Update app.py

app.py

@@ -2,7 +2,10 @@ import gradio as gr
 from transformers import pipeline
 import numpy as np
 import pandas as pd
-
+import re
+from pydub import AudioSegment
+from pydub.generators import Sine
+import io
 
 MODEL_NAME = "openai/whisper-large-v3"
 BATCH_SIZE = 8
@@ -15,14 +18,62 @@ pipe = pipeline(
     # device=device,
 )
 
-# eng_classifier = pipeline("text-classification", model="Hate-speech-CNERG/bert-base-uncased-hatexplain")
+arabic_bad_Words = pd.read_csv("arabic_bad_words_dataset.csv")
+english_bad_Words = pd.read_csv("english_bad_words_dataset.csv")
+
+
+def clean_text(text):
+    # Use regex to remove special characters, punctuation, and spaces around words
+    cleaned_text = re.sub(r'^[\s\W_]+|[\s\W_]+$', '', text)
+    return cleaned_text
+
+def classifier(word_list_with_timestamp, language):
+    if language == "English":
+        list_to_search = set(english_bad_Words["words"])
+
+    else:
+        list_to_search = set(english_bad_Words["words"])
+    
+    output = []
+    negative_timestamps = []
+    for item in word_list_with_timestamp:
+        word = clean_text(item['text'])
+        if word in list_to_search:
+            output.append((item['text'], "negative"))
+            negative_timestamps.append(item['timestamp'])
+        else:
+            output.append((item['text'], "positive"))
+    return [output, negative_timestamps]
+
+def generate_bleep(duration_ms, frequency=1000):
+    sine_wave = Sine(frequency)
+    bleep = sine_wave.to_audio_segment(duration=duration_ms)
+    return bleep
+
+def mute_audio_range(audio_filepath, ranges, bleep_frequency=800):
+    audio = AudioSegment.from_file(audio_filepath)
+
+    for range in ranges:
+        start_time = range[0] - 0.1
+        end_time = range[-1] + 0.1
+        start_ms = start_time * 1000  # pydub works with milliseconds
+        end_ms = end_time * 1000
+        duration_ms = end_ms - start_ms
+        
+        # Generate the bleep sound
+        bleep_sound = generate_bleep(duration_ms, bleep_frequency)
+        
+        # Combine the original audio with the bleep sound
+        audio = audio[:start_ms] + bleep_sound + audio[end_ms:]
+
+    return audio
 
 def format_output_to_list(data):
     formatted_list = "\n".join([f"{item['timestamp'][0]}s - {item['timestamp'][1]}s \t : {item['text']}" for item in data])
     return formatted_list
 
-def transcribe(inputs, task, timestamp_type):
-    if inputs is None:
+def transcribe(input_audio, audio_language, task, timestamp_type):
+    if input_audio is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
 
     if timestamp_type == "sentence":
@@ -30,33 +81,42 @@ def transcribe(inputs, task, timestamp_type):
     else:
         timestamp_type = "word"
 
-    output = pipe(inputs, batch_size=BATCH_SIZE, return_timestamps=timestamp_type, generate_kwargs={"task": task})
+    output = pipe(input_audio, batch_size=BATCH_SIZE, return_timestamps=timestamp_type, generate_kwargs={"task": task})
     text = output['text']
+
     timestamps = format_output_to_list(output['chunks'])
-    return  [text, timestamps]
+
+    classifier_output, negative_timestamps = classifier(output['chunks'], audio_language)
+
+    audio_output = mute_audio_range(input_audio, negative_timestamps)
+    output_path = "output_audio.wav"
+    audio_output.export(output_path, format="wav")
+
+    return  [text, timestamps, classifier_output, output_path]
 
 examples = [
-        ["arabic_english_audios/audios/arabic_audio_1.wav"],
-        ["arabic_english_audios/audios/arabic_audio_2.wav"],
-        ["arabic_english_audios/audios/arabic_audio_3.wav"],
-        ["arabic_english_audios/audios/arabic_audio_4.wav"],
-        ["arabic_english_audios/audios/arabic_hate_audio_1.mp3"],
-        ["arabic_english_audios/audios/arabic_hate_audio_2.mp3"],
-        ["arabic_english_audios/audios/arabic_hate_audio_3.mp3"],
-        ["arabic_english_audios/audios/english_audio_1.wav"],
-        ["arabic_english_audios/audios/english_audio_2.mp3"],
-        ["arabic_english_audios/audios/english_audio_3.mp3"],
-        ["arabic_english_audios/audios/english_audio_4.mp3"],
-        ["arabic_english_audios/audios/english_audio_5.mp3"],
-        ["arabic_english_audios/audios/english_audio_6.wav"]
+        ["arabic_english_audios/audios/arabic_audio_1.wav", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_audio_2.wav", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_audio_3.wav", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_audio_4.wav", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_hate_audio_1.mp3", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_hate_audio_2.mp3", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_hate_audio_3.mp3", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/english_audio_1.wav", 'English', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/english_audio_2.mp3", 'English', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/english_audio_3.mp3", 'English', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/english_audio_4.mp3", 'English', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/english_audio_5.mp3", 'English', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/english_audio_6.wav", 'English', 'transcribe', 'word']
     ]
 
 with gr.Blocks(theme=gr.themes.Default()) as demo:
     gr.HTML("<h2 style='text-align: center;'>Transcribing Audio with Timestamps using whisper-large-v3</h2>")
-    gr.Markdown("")
+    # gr.Markdown("")
     with gr.Row():
         with gr.Column():
             audio_input = gr.Audio(sources=["upload", 'microphone'], type="filepath", label="Audio file")
+            audio_language = gr.Radio(["Arabic", "English"], label="Audio Language")
             task = gr.Radio(["transcribe", "translate"], label="Task")
             timestamp_type = gr.Radio(["sentence", "word"], label="Timestamp Type")
             with gr.Row():
@@ -66,11 +126,13 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
         with gr.Column():
             transcript_output = gr.Text(label="Transcript")
             timestamp_output = gr.Text(label="Timestamps")
+            highlighted_output = gr.HighlightedText(label="Words Classification", combine_adjacent=True, show_legend=True, color_map={"negative": "red", "positive": "green"})
+            output_audio = gr.Audio(label="Output Audio")
     
-    examples = gr.Examples(examples, inputs=[audio_input, task, timestamp_type], outputs=[transcript_output, timestamp_output], fn=transcribe, examples_per_page=20)
+    examples = gr.Examples(examples, inputs=[audio_input, audio_language, task, timestamp_type], outputs=[transcript_output, timestamp_output], fn=transcribe, examples_per_page=20)
 
-    submit_button.click(fn=transcribe, inputs=[audio_input, task, timestamp_type], outputs=[transcript_output, timestamp_output])
-    clear_button.add([audio_input, task, timestamp_type, transcript_output, timestamp_output])
+    submit_button.click(fn=transcribe, inputs=[audio_input, audio_language, task, timestamp_type], outputs=[transcript_output, timestamp_output, highlighted_output, output_audio])
+    clear_button.add([audio_input, audio_language, task, timestamp_type, transcript_output, timestamp_output, highlighted_output, output_audio])
 
 
 if __name__ == "__main__":