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Updated the app.py with the model name shivkumarganesh/whisper-small-hi-v1 that is finetunes on whisper-small in hindi
dadf31e
import torch | |
import gradio as gr | |
import pytube as pt | |
from transformers import pipeline | |
from huggingface_hub import model_info | |
MODEL_NAME = "shivkumarganesh/whisper-small-hi" #this always needs to stay in line 8 :D sorry for the hackiness | |
lang = "hi" | |
device = 0 if torch.cuda.is_available() else "cpu" | |
pipe = pipeline( | |
task="automatic-speech-recognition", | |
model=MODEL_NAME, | |
chunk_length_s=30, | |
device=device, | |
) | |
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") | |
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 | |
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="Whisper Demo: Transcribe Audio", | |
description=( | |
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" | |
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
" of arbitrary length." | |
), | |
allow_flagging="never", | |
) | |
yt_transcribe = gr.Interface( | |
fn=yt_transcribe, | |
inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], | |
outputs=["html", "text"], | |
layout="horizontal", | |
theme="huggingface", | |
title="Whisper Demo: Transcribe YouTube", | |
description=( | |
"Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:" | |
f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of" | |
" arbitrary length." | |
), | |
allow_flagging="never", | |
) | |
with demo: | |
gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
demo.launch(enable_queue=True) | |