File size: 6,086 Bytes
1f7af69
 
a67942c
 
 
51499e8
 
6a84f9b
a67942c
bcf2709
92bc38d
a67942c
 
7b66ddd
3eb8d72
 
 
 
 
92bc38d
3eb8d72
7b66ddd
3eb8d72
 
 
51499e8
a67942c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5b5f276
a67942c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
51499e8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a67942c
 
 
 
 
 
 
3eb8d72
 
 
 
 
 
 
 
 
 
 
0bd56e6
3eb8d72
 
 
0bd56e6
3eb8d72
 
 
a67942c
 
 
 
 
 
28b7956
148b8e4
a67942c
 
92bc38d
27924ec
92bc38d
 
 
51499e8
009429d
 
 
 
a67942c
 
7b66ddd
51499e8
a67942c
 
 
0bd56e6
a67942c
 
 
 
 
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
import os
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper

from share_btn import community_icon_html, loading_icon_html, share_js

model = whisper.load_model("medium")

languages = {'auto': None} | {long_name: short_name for short_name, long_name in whisper.tokenizer.LANGUAGES.items()}


        
def inference(audio, language_long_name):
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)
    
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    
    #_, probs = model.detect_language(mel)
    
    options = whisper.DecodingOptions(fp16 = False, language=languages[language_long_name])
    result = whisper.decode(model, mel, options)
    
    print(result.text)
    return result.text, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)




css = """
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: black;
            background: black;
        }
        input[type='range'] {
            accent-color: black;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        .container {
            max-width: 730px;
            margin: auto;
            padding-top: 1.5rem;
        }
     
        .details:hover {
            text-decoration: underline;
        }
        .gr-button {
            white-space: nowrap;
        }
        .gr-button:focus {
            border-color: rgb(147 197 253 / var(--tw-border-opacity));
            outline: none;
            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
            --tw-border-opacity: 1;
            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
            --tw-ring-opacity: .5;
        }
        .footer {
            margin-bottom: 45px;
            margin-top: 35px;
            text-align: center;
            border-bottom: 1px solid #e5e5e5;
        }
        .footer>p {
            font-size: .8rem;
            display: inline-block;
            padding: 0 10px;
            transform: translateY(10px);
            background: white;
        }
        .dark .footer {
            border-color: #303030;
        }
        .dark .footer>p {
            background: #0b0f19;
        }
        .prompt h4{
            margin: 1.25em 0 .25em 0;
            font-weight: bold;
            font-size: 115%;
        }
        .animate-spin {
            animation: spin 1s linear infinite;
        }
        @keyframes spin {
            from {
                transform: rotate(0deg);
            }
            to {
                transform: rotate(360deg);
            }
        }
        #share-btn-container {
            display: flex; margin-top: 1.5rem !important; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
        }
        #share-btn {
            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
        }
        #share-btn * {
            all: unset;
        }
"""

block = gr.Blocks(css=css)



with block:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  🤫 Whisper demo
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Whisper is een generiek spraakherkenning-model van OpenAI. Het is getraind op een grote, diverse dataset van audio. Het kan audio transcriberen, talen herkennen en vertalen. Deze demo kapt audio af na ongeveer 30 seconden.              </p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                audio = gr.Audio(
                    label="Input Audio",
                    show_label=False,
                    source="microphone",
                    type="filepath"
                )

                language_long_name = gr.Dropdown(
                    list(languages.keys()), value="auto", label="Taal van gesproken tekst", info="Taal van de gesproken tekst. Kies auto voor automatische detectie."
                )

                btn = gr.Button("Transcribeer")
        text = gr.Textbox(show_label=False, elem_id="result-textarea")
        with gr.Group(elem_id="share-btn-container"):
            community_icon = gr.HTML(community_icon_html, visible=False)
            loading_icon = gr.HTML(loading_icon_html, visible=False)
            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
        
        
        btn.click(inference, inputs=[audio, language_long_name], outputs=[text, community_icon, loading_icon, share_button])
        share_button.click(None, [], [], _js=share_js)
 
        gr.HTML('''
        <div class="footer">
                    <p>Model door <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> - Demo gebaseerd op de <a href="https://huggingface.co/spaces/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI Whisper Demo</a> van 🤗 Hugging Face
                    </p>
        </div>
        ''')

block.launch()