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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("small")
def inference(audio):
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)
result = whisper.decode(model, mel, options)
print(result.text)
return result.text
title="Whisper OpenAI, deployed with jthteo"
description="Whisper is a general-purpose speech recognition model into English. It has been 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;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<svg
width="0.65em"
height="0.65em"
viewBox="0 0 115 115"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<circle cx="62" cy="45" r="36" stroke="blue" stroke-width="4" fill="blue" />
<polygon points="40, 30, 84, 30, 62, 69" style="fill:red;stroke:red;stroke-width:5;" />
</svg>
<h1 style="font-weight: 900; margin-bottom: 7px; style="color:red"">
Whisper
</h1>
<svg
width="0.65em"
height="0.65em"
viewBox="0 0 115 115"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<circle cx="62" cy="45" r="36" stroke="blue" stroke-width="4" fill="blue" />
<polygon points="40, 30, 84, 30, 62, 69" style="fill:red;stroke:red;stroke-width:5;" />
</svg>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
Whisper is a general-purpose speech recognition model. It has been 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. </p>
<p>This app is written in Gradio by OpenAI. This is a fork by JTHTEO.
</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"
)
btn = gr.Button("Transcribe")
text = gr.Textbox(show_label=False)
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> - Gradio Demo by 🤗 Hugging Face, this is a fork by JTHTEO
</p>
</div>
''')
block.launch() |