Spaces:
Runtime error
Runtime error
File size: 6,753 Bytes
a2c2b3c b8b135b 039ef51 c2110e8 a2c2b3c 0adbe8f a2c2b3c fbf8e92 a2c2b3c cb9a89f e24cd77 e559d03 8549c9b e559d03 a2c2b3c e559d03 a2c2b3c e559d03 a2c2b3c febac70 55a586c a2c2b3c 7f77256 febac70 43ba5f1 e559d03 77fc3c3 accb4e2 55a586c 43ba5f1 55a586c 77fc3c3 bba23d3 77fc3c3 55a586c c2110e8 accb4e2 febac70 55a586c febac70 55a586c 43ba5f1 14c0ec2 febac70 a2c2b3c febac70 7f77256 dcd2d8b febac70 4abc7f4 febac70 a2c2b3c febac70 2994d17 febac70 a68a0c7 a2c2b3c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
#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
#---------------------------------------------------------------------
##examples = [["https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",None,None,
# os.path.join(os.path.dirname(__file__), "examples/When a Demon Defiles a Witch.wav"),True, True]]
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 = ("<div style='text-align: center; max-width:800px; margin:10px auto;'>"
f"<p>{linkedin} <a href='https://www.linkedin.com/in/juanpablodiazp/' target='_blank'>Juan Pablo Díaz Pardo</a><br>"
f"{github} <a href='https://github.com/jpdiazpardo' target='_blank'>jpdiazpardo</a></p>")
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,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 = "<br>".join(str(feature) for feature in timestamps)
text = f"<h4>Transcription</h4><div style='overflow-y: scroll; height: 150px;'>{text}</div>"
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 = '<iframe src="https://www.youtube.com/embed/YOUTUBE_ID'\
'title="YouTube video player" frameborder="0" allow="accelerometer;'\
'autoplay; clipboard-write; encrypted-media; gyroscope;'\
'picture-in-picture" allowfullscreen></iframe>'
def download(link):
subprocess.run(['python3', 'youtubetowav.py', link])
return thumbnail.update(value=embed_html.replace("YOUTUBE_ID",get_youtube_video_id(link)), visible=True)
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=embed_html, visible=False, 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
)
gr.examples(examples=["Demon"],
inputs = ["https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",
"<a href='https://www.linkedin.com/in/juanpablodiazp/'",
"examples/When a Demon Defiles a Witch.wav",True, True]
)
demo.queue(concurrency_count=3)
demo.launch(debug = True)
#---------------------------------------------------------------------------------------------------------------------------------------------------- |