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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("Language Output", open=False):
language = gr.Radio(["en-us", "pt-br"], label="Language", info="Choose the language to display the classification result and audio", value='en-us', interactive=True)
with gr.Row():
btn0 = gr.Button("Audio Classification", scale=1, icon='content/Audio Classification.png', size='lg')
btn1 = gr.Button("Realtime Audio Classification", scale=1, icon='content/Realtime Audio Classification.png', size='lg')
btn2 = gr.Button("Speech Recognition", scale=1, icon='content/Speech Recognition.png', size='lg')
btn3 = gr.Button("Help", 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("Record an Audio", open=True):
inputRecord = gr.Audio(label="Audio Input", source="microphone", type="filepath")
with gr.Accordion("Upload a file", open=False):
inputUpload = gr.Audio(label="Audio Input", source="upload", type="filepath")
clearBtn = gr.ClearButton([inputRecord, inputUpload])
with gr.Column(min_width=700):
output = gr.Label(label="Audio Classification")
btn = gr.Button(value="Generate Audio")
audioOutput = gr.Audio(label="Audio Output", 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="Audio Input", source="microphone", type="filepath",streaming=True, every=10)
historyOutput = gr.Textbox(label="History", interactive=False)
# historyOutput = gr.Label(label="History")
with gr.Column(min_width=700):
output = gr.Label(label="Audio Classification")
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("Record an Audio", open=True):
inputRecord = gr.Audio(label="Audio Input", source="microphone", type="filepath")
with gr.Accordion("Upload a file", open=False):
inputUpload = gr.Audio(label="Audio Input", source="upload", type="filepath")
clearBtn = gr.ClearButton([inputRecord])
with gr.Column(min_width=700):
output = gr.Label(label="Transcription")
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="Enter text and press enter",
container=False,
min_width=1000
)
submit = gr.Button(value="", size='sm', scale=1, icon='content/send-icon.png')
inputRecord = gr.Audio(label="Record a question", source="microphone", type="filepath", min_width=600)
btn = gr.Button(value="Listen the answer")
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="Portuguese Examples")
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() |