|
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 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"],["examples/example3.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/SakshiRathi77/SakshiRathi77-Wav2Vec2-hi-kagglex/blob/main/Images/Hindi-Speech-Voice-Recognition-Tool.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, |
|
) |
|
|
|
rt_transcribe = gr.Interface( |
|
fn=rt_transcribe, |
|
inputs=[ |
|
gr.Audio(source="microphone", type="filepath", streaming=True), |
|
"state" |
|
], |
|
outputs=[ "textbox", |
|
"state"], |
|
theme="huggingface", |
|
title="HindiSpeechPro: WAV2VEC-Powered ASR Interface", |
|
description= description , |
|
allow_flagging="never", |
|
live=True, |
|
) |
|
|
|
|
|
with demo: |
|
gr.TabbedInterface([mf_transcribe, rt_transcribe], ["Transcribe Audio", "Transcribe Realtime Voice"]) |
|
|
|
demo.launch(share=True) |
|
|