel/RsGoksel_ITU_Mainframe-balanced-ct2
RsGoksel_ITU_Mainframe-balanced-ct2 is a automatic speech recognition model from RsGoksel. Use it when you need speech turned into text. It is set up for ctranslate2. The card lists the license as mit.
Türkçe ASR — openai/whisper-large-v3-turbo üzerine LoRA fine-tune. Çok-domain dengeli all-rounder (clean + robustluk + medikal birlikte korunur). CTranslate2 (float16) derlemesi — faster-whisper / WhisperX ile hızlı ç…
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At a glance
- Task
- Automatic Speech Recognition
- Library
- ctranslate2
- License
- mit
- Access
- Public
- Created
- Jun 29, 2026
- Updated
- Jun 29, 2026
- SHA
- f0e3f494
Base models
Model card
From the Hugging Face model README
RsGoksel_ITU_Mainframe-balanced-ct2
Türkçe ASR — openai/whisper-large-v3-turbo üzerine LoRA fine-tune. Çok-domain dengeli all-rounder (clean + robustluk + medikal birlikte korunur).
CTranslate2 (float16) derlemesi — faster-whisper / WhisperX ile hızlı çıkarım.
Hızlı kullanım — faster-whisper
from faster_whisper import WhisperModel
model = WhisperModel("RsGoksel/RsGoksel_ITU_Mainframe-balanced-ct2", device="cuda", compute_type="float16")
segments, _ = model.transcribe("audio.wav", language="tr",
condition_on_previous_text=False, beam_size=5)
text = " ".join(s.text for s in segments).strip()
WhisperX ile
import whisperx
m = whisperx.load_model("RsGoksel/RsGoksel_ITU_Mainframe-balanced-ct2", device="cuda", compute_type="float16",
language="tr", asr_options={"condition_on_previous_text": False})
res = m.transcribe("audio.wav", language="tr")
text = " ".join(s["text"] for s in res["segments"])
Önerilen: language="tr", condition_on_previous_text=False.
Metrik: Türkçe normalizasyon sonrası mean-per-utterance WER.
Tam-hassasiyet (transformers) sürümü: RsGoksel/RsGoksel_ITU_Mainframe-balanced.
- Task
- Automatic Speech Recognition
- Library
- ctranslate2
- License
- mit
- Languages
- tr
- Created
- Jun 29, 2026
- Updated
- Jun 29, 2026