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import torch
import gradio as gr
import pytube as pt
from transformers import pipeline
from huggingface_hub import model_info
import time
import unicodedata
MODEL_NAME = "SakshiRathi77/wav2vec2-large-xlsr-300m-hi-kagglex"
lang = "hi"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
device=device,
)
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
def rt_transcribe(audio, state=""):
time.sleep(2)
text = p(audio)["text"]
state += unicodedata.normalize("NFC",text) + " "
return state, state
demo = gr.Blocks()
examples=[["examples/example1.mp3"], ["examples/example2.mp3"]]
description = """
<p>
<center>
Welcome to the HindiSpeechPro, a cutting-edge interface powered by a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. Easily convert your spoken words to accurate text with just a few clicks.
</center>
</p>
<center>
<img src="https://huggingface.co/spaces/kingabzpro/real-time-Urdu-ASR/resolve/main/Images/cover.jpg" alt="logo" width="550"/>
</center>
"""
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath"),
gr.inputs.Audio(source="upload", type="filepath"),
],
outputs="text",
theme="huggingface",
title="HindiSpeechPro: WAV2VEC-Powered ASR Interface",
description= description ,
allow_flagging="never",
examples=examples,
).launch(share=True )
gr.Interface.load("models/SakshiRathi77/wav2vec2-large-xlsr-300m-hi-kagglex").launch() |