---
license: mit
datasets:
- mozilla-foundation/common_voice_17_0
language:
- ru
base_model:
- openai/whisper-large-v3-turbo
pipeline_tag: automatic-speech-recognition
metrics:
- accuracy
library_name: transformers
tags:
- call
---

### This model whas trained with two A100 40 GB, 128 GB RAM and 2 x Xeon 48 Core 2.4 GHz
- Time spent ~ 7 hours
- Count of train dataset - 118k of audio samples from Mozilla Common Voice 17
---
Example of usage
```python
from transformers import pipeline
import gradio as gr
import time

pipe = pipeline(
    model="dvislobokov/whisper-large-v3-turbo-russian",
    tokenizer="dvislobokov/whisper-large-v3-turbo-russian",
    task='automatic-speech-recognition',
    device='cpu'
)

def transcribe(audio):
    start = time.time()
    text = pipe(audio, return_timestamps=True)['text']
    print(time.time() - start)
    return text

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=['microphone', 'upload'], type='filepath'),
    outputs='text'
)

iface.launch(share=True)

```