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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_Request, entity_labels_sample, set_start, audit_elapsedtime |
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import ast |
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import numpy as np |
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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): |
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s2t = init_model_trans() |
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sr, y = audio |
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y = y.astype(np.float32) |
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y /= np.max(np.abs(y)) |
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return transcribe({"sampling_rate": sr, "raw": y}, s2t) |
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def named_entity_recognition(req: NER_Request): |
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ner = init_model_ner() |
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result = get_entity_results(entities_list=req.entities, |
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model=ner, |
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text=req.text) |
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print('result:',result,type(result)) |
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return json.dumps(result) |
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def get_lead(audio: bytes, labels: str, input_text: str): |
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print("audio",audio,type(audio)) |
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print("input text:",input_text) |
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print("labels:",labels) |
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start = set_start() |
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labels_list = ast.literal_eval(labels) |
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if audio == None: |
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text = input_text |
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else: |
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transcribe = transcription(audio) |
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text = translation_to_english(transcribe) |
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lead_input.value = text |
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ner = named_entity_recognition(NER_Request( |
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entities=labels_list, |
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text=text |
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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.Audio( |
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label="Record your audio" |
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) |
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labels_input = gr.Textbox( |
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label="Labels", |
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info="Choose your labels", |
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value=entity_labels_sample |
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) |
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lead_input = gr.Textbox( |
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label="Lead", |
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info="[Optional] Input your lead", |
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lines=9, |
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value="I have a lead that Salesforce needs 3 developers for 600 euros a day, for 6 months" |
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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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ui = gr.Interface( |
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fn=get_lead, |
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description= "Voice your lead", |
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inputs=[audio_input, labels_input, lead_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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ui.launch() |
