import librosa
import torch
from transformers import Wav2Vec2ForCTC, AutoProcessor

processor = AutoProcessor.from_pretrained("derkar00/mms-tuk-latin-asr")
model = Wav2Vec2ForCTC.from_pretrained("derkar00/mms-tuk-latin-asr")

processor.tokenizer.set_target_lang("tuk-script_latin")
model.load_adapter("tuk-script_latin")

# Load audio (must be 16kHz mono)
audio, rate = librosa.load("your_audio.wav", sr=16000, mono=True)

inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_ids = torch.argmax(logits, dim=-1)
print(processor.decode(predicted_ids[0]))

Language

Language: Turkmen (tuk)
Script: Latin
Task: Automatic Speech Recognition

Limitations

Accuracy may vary on noisy audio or strong accents
Turkmen is a low-resource language so training data is limited
Works best on clean, clear speech

derkar00/mms-tuk-latin-asr

MMS Turkmen ASR (Latin Script)

Usage

Language

Limitations

Base Model

derkar00/
mms-tuk-latin-asr