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import gradio as gr
import json
from nameder import init_model_ner, get_entity_results
from speech2text import init_model_trans, transcribe
from translation import translate
from resources import NER_Request, entity_labels_sample, set_start, audit_elapsedtime
import ast
import numpy as np
def translation_to_english(text: str):
resultado = translate(text)
return resultado
def transcription(audio):
s2t = init_model_trans()
sr, y = audio
y = y.astype(np.float32)
y /= np.max(np.abs(y))
return transcribe({"sampling_rate": sr, "raw": y}, s2t)
def named_entity_recognition(req: NER_Request):
ner = init_model_ner()
result = get_entity_results(entities_list=entity_labels_sample,
model=ner,
text=req.text)
print('result:',result,type(result))
return json.dumps(result)
def get_lead(audio: bytes, labels: str, input_text: str):
print("audio",audio,type(audio))
print("input text:",input_text)
print("labels:2",labels)
start = set_start()
labels_list = ast.literal_eval(labels)
if audio == None:
text = input_text
else:
transcribe = transcription(audio)
text = transcribe#translate = translation_to_english(transcribe)
lead_input.value = text
ner = named_entity_recognition(NER_Request(
entities=labels_list,
text=text
))
audit_elapsedtime("VoiceLead", start)
return ner
audio_input = gr.Audio(
label="Record your audio"
)
labels_input = gr.Textbox(
label="Labels",
info="Choose your labels",
value=entity_labels_sample
)
lead_input = gr.Textbox(
label="Lead",
info="[Optional] Input your lead",
lines=9,
value="I have a lead that Salesforce needs 3 developers for 600 euros a day, for 6 months"
)
text_output = gr.Textbox(
label="Labels",
info="",
lines=9,
value=""
)
ui = gr.Interface(
fn=get_lead,
description= "Voice your lead",
inputs=[audio_input, labels_input, lead_input],
outputs=[text_output],
title="VoiceLead"
)
if __name__ == "__main__":
ui.launch(share=True) |