#Imports------------------------------------------------------------- import gradio as gr import subprocess import torch from transformers import pipeline import os #User defined functions (UDF) from functions.charts import spider_chart from functions.dictionaries import calculate_average, transform_dict from functions.icon import generate_icon from functions.timestamp import format_timestamp from functions.youtube import get_youtube_video_id #--------------------------------------------------------------------- MODEL_NAME = "openai/whisper-medium" BATCH_SIZE = 8 device = 0 if torch.cuda.is_available() else "cpu" #Transformers pipeline pipe = pipeline( task="automatic-speech-recognition", model=MODEL_NAME, chunk_length_s=30, device=device ) #Formating--------------------------------------------------------------------------------------------- title = "Whisper Demo: Transcribe Audio" MODEL_NAME1 = "jpdiazpardo/whisper-tiny-metal" description = ("Transcribe long-form audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME1}](https://huggingface.co/{MODEL_NAME1}) and 🤗 Transformers to transcribe audio files" " of arbitrary length. Check some of the 'cool' examples below") linkedin = generate_icon("linkedin") github = generate_icon("github") article = ("
" f"

{linkedin} Juan Pablo Díaz Pardo
" f"{github} jpdiazpardo

") title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recognition 🤟🤟🤟" #------------------------------------------------------------------------------------------------------------------------------- #Define classifier for sentiment analysis classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None) #Functions----------------------------------------------------------------------------------------------------------------------- def transcribe(link,download,thumbnail,file,use_timestamps,sentiment_analysis):#file, return_timestamps, *kwargs): '''inputs: file, return_timestamps''' outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True) text = outputs["text"] timestamps = outputs["chunks"] #If return timestamps is True, return html text with timestamps format if use_timestamps==True: spider_text = [f"{chunk['text']}" for chunk in timestamps] #Text for spider chart without timestamps timestamps = [f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" for chunk in timestamps] else: timestamps = [f"{chunk['text']}" for chunk in timestamps] spider_text = timestamps text = "
".join(str(feature) for feature in timestamps) text = f"

Transcription

{text}
" spider_text = "\n".join(str(feature) for feature in spider_text) trans_dict=[transform_dict(classifier.predict(t)[0]) for t in spider_text.split("\n")] av_dict = calculate_average(trans_dict) fig = spider_chart(av_dict) return file, text, fig, av_dict embed_html = '' def download(link): subprocess.run(['python3', 'youtubetowav.py', link]) return thumbnail.update(value=embed_html.replace("YOUTUBE_ID",get_youtube_video_id(link))) def hide_sa(value): if value == True: return sa_plot.update(visible=True), sa_frequency.update(visible=True) else: return sa_plot.update(visible=False), sa_frequency.update(visible=False) #---------------------------------------------------------------------------------------------------------------------------------------------- #Components------------------------------------------------------------------------------------------------------------------------------------ #Input components yt_link = gr.Textbox(value=None,label="YouTube link", info = "Optional: Copy and paste YouTube URL") audio_input = gr.Audio(source="upload", type="filepath", label="Upload audio file for transcription") download_button = gr.Button(value="Download") thumbnail = gr.HTML(value="", label = "Thumbnail") sa_checkbox = gr.Checkbox(value=True, label="Sentiment analysis") inputs = [yt_link, #0 download_button, #1 thumbnail, #2 audio_input, #3 gr.Checkbox(value=True, label="Return timestamps"), #4 sa_checkbox] #5 #Ouput components audio_out = gr.Audio(label="Processed Audio", type="filepath", info = "Vocals only") sa_plot = gr.Plot(label="Sentiment Analysis") sa_frequency = gr.Label(label="Frequency") outputs = [audio_out, gr.outputs.HTML("text"), sa_plot, sa_frequency] #---------------------------------------------------------------------------------------------------------------------------------------------------- #Launch demo----------------------------------------------------------------------------------------------------------------------------------------- with gr.Blocks() as demo: download_button.click(download, inputs=[yt_link], outputs=[thumbnail]) sa_checkbox.change(hide_sa, inputs=[sa_checkbox], outputs=[sa_plot, sa_frequency]) with gr.Column(): gr.Interface(title = title, fn=transcribe, inputs = inputs, outputs = outputs, description=description, allow_flagging="never", article = article, examples="examples") demo.queue(concurrency_count=3) demo.launch(debug = True) #----------------------------------------------------------------------------------------------------------------------------------------------------