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import gradio as gr |
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import json |
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from nameder import init_model_ner, get_entity_results |
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from speech2text import init_model_trans, transcribe |
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from translation import translate |
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from resources import NER_Response, NER_Request, entity_labels_sample, set_start, audit_elapsedtime |
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def translation_to_english(text: str): |
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resultado = translate(text) |
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return resultado |
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def transcription(audio: bytes): |
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s2t = init_model_trans() |
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return transcribe(audio, s2t) |
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def named_entity_recognition(text: str): |
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tokenizer, ner = init_model_ner() |
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result = get_entity_results(entities_list=entity_labels_sample, |
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model=ner, |
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tokenizer=tokenizer, |
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text=text) |
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print('result:',result,type(result)) |
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return result |
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def get_lead(audio: bytes): |
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start = set_start() |
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transcribe = transcription(audio) |
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translate = translation_to_english(transcribe) |
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ner = named_entity_recognition(NER_Request( |
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entities=entity_labels_sample, |
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text=translate |
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)) |
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audit_elapsedtime("VoiceLead", start) |
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return ner |
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audio_input = gr.Microphone( |
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label="Record your audio" |
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) |
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text_output = gr.Textbox( |
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label="Labels", |
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info="", |
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lines=9, |
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value="" |
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) |
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demo = gr.Interface( |
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fn=named_entity_recognition, |
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description= "Get the ", |
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inputs=[audio_input], |
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outputs=[text_output], |
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title="VoiceLead" |
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) |
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if __name__ == "__main__": |
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demo.launch() |
