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| import gradio as gr | |
| from nemo.collections.asr.models import ASRModel | |
| import torch | |
| if torch.cuda.is_available(): | |
| device = torch.device(f'cuda:0') | |
| asr_model = ASRModel.from_pretrained(model_name='stt_en_citrinet_1024') | |
| from happytransformer import HappyTextToText, TTSettings | |
| happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction") | |
| args = TTSettings(num_beams=5, min_length=1) | |
| def transcribe(audio): | |
| """Speech to text using Nvidia Nemo""" | |
| text = asr_model.transcribe(paths2audio_files=[audio])[0] | |
| # Add the prefix "grammar: " before each input | |
| correct = happy_tt.generate_text("grammar: " + text, args=args) | |
| return text, correct.text | |
| gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath"), | |
| ], | |
| outputs=[ | |
| "textbox", | |
| "textbox" | |
| ]).launch() |