W2v Bert Uk V2.1

🚀 w2v-bert-uk v2.1

This is an automatic speech recognition model designed for the Ukrainian language. It is based on the facebook/w2v-bert-2.0 model and can achieve high accuracy in speech recognition tasks.

🚀 Quick Start

Prerequisites

Make sure you have installed the necessary libraries:

pip install -U torch soundfile transformers

Code Example

# pip install -U torch soundfile transformers

import torch
import soundfile as sf
from transformers import AutoModelForCTC, Wav2Vec2BertProcessor

# Config
model_name = 'Yehor/w2v-bert-uk-v2.1'
device = 'cuda:0' # or cpu
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
sampling_rate = 16_000

# Load the model
asr_model = AutoModelForCTC.from_pretrained(model_name, torch_dtype=torch_dtype).to(device)
processor = Wav2Vec2BertProcessor.from_pretrained(model_name)

paths = [
  'sample1.wav',
]

# Extract audio
audio_inputs = []
for path in paths:
  audio_input, _ = sf.read(path)
  audio_inputs.append(audio_input)

# Transcribe the audio
inputs = processor(audio_inputs, sampling_rate=sampling_rate).input_features
features = torch.tensor(inputs).half().to(device)

with torch.inference_mode():
  logits = asr_model(features).logits

predicted_ids = torch.argmax(logits, dim=-1)
predictions = processor.batch_decode(predicted_ids)

# Log results
print('Predictions:')
print(predictions)

✨ Features

• Automatic Speech Recognition: This model is specifically designed for automatic speech recognition tasks in the Ukrainian language.
• High Accuracy: Achieves a WER of 17.34% and a CER of 3.33% on the common_voice_10_0 dataset.

📦 Installation

The model can be installed using the transformers library. You can install the necessary dependencies with the following command:

pip install -U torch soundfile transformers

💻 Usage Examples

Basic Usage

# The above code example shows the basic usage of the model.

📚 Documentation

Community

• Discord: https://bit.ly/discord-uds
• Speech Recognition: https://t.me/speech_recognition_uk
• Speech Synthesis: https://t.me/speech_synthesis_uk

See other Ukrainian models: https://github.com/egorsmkv/speech-recognition-uk

Overview

This is a next model of https://huggingface.co/Yehor/w2v-bert-uk

Metrics

• AM (F16):
  • WER: 0.1734 metric, 17.34%
  • CER: 0.0333 metric, 3.33%
  • Accuracy on words: 82.66%
  • Accuracy on chars: 96.67%

Demo

Use https://huggingface.co/spaces/Yehor/w2v-bert-uk-v2.1-demo space to see how the model works with your audios.

Model Information

Model Index

• Name: w2v-bert-uk-v2.1
  • Results:
    • Task:
      • Name: Automatic Speech Recognition
      • Type: automatic-speech-recognition
    • Dataset:
      • Name: common_voice_10_0
      • Type: common_voice_10_0
      • Config: uk
      • Split: test
      • Args: uk
    • Metrics:
      • Name: WER
      • Type: wer
      • Value: 17.34
      • Name: CER
      • Type: cer
      • Value: 3.33

📄 License

This model is licensed under the apache-2.0 license.

📚 Cite this work

@misc {smoliakov_2025,
	author       = { {Smoliakov} },
	title        = { w2v-bert-uk-v2.1 (Revision 094c59d) },
	year         = 2025,
	url          = { https://huggingface.co/Yehor/w2v-bert-uk-v2.1 },
	doi          = { 10.57967/hf/4554 },
	publisher    = { Hugging Face }
}