app.py

import gradio as gr
from transformers import pipeline
from helpers import load_model_file, load_wav_16k_mono_librosa, initialize_text_to_speech_model, load_label_mapping, predict_yamnet, classify, classify_realtime
from helpers import interface, interface_realtime, updateHistory, clearHistory, clear, format_dictionary, format_json
from helpers import generate_audio, TTS, TTS_ASR, TTS_chatbot, transcribe_speech, transcribe_speech_realtime, transcribe_realtime, translate_enpt
from helpers import chatbot_response, add_text

history = ""
last_answer = ""

examples_audio_classification = [
    "content/talking-people.mp3",
    "content/miaow_16k.wav",
    "content/birds-in-forest-loop.wav",
    "content/drumming-jungle-music.wav",
    "content/driving-in-the-rain.wav",
    "content/city-alert-siren.wav",
    "content/small-group-applause.wav",
    "content/angry-male-crowd-ambience.wav",
    "content/slow-typing-on-a-keyboard.wav",
    "content/emergency-car-arrival.wav"
]

examples_speech_recognition_en = [
    "content/speech1-en.wav",
    "content/speech2-en.wav",
    "content/speech1-ptbr.wav",
    "content/speech2-ptbr.wav",
    "content/speech3-ptbr.wav"
]
examples_speech_recognition_ptbr = [
    "content/speech1-ptbr.wav",
    "content/speech2-ptbr.wav",
    "content/speech3-ptbr.wav",
]

examples_chatbot_en = [
    ['How does SocialEar assist people with hearing disabilities?'],
    ['Give me suggestions on how to use SocialEar'],
    ['How does SocialEar work?'],
    ['Are SocialEar results accurate?'],
    ['What accessibility features does SocialEar offer?'],
    ['Does SocialEar collect personal data?'],
    ['Can I use SocialEar to identify songs and artists from recorded audio?'],
    ]

examples_chatbot_ptbr = [
    ['Como o SocialEar auxilia pessoas com deficiência auditiva?'],
     ['Dê-me sugestões sobre como usar o SocialEar'],
     ['Como funciona o SocialEar?'],
     ['Os resultados do SocialEar são precisos?'],
     ['Quais recursos de acessibilidade o SocialEar oferece?'],
     ['O SocialEar coleta dados pessoais?'],
     ['Posso usar o SocialEar para identificar músicas e artistas de áudio gravado?'],
    ]

def to_audioClassification():
    return {
      audio_classification: gr.Row(visible=True),
      realtime_classification: gr.Row(visible=False),
      speech_recognition: gr.Row(visible=False),
      chatbot_qa: gr.Row(visible=False),
    }

def to_realtimeAudioClassification():
    return {
      audio_classification: gr.Row(visible=False),
      realtime_classification: gr.Row(visible=True),
      speech_recognition: gr.Row(visible=False),
      chatbot_qa: gr.Row(visible=False),
    }

def to_speechRecognition():
    return {
      audio_classification: gr.Row(visible=False),
      realtime_classification: gr.Row(visible=False),
      speech_recognition: gr.Row(visible=True),
      chatbot_qa: gr.Row(visible=False),
    }

def to_chatbot():
    return {
      audio_classification: gr.Row(visible=False),
      realtime_classification: gr.Row(visible=False),
      speech_recognition: gr.Row(visible=False),
      chatbot_qa: gr.Row(visible=True),
    }


with gr.Blocks() as demo:

    with gr.Accordion("Idioma de saída", open=False):
      language = gr.Radio(["en-us", "pt-br"], label="Idioma", info="Escolha o idioma de saída para os resultados", value='pt-br', interactive=True)

    with gr.Row():
      btn0 = gr.Button("Classificação de áudio", scale=1, icon='content/Audio Classification.png', size='lg')
      btn1 = gr.Button("Classificação de áudio em tempo real", scale=1, icon='content/Realtime Audio Classification.png', size='lg')
      btn2 = gr.Button("Reconhecimento de Fala", scale=1, icon='content/Speech Recognition.png', size='lg')
      btn3 = gr.Button("Ajuda Q&A", scale=1, icon='content/Chatbot.png', size='lg')

