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import gradio as gr
from transformers import pipeline
import numpy as np
import pytesseract
import cv2
from PIL import Image
from evaluate import load
import librosa
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
asr = pipeline("automatic-speech-recognition", model="openai/whisper-base")
wer = load("wer")
def extract_text(image):
"""
Extracts text from an image using OCR.
Args:
image (PIL.Image.Image): Input image.
Returns:
dict: Extracted text with confidence and coordinates.
Raises:
ValueError: If the input image is not a PIL Image object.
"""
result = pytesseract.image_to_data(image, output_type='dict')
n_boxes = len(result['level'])
data = {}
k = 0
for i in range(n_boxes):
if result['conf'][i] >= 0.3 and result['text'][i] != '' and result['conf'][i] != -1:
data[k] = {}
(x, y, w, h) = (result['left'][i], result['top']
[i], result['width'][i], result['height'][i])
data[k]["coordinates"] = (x, y, w, h)
text, conf = result['text'][k], result['conf'][k]
data[k]["text"] = text
data[k]["conf"] = conf
k += 1
return data
def draw_rectangle(image, x, y, w, h, color=(0, 0, 255), thickness=2):
"""
Draws a rectangle on the given image.
Args:
image (PIL.Image.Image): Input image.
x (int): x-coordinate of the top-left corner of the rectangle.
y (int): y-coordinate of the top-left corner of the rectangle.
w (int): Width of the rectangle.
h (int): Height of the rectangle.
color (tuple, optional): Color of the rectangle in RGB format.
thickness (int, optional): Thickness of the rectangle's border.
Returns:
PIL.Image.Image: Image with the rectangle drawn on it.
Raises:
ValueError: If the input image is not a PIL Image object.
"""
image_array = np.array(image)
image_array = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
cv2.rectangle(image_array, (x, y), (x + w, y + h), color, thickness)
return Image.fromarray(cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB))
def transcribe(audio):
"""
Transcribes audio into text using ASR.
Parameters:
audio (str or tuple): Audio source.
Returns:
str: Transcribed text.
Raises:
ValueError: If the input audio is not valid.
"""
if not isinstance(audio, (str, tuple)):
raise ValueError(
"Invalid input. Audio should be either a file path or a tuple of (sampling_rate, raw_audio).")
if isinstance(audio, str): # If audio is a file path
y, sr = librosa.load(audio)
# If audio is (sampling_rate, raw_audio)
elif isinstance(audio, tuple) and len(audio) == 2:
sr, y = audio
y = y.astype(np.float32)
else:
raise ValueError(
"Invalid input. Audio should be a file path or a tuple of (sampling_rate, raw_audio).")
y /= np.max(np.abs(y))
transcribed_text = asr(
{"sampling_rate": sr, "raw": y})["text"]
return transcribed_text
def clean_transcription(transcription):
"""
Cleans the transcription by removing consecutive duplicate words.
Args:
transcription (str): Input transcription.
Returns:
str: Cleaned transcription.
Raises:
ValueError: If the input transcription is not a string.
"""
if not isinstance(transcription, str):
raise ValueError("Invalid input. Transcription should be a string.")
text = transcription.lower()
words = text.split()
cleaned_words = [words[0]]
for word in words[1:]:
if word != cleaned_words[-1]:
cleaned_words.append(word)
return ' '.join(cleaned_words)
def match(refence, spoken):
"""
Calculates the match score between a reference and spoken string.
Args:
reference (str): Reference string.
spoken (str): Spoken string.
Returns:
float: Match score between 0 and 1.
Raises:
ValueError: If either reference or spoken is not a string.
"""
if not isinstance(refence, str) or not isinstance(spoken, str):
raise ValueError(
"Invalid input. Reference and spoken should be strings.")
if spoken == "":
return 0
normalizer = BasicTextNormalizer()
spoken = clean_transcription(spoken)
predection = normalizer(spoken)
refence = normalizer(refence)
wer_score = wer.compute(references=[refence], predictions=[predection])
score = 1 - wer_score
return score
def split_to_l(text, answer):
"""
Splits the given text into chunks of length 'l' based on the answer.
Args:
text (str): The input text to be split.
answer (str): The answer used to determine the chunk size.
Returns:
tuple: A tuple containing the chunks of text, the indices of the chunks, and the length of each chunk.
"""
if not isinstance(text, str) or not isinstance(answer, str):
raise ValueError("Invalid input. Text and answer should be strings.")
l = len(answer.split(" "))
text_words = text.split(" ")
chunks = []
indices = []
for i in range(0, len(text_words), l):
chunk = " ".join(text_words[i: i + l])
chunks.append(chunk)
indices.append(i)
return chunks, indices, l
def reindex_data(data, index, l):
"""
Reindexes a dictionary with keys ranging from 0 to l-1.
Args:
data (dict): Original dictionary.
index (int): Starting index for reindexing.
l (int): Length of the reindexed dictionary.
Returns:
dict: Reindexed dictionary.
Raises:
ValueError: If the input data is not a dictionary, or if index or l are not integers.
"""
if not isinstance(data, dict) or not isinstance(index, int) or not isinstance(l, int):
raise ValueError(
"Invalid input. Data should be a dictionary, index and l should be integers.")
reindexed_data = {}
for i in range(l):
original_index = index + i
reindexed_data[i] = data[original_index]
return reindexed_data
def process_image(im, data):
"""
Processes an image by extracting text regions.
Args:
im (PIL.Image.Image): Input image.
data (dict): Data containing information about text regions.
Returns:
numpy.ndarray: Processed image with text regions highlighted.
Raises:
ValueError: If the input image is not a PIL Image object or if the data is not a dictionary.
"""
im_array = np.array(im)
hg, wg, _ = im_array.shape
text_y = np.max([data[i]["coordinates"][1]
for i in range(len(data))])
text_x = np.max([data[i]["coordinates"][0]
for i in range(len(data))])
text_start_x = np.min([data[i]["coordinates"][0]
for i in range(len(data))])
text_start_y = np.min([data[i]["coordinates"][1]
for i in range(len(data))])
max_height = int(np.mean([data[i]["coordinates"][3]
for i in range(len(data))]))
max_width = int(np.mean([data[i]["coordinates"][2]
for i in range(len(data))]))
wall = np.zeros((hg, wg, 3), np.uint8)
wall[text_start_y:text_y + max_height, text_start_x:text_x + max_width] = \
im_array[text_start_y:text_y + max_height,
text_start_x:text_x + max_width, :]
for i in range(1, len(data)):
x, y, w, h = data[i]["coordinates"]
wall = draw_rectangle(wall, x, y, w, h)
return wall
def run(stream, image):
"""
Processes an image and transcribes audio.
Args:
stream (str or tuple): Audio source.
image (PIL.Image.Image): Input image.
Returns:
numpy.ndarray or PIL.Image.Image: Processed image data.
Raises:
ValueError: If the input stream is not a valid type or if the input image is not a PIL Image object.
"""
if not isinstance(stream, (str, tuple)):
raise ValueError(
"Invalid input. Stream should be either a file path or a tuple of (sampling_rate, raw_audio).")
data = extract_text(image)
im_text_ = [data[i]["text"] for i in range(len(data))]
im_text = " ".join(im_text_)
trns_text = transcribe(stream)
chunks, index, l = split_to_l(im_text, trns_text)
im_array = np.array(Image.open(image))
data2 = None
for i in range(len(chunks)):
if match(chunks[i], trns_text) >= 0.10:
data2 = reindex_data(data, index[i], l)
break
if data2 is not None:
return process_image(im_array, data2)
else:
return im_array
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