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whisper-youtube
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import os
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper

model = whisper.load_model("base")



        
def inference(audio):
  result = model.transcribe(audio)
  print(result["text"])
  return result["text"]


title="Whisper"

description="Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification."

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%;
        }
"""

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;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                  margin-bottom: 10px;
                  margin-left: 220px;
                  justify-content: center;
                "
              >
              <a href="https://github.com/PaddlePaddle/PaddleHub"><img src="https://user-images.githubusercontent.com/22424850/187387422-f6c9ccab-7fda-416e-a24d-7d6084c46f67.jpg" alt="Paddlehub" width="40%"></a>
              </div> 
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                  margin-bottom: 10px;
                  justify-content: center;
                ">
              <a href="https://github.com/PaddlePaddle/PaddleHub"><h1 style="font-weight: 900; margin-bottom: 7px;">
                  ERNIE-ViLG Demo
              </h1></a>
              </div> 
              <p style="margin-bottom: 10px; font-size: 94%">
                ERNIE-ViLG is a state-of-the-art text-to-image model that generates
                images from Chinese text.
              </p>
              <a href="https://github.com/PaddlePaddle/PaddleHub"><img src="https://user-images.githubusercontent.com/22424850/188184795-98605a22-9af2-4106-827b-e58548f8892f.png" alt="star Paddlehub" width="100%"></a>
            </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,
                ).style(
                    rounded=(True, False, False, True),
                    container=False,
                )

                btn = gr.Button("Transcribe").style(
                    margin=False,
                    rounded=(False, True, True, False),
                )
        text = gr.Textbox(
        ).style(height="auto")
        


        
        btn.click(inference, inputs=[audio], outputs=[text])
 
        gr.HTML('''
        <div class="footer">
                    <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> and <a href="https://wenxin.baidu.com" style="text-decoration: underline;" target="_blank">文心大模型</a> - Gradio Demo by 🤗 Hugging Face
                    </p>
        </div>
        ''')

block.launch()