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'
' # "
" # ) # 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 = """

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.

logo
""" 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()