app.py

import torch
import gradio as gr
import pytube as pt
from transformers import pipeline
from huggingface_hub import model_info
import time
import unicodedata
# from gradio.themes.utils.theme_dropdown import create_theme_dropdown

MODEL_NAME = "SakshiRathi77/wav2vec2-large-xlsr-300m-hi-kagglex"
lang = "hi"

# my_theme = gr.Theme.from_hub('freddyaboulton/dracula_revamped')
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 = pipe(audio)["text"]
    state += unicodedata.normalize("NFC",text) + " "
    
    return state, state



demo = gr.Blocks()
examples=[["examples/example1.mp3"], ["examples/example2.mp3"],["examples/example3.mp3"]]

title ="""
HindiSpeechPro: WAV2VEC-Powered ASR Interface
"""

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.
<img src="https://huggingface.co/spaces/SakshiRathi77/SakshiRathi77-Wav2Vec2-hi-kagglex/resolve/main/Images/main_image2.png" alt="logo" ;>
</center>
</p>
"""


# article = "<p style='text-align: center'><a href='https://github.com/SakshiRathi77/ASR' target='_blank'>Source Code on Github</a></p><p style='text-align: center'><a href='https://huggingface.co/blog/fine-tune-xlsr-wav2vec2' target='_blank'>Reference</a></p><p style='text-align: center'><a href='https://forms.gle/hjfc3F1P7m3weQVAA' target='_blank'><img src='https://e7.pngegg.com/pngimages/794/310/png-clipart-customer-review-feedback-user-service-others-miscellaneous-text-thumbnail.png' alt='Feedback Form' ;></a></p>"


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=title,
    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=title,
    description= description ,
    allow_flagging="never",
    live=True,
)


with demo:
    gr.TabbedInterface([mf_transcribe, rt_transcribe], ["Transcribe Audio", "Transcribe Realtime Voice"])

demo.launch(share=True)