    with gr.Row(visible=False) as audio_classification:
          with gr.Column(min_width=700):
                with gr.Accordion("Grave um áudio", open=True):
                  inputRecord = gr.Audio(label="Entrada de áudio", source="microphone", type="filepath")
                with gr.Accordion("Carregue um arquivo", open=False):
                  inputUpload = gr.Audio(label="Entrada de áudio", source="upload", type="filepath")
                clearBtn = gr.ClearButton([inputRecord, inputUpload])
          with gr.Column(min_width=700):
                output = gr.Label(label="Classificação de Áudio")
                btn = gr.Button(value="Gerar áudio")
                audioOutput = gr.Audio(label="Saída de áudio", interactive=False)


          inputRecord.stop_recording(interface, [inputRecord, language], [output])
          inputUpload.upload(interface, [inputUpload, language], [output])
          btn.click(fn=TTS, inputs=[output, language], outputs=audioOutput)

          examples = gr.Examples(fn=interface, examples=examples_audio_classification, inputs=[inputRecord], outputs=[output], run_on_click=True)

    with gr.Row(visible=False) as realtime_classification:
          with gr.Column(min_width=700):
                input = gr.Audio(label="Entrada de áudio", source="microphone", type="filepath",streaming=True, every=10)
                historyOutput = gr.Textbox(label="Histórico", interactive=False)
                # historyOutput = gr.Label(label="History")
          with gr.Column(min_width=700):
                output = gr.Label(label="Classificação de Áudio")

          input.change(interface_realtime, [input, language], output)
          input.change(updateHistory, None, historyOutput)
          input.start_recording(clearHistory, None, historyOutput)


    with gr.Row(visible=False) as speech_recognition:
          with gr.Column(min_width=700):
                with gr.Accordion("Grave um áudio", open=True):
                  inputRecord = gr.Audio(label="Entrada de áudio", source="microphone", type="filepath")
                with gr.Accordion("Carregue um arquivo", open=False):
                  inputUpload = gr.Audio(label="Entrada de áudio", source="upload", type="filepath")
                clearBtn = gr.ClearButton([inputRecord])
          with gr.Column(min_width=700):
                output = gr.Label(label="Transcrição")


          inputRecord.stop_recording(transcribe_speech, [inputRecord, language], [output])
          inputUpload.upload(transcribe_speech, [inputUpload, language], [output])

          # examplesSpeechEn = gr.Examples(fn=transcribe_speech, examples=examples_speech_recognition_en, inputs=[inputRecord], outputs=[output], run_on_click=True, label="Examples")
          examplesSpeechPtbr = gr.Examples(fn=transcribe_speech, examples=examples_speech_recognition_ptbr, inputs=[inputRecord], outputs=[output], run_on_click=True, label="Portuguese Examples")

    with gr.Row(visible=False) as chatbot_qa:
      chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        bubble_full_width=False,
        avatar_images=(None, "content/avatar-socialear.png"),
        min_width=2000
      )
      with gr.Row(min_width=2000):
        txt = gr.Textbox(
              scale=4,
              show_label=False,
              placeholder="Escreva o texto e precione enter",
              container=False,
              min_width=1000
          )
        submit = gr.Button(value="", size='sm', scale=1, icon='content/send-icon.png')


        inputRecord = gr.Audio(label="Grave uma pergunta", source="microphone", type="filepath", min_width=600)
        btn = gr.Button(value="Escute a resposta")
        audioOutput = gr.Audio(interactive=False, min_width=600)

        txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
              chatbot_response, [chatbot, language], chatbot)
        txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
        submit.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
              chatbot_response, [chatbot, language], chatbot).then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
        inputRecord.stop_recording(transcribe_speech, [inputRecord, language], [txt])
        btn.click(fn=TTS_chatbot, inputs=[language], outputs=audioOutput)

        with gr.Row(min_width=2000):
          # examplesChatbotEn = gr.Examples(examples=examples_chatbot_en, inputs=[txt], label="English Examples")
          examplesChatbotPtbr = gr.Examples(examples=examples_chatbot_ptbr, inputs=[txt], label="Exemplos")


    btn0.click(fn=to_audioClassification, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])
    btn1.click(fn=to_realtimeAudioClassification, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])
    btn2.click(fn=to_speechRecognition, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])
    btn3.click(fn=to_chatbot, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])


if __name__ == "__main__":
    demo.queue()
    demo.launch()