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import os | |
import wikipedia | |
os.system("pip install git+https://github.com/openai/whisper.git") | |
import gradio as gr | |
import whisper | |
import jiwer | |
from share_btn import community_icon_html, loading_icon_html, share_js | |
model = whisper.load_model("small") | |
wikipedia.set_lang("en") | |
passage = wikipedia.summary("pirate code", sentences=2) | |
def inference(audio): | |
audio = whisper.load_audio(audio) | |
audio_length = audio.shape[-1]/16000 | |
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) | |
transformation = jiwer.Compose([ | |
jiwer.ToLowerCase(), | |
jiwer.RemoveWhiteSpace(replace_by_space=True), | |
jiwer.RemoveMultipleSpaces(), | |
jiwer.ReduceToListOfListOfWords(word_delimiter=" ") | |
]) | |
error = jiwer.wer( | |
passage, | |
result.text, | |
truth_transform=transformation, | |
hypothesis_transform=transformation | |
) | |
# error = jiwer.wer(passage, result.text) | |
we_num = error * len(passage.split()) | |
# print(f"WER is {we_num}") | |
# print(result.text) | |
# print(passage) | |
return f"For a {audio_length} second audio, {we_num} errors were made, resulting in a final time of {audio_length + we_num}.\n\n{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; | |
" | |
> | |
<svg | |
width="0.65em" | |
height="0.65em" | |
viewBox="0 0 115 115" | |
fill="none" | |
xmlns="http://www.w3.org/2000/svg" | |
> | |
<rect width="23" height="23" fill="white"></rect> | |
<rect y="69" width="23" height="23" fill="white"></rect> | |
<rect x="23" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="46" width="23" height="23" fill="white"></rect> | |
<rect x="46" y="69" width="23" height="23" fill="white"></rect> | |
<rect x="69" width="23" height="23" fill="black"></rect> | |
<rect x="69" y="69" width="23" height="23" fill="black"></rect> | |
<rect x="92" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="115" y="46" width="23" height="23" fill="white"></rect> | |
<rect x="115" y="115" width="23" height="23" fill="white"></rect> | |
<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="92" y="69" width="23" height="23" fill="white"></rect> | |
<rect x="69" y="46" width="23" height="23" fill="white"></rect> | |
<rect x="69" y="115" width="23" height="23" fill="white"></rect> | |
<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="46" y="46" width="23" height="23" fill="black"></rect> | |
<rect x="46" y="115" width="23" height="23" fill="black"></rect> | |
<rect x="46" y="69" width="23" height="23" fill="black"></rect> | |
<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="23" y="69" width="23" height="23" fill="black"></rect> | |
</svg> | |
<h1 style="font-weight: 900; margin-bottom: 7px;"> | |
The Rap God Test | |
</h1> | |
</div> | |
<p style="margin-bottom: 10px; font-size: 94%"> | |
The point of the game is to say the given text as fast as possible without errors. Each error adds a one second penaly to the final time and is measured by the WER metric multiplied by text length. The STT is powered by Whisper, 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. This demo cuts audio after around 30 secs. | |
</p> | |
</div> | |
""" | |
) | |
with gr.Group(): | |
gr.Textbox(value=passage) | |
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, 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], outputs=[text, community_icon, loading_icon, share_button]) | |
share_button.click(None, [], [], _js=share_js) | |
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 Manu | |
</p> | |
</div> | |
''') | |
block.launch() |