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import torch
import gradio as gr
import pytube as pt
from transformers import pipeline
from huggingface_hub import model_info
MODEL_NAME = "openai/whisper-small" #this always needs to stay in line 8 :D sorry for the hackiness
lang = "en"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
def transcribe(microphone, file_upload):
warn_output = ""
if (microphone is not None) and (file_upload is not None):
warn_output = (
"WARNING: You've uploaded an audio file and used the microphone. "
"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
)
elif (microphone is None) and (file_upload is None):
return "ERROR: You have to either use the microphone or upload an audio file"
file = microphone if microphone is not None else file_upload
text = pipe(file)["text"]
return warn_output + text
demo = gr.Blocks()
css = """
footer {display:none !important}
.output-markdown{display:none !important}
.gr-button-primary {
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: none rgb(17, 20, 45) !important;
border: none !important;
text-align: center !important;
font-family: Poppins !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: none !important;
}
.gr-button-primary:hover{
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: none rgb(37, 56, 133) !important;
border: none !important;
text-align: center !important;
font-family: Poppins !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
.hover\:bg-orange-50:hover {
--tw-bg-opacity: 1 !important;
background-color: rgb(229,225,255) !important;
}
.to-orange-200 {
--tw-gradient-to: rgb(37 56 133 / 37%) !important;
}
.from-orange-400 {
--tw-gradient-from: rgb(17, 20, 45) !important;
--tw-gradient-to: rgb(255 150 51 / 0);
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group-hover\:from-orange-500{
--tw-gradient-from:rgb(17, 20, 45) !important;
--tw-gradient-to: rgb(37 56 133 / 37%);
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group:hover .group-hover\:text-orange-500{
--tw-text-opacity: 1 !important;
color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
}
"""
examples = [
['TestAudio1.mp3'], ['TestAudio2.wav'], ['TestAudio3.wav'], ['TestAudio4.wav'], ['TestAudio5.wav'], ['TestAudio6.wav'], ['TestAudio7.wav'], ['TestAudio8.wav'], ['TestAudio9.wav'], ['TestAudio10.wav']
]
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath", optional=True),
gr.inputs.Audio(source="upload", type="filepath", optional=True)
],
outputs="text",
layout="horizontal",
theme="huggingface",
allow_flagging="never",
examples = examples,
css = css
).launch(enable_queue=True)
#used openai/whisper model |