Spaces:
Running
on
T4
Running
on
T4
File size: 3,671 Bytes
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import os
import torch
import gradio as gr
import pytube as pt
import spaces
from transformers import pipeline
from huggingface_hub import model_info
MODEL_NAME = "NbAiLab/whisper-large-sme" #this always needs to stay in line 8 :D sorry for the hackiness
lang = "fi"
share = (os.environ.get("SHARE", "False")[0].lower() in "ty1") or None
auth_token = os.environ.get("AUTH_TOKEN") or True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
@spaces.GPU(duration=120)
def transcribe(file): #microphone, file_upload):
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
token=auth_token,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
#pipe.to(device)
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):
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
token=auth_token,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
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.components.Audio(sources=['upload', 'microphone'], type="filepath"),
],
outputs="text",
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.components.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
examples=[["https://www.youtube.com/watch?v=mukeSSa5GKo"]],
outputs=["html", "text"],
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(share=True).queue()
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