ufakzeka-kulak

ufakzeka-kulak ("kulak" is Turkish for ear) is a small Turkish speech-to-text model by ufak AI, trained from scratch and released under Apache-2.0. It runs offline on a CPU, and the same weights ship for NeMo, transformers, sherpa-onnx, parakeet.cpp (GGUF) and parakeet-mlx (MLX).

Read this before you try it

The model is meant for transcribing Turkish speech offline on a laptop, a server or in a browser page, where a licence that allows commercial use and a known list of training sources matter. Identifying, tracking or profiling the people who speak is out of scope, and so is acting on a transcript about a person without someone checking it.

Limits:

• Turkish only. Other languages, and Turkish mixed with them, were not measured.
• One utterance at a time. The released model is not a streaming model. Cut long recordings into utterances with voice activity detection and keep a few hundred milliseconds of audio around each one, as the demo does; a segment cut tight to the speech can come out without capitals or punctuation.
• The model does not say who is speaking. Word timestamps were not evaluated.
• Numbers come out as words. An application that needs digits must convert them.
• Commas are often missed, with a comma recall of 0.359 on FLEURS Turkish test and 0.612 on Common Voice 27.0 Turkish test, against 0.839 and 0.939 for full stops.
• Conversational speech is much harder. On Real-TurnTurk, a conversational test set with 6 speakers, normalised WER is 0.3807, against 0.0921 on FLEURS Turkish test; with so few speakers its interval is wide.
• Most of the training text of the YODAS recordings (4,326.6 of 4,696.2 hours) was written by one speech recogniser and checked by another, wherever no uploader subtitle agreed with that transcript (see Training data). Errors on which the two agreed remain in the training text.
• Accuracy by speaker gender, age or regional accent was not measured.
• Each runtime has its own limits, listed under Known limitations.

Model summary

Output. The model writes Turkish with capital letters, full stops, commas, question marks, apostrophes and hyphens inside words. Other punctuation in the training text was mapped to these (an exclamation mark to a full stop, a semicolon or a colon to a comma). Numbers are written as words, so the model writes "iki bin yirmi ikide", not 2022'de. Circumflex letters are written without the circumflex.

Files and how to run them

"Checked" means that the file was decoded on Common Voice dev and FLEURS Turkish dev and its normalised WER stayed within 0.0050 of NeMo's on both sets, with no failed utterance; a reduced copy (int8, q8_0, or the MLX weights run at bfloat16) was also held to its full-precision copy by the same margin. The sherpa-onnx, parakeet.cpp and parakeet-mlx checks ran under Linux x86-64; parakeet.cpp and parakeet-mlx ran on the CPU. One SHA256SUMS file at the top level lists every file of the release, LICENSE and ATTRIBUTION.md included, except this card, SHA256SUMS itself and the Hub's .gitattributes. Add "SHA256SUMS" to the allow_patterns below, then sha256sum -c --ignore-missing SHA256SUMS (GNU coreutils) in the download folder checks the files you fetched.

Everything is in this one repo. The NeMo file sits at the top level, where NeMo's from_pretrained looks for it, and NeMo downloads only that file; each other runtime has its own folder, and the examples below download only the folder they use.

The examples below read speech.wav, one utterance of mono audio at 16,000 Hz. The install lines name the versions the files were checked with.

NeMo

The checkpoint holds both the TDT decoder and the CTC head; TDT is the default.

pip install "nemo_toolkit[asr]==3.0.0"

from nemo.collections.asr.models import ASRModel

model = ASRModel.from_pretrained("ufakai/ufakzeka-kulak")
print(model.transcribe(["speech.wav"])[0].text)

model.change_decoding_strategy(decoder_type="ctc")  # the CTC head
print(model.transcribe(["speech.wav"])[0].text)

transformers

pip install transformers==5.20.1 torch soundfile==0.14.0 huggingface_hub==1.33.0

import soundfile as sf
import torch
from huggingface_hub import snapshot_download
from transformers import ParakeetForTDT