48 lines
1.0 KiB
Markdown
48 lines
1.0 KiB
Markdown
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---
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license: mit
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datasets:
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- mozilla-foundation/common_voice_17_0
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language:
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- ru
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base_model:
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- openai/whisper-large-v3-turbo
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pipeline_tag: automatic-speech-recognition
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metrics:
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- accuracy
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library_name: transformers
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tags:
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- call
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---
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### This model whas trained with two A100 40 GB, 128 GB RAM and 2 x Xeon 48 Core 2.4 GHz
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- Time spent ~ 7 hours
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- Count of train dataset - 118k of audio samples from Mozilla Common Voice 17
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---
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Example of usage
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```python
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from transformers import pipeline
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import gradio as gr
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import time
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pipe = pipeline(
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model="dvislobokov/whisper-large-v3-turbo-russian",
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tokenizer="dvislobokov/whisper-large-v3-turbo-russian",
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task='automatic-speech-recognition',
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device='cpu'
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)
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def transcribe(audio):
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start = time.time()
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text = pipe(audio, return_timestamps=True)['text']
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print(time.time() - start)
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return text
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iface = gr.Interface(
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fn=transcribe,
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inputs=gr.Audio(sources=['microphone', 'upload'], type='filepath'),
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outputs='text'
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)
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iface.launch(share=True)
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```
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