File size: 8,996 Bytes
1e40d63
f90f3f5
 
 
 
 
 
 
 
 
 
 
 
1ec0028
e2a9b8f
 
 
 
 
 
 
f90f3f5
 
 
 
 
e2a9b8f
 
 
f90f3f5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e40d63
5f3740d
 
 
 
1ebd0cd
 
5f3740d
f90f3f5
5f3740d
 
 
 
1ebd0cd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5f3740d
c518990
1e40d63
 
0667117
1ebd0cd
 
 
5f3740d
f90f3f5
 
 
 
1e40d63
5f3740d
 
 
 
 
 
 
 
 
 
 
 
 
f90f3f5
 
 
 
 
 
5f3740d
 
 
 
 
 
 
 
f90f3f5
 
 
 
 
1ebd0cd
 
 
 
 
 
 
 
 
 
f90f3f5
 
 
 
e2a9b8f
f90f3f5
 
1ebd0cd
 
 
 
 
f90f3f5
1ebd0cd
 
 
 
 
 
 
 
 
 
f90f3f5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1ebd0cd
5f3740d
1ebd0cd
 
 
 
7662900
5f3740d
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
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("Settings", open=True):
      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()