dvislobokov/whisper-large-v3-turbo-russian

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

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Example of usage

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)