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---
base_model: facebook/w2v-bert-2.0
library_name: transformers
language:
- uk
license: "apache-2.0"
tags:
- automatic-speech-recognition
datasets:
- espnet/yodas2
metrics:
- wer
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: 0.0000
---
# w2v-bert-uk `v2.1`
## Community
- **Discord**: https://bit.ly/discord-uds
- Speech Recognition: https://t.me/speech_recognition_uk
- Speech Synthesis: https://t.me/speech_synthesis_uk
## Overview
This is a next model of https://huggingface.co/Yehor/w2v-bert-uk
## Demo
Use https://huggingface.co/spaces/Yehor/w2v-bert-uk-v2.1-demo space to see how the model works with your audios.
## Usage
```python
# pip install -U torch soundfile transformers
import torch
import soundfile as sf
from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
# Config
model_name = 'Yehor/w2v-bert-2.0-uk-v2.1'
device = 'cuda:1' # or cpu
sampling_rate = 16_000
# Load the model
asr_model = AutoModelForCTC.from_pretrained(model_name).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).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)
```
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