license, datasets, language, base_model, pipeline_tag, metrics, library_name, tags

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

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)