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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 = "cloudqi/cqi_speech_recognize_pt_v0"
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
langs = model_info(MODEL_NAME).cardData["language"]
article = f"<details><summary>Esse modelo suporta {len(langs)} línguas ! (Clique para expandir)</summary>> {langs}</details>"
def transcribe(microphone, file_upload):
warn_output = ""
if (microphone is not None) and (file_upload is not None):
warn_output = (
"WARNING: Você carregou um arquivo de áudio e usou o microfone. "
"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: Transcreva microfones longos ou entradas de áudio com o clique de um botão"
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="Demonstração: Transcrever Audio",
description=(
"Transcreva microfones longos ou entradas de áudio com o clique de um botão! Essa Demo usa o ajuste fino"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) e 🤗 Transformers para transcrever arquivos de áudio"
" de comprimento arbitrário."
),
article=article,
allow_flagging="never",
)
yt_transcribe = gr.Interface(
fn=yt_transcribe,
inputs=[gr.inputs.Textbox(lines=1, placeholder="Cole o URL de um vídeo do YouTube aqui", label="YouTube URL")],
outputs=["html", "text"],
layout="horizontal",
theme="huggingface",
title="Transcrever do YouTube",
description=(
"Gere legendas com um clique ! A demonstração usa o ponto de verificação aprimorado:"
f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) e 🤗 Transformers para transcrever arquivos de áudio de"
" comprimento arbitrário."
),
article=article,
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcrever de áudio", "Transcrever do YouTube"])
demo.launch(enable_queue=True)
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