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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 = "arpagon/whisper-large-v2-es" #this always needs to stay in line 8 :D sorry for the hackiness
lang = "es"
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
def _return_yt_html_embed(yt_url):
video_id = yt_url.split("?v=")[-1]
HTML_str = (
f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
" </center>"
)
return HTML_str
def yt_transcribe(yt_url):
yt = pt.YouTube(yt_url)
html_embed_str = _return_yt_html_embed(yt_url)
stream = yt.streams.filter(only_audio=True)[0]
stream.download(filename="audio.mp3")
text = pipe("audio.mp3")["text"]
return html_embed_str, text
demo = gr.Blocks()
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",
title="Whisper Demo: Transcribe Audio",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
" of arbitrary length."
),
allow_flagging="never",
)
yt_transcribe = gr.Interface(
fn=yt_transcribe,
inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
outputs=["html", "text"],
layout="horizontal",
theme="huggingface",
title="Whisper Demo: Transcribe YouTube",
description=(
"Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
" arbitrary length."
),
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
demo.launch(enable_queue=True)
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