Update app.py

app.py

@@ -1,162 +1,161 @@
-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
-from scipy.signal import resample
-
-MODEL_NAME = "openai/whisper-tiny"
-BATCH_SIZE = 8
-# device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    # device=device,
-)
-
-arabic_bad_Words = pd.read_csv("arabic_bad_words_dataset.csv")
-english_bad_Words = pd.read_csv("english_bad_words_dataset.csv")
-
-
-def clean_english_word(word):
-    # Use regex to remove special characters, punctuation, and spaces around words
-    cleaned_text = re.sub(r'^[\s\W_]+|[\s\W_]+$', '', word)
-    return cleaned_text
-
-def clean_arabic_word(word):
-    # Define a regex pattern to match any non-Arabic letter character
-    pattern = r'[^\u0600-\u06FF]'
-    # Replace any character matching the pattern with an empty string
-    cleaned_word = re.sub(pattern, '', word)
-    return cleaned_word
-
-def classifier(word_list_with_timestamp, language):
-
-    foul_words = []
-    negative_timestamps = []
-
-    if language == "English":
-        list_to_search = set(english_bad_Words["words"])
-        for item in word_list_with_timestamp:
-            word = clean_english_word(item['text'])
-            if word in list_to_search:
-                foul_words.append(word)
-                negative_timestamps.append(item['timestamp'])
-    else:
-        list_to_search = list(arabic_bad_Words["words"])
-        for item in word_list_with_timestamp:
-            word = clean_arabic_word(item['text'])
-            for word_in_list in list_to_search:
-                if word_in_list == word:
-                    foul_words.append(word)
-                    negative_timestamps.append(item['timestamp'])
-                    break   
-        
-    return [foul_words, 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(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":
-        timestamp_type = True
-    else:
-        timestamp_type = "word"
-
-    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'])
-
-    foul_words, negative_timestamps = classifier(output['chunks'], audio_language)
-    foul_words = ", ".join(foul_words)
-
-
-    audio_output = mute_audio_range(input_audio, negative_timestamps)
-    # Save the output audio to a BytesIO object
-    output_buffer = io.BytesIO()
-    audio_output.export(output_buffer, format="wav")
-    output_buffer.seek(0)
-    
-    # Read the audio data from the BytesIO buffer
-    sample_rate = audio_output.frame_rate
-    audio_data = np.frombuffer(output_buffer.read(), dtype=np.int16)
-
-
-    return  [text, timestamps, foul_words, (sample_rate, audio_data)]
-
-examples = [
-        ["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("")
-    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():
-                clear_button = gr.ClearButton(value="Clear")
-                submit_button = gr.Button("Submit", variant="primary", )
-            
-        with gr.Column():
-            transcript_output = gr.Text(label="Transcript")
-            timestamp_output = gr.Text(label="Timestamps")
-            foul_words = gr.Text(label="Foul Words")
-            output_audio = gr.Audio(label="Output Audio", type="numpy")
-    
-    examples = gr.Examples(examples, inputs=[audio_input, audio_language, task, timestamp_type], outputs=[transcript_output, timestamp_output,  foul_words, output_audio], fn=transcribe, examples_per_page=20)
-
-    submit_button.click(fn=transcribe, inputs=[audio_input, audio_language, task, timestamp_type], outputs=[transcript_output, timestamp_output, foul_words, output_audio])
-    clear_button.add([audio_input, audio_language, task, timestamp_type, transcript_output, timestamp_output, foul_words, output_audio])
-
-
-if __name__ == "__main__":
-    demo.launch()
+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
+from scipy.signal import resample
+
+MODEL_NAME = "openai/whisper-tiny"
+BATCH_SIZE = 8
+# device = 0 if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    # device=device,
+)
+
+arabic_bad_Words = pd.read_csv("arabic_bad_words_dataset.csv")
+english_bad_Words = pd.read_csv("english_bad_words_dataset.csv")
+
+
+def clean_english_word(word):
+    # Use regex to remove special characters, punctuation, and spaces around words
+    cleaned_text = re.sub(r'^[\s\W_]+|[\s\W_]+$', '', word)
+    return cleaned_text
+
+def clean_arabic_word(word):
+    # Define a regex pattern to match any non-Arabic letter character
+    pattern = r'[^\u0600-\u06FF]'
+    # Replace any character matching the pattern with an empty string
+    cleaned_word = re.sub(pattern, '', word)
+    return cleaned_word
+
+def classifier(word_list_with_timestamp, language):
+
+    foul_words = []
+    negative_timestamps = []
+
+    if language == "English":
+        list_to_search = set(english_bad_Words["words"])
+        for item in word_list_with_timestamp:
+            word = clean_english_word(item['text'])
+            if word in list_to_search:
+                foul_words.append(word)
+                negative_timestamps.append(item['timestamp'])
+    else:
+        list_to_search = list(arabic_bad_Words["words"])
+        for item in word_list_with_timestamp:
+            word = clean_arabic_word(item['text'])
+            for word_in_list in list_to_search:
+                if word_in_list == word:
+                    foul_words.append(word)
+                    negative_timestamps.append(item['timestamp'])
+                    break   
+        
+    return [foul_words, 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(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":
+        timestamp_type = True
+    else:
+        timestamp_type = "word"
+
+    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'])
+
+    foul_words, negative_timestamps = classifier(output['chunks'], audio_language)
+    foul_words = ", ".join(foul_words)
+
+
+    audio_output = mute_audio_range(input_audio, negative_timestamps)
+    # Save the output audio to a BytesIO object
+    output_buffer = io.BytesIO()
+    audio_output.export(output_buffer, format="wav")
+    output_buffer.seek(0)
+    
+    # Read the audio data from the BytesIO buffer
+    sample_rate = audio_output.frame_rate
+    audio_data = np.frombuffer(output_buffer.read(), dtype=np.int16)
+
+
+    return  [text, timestamps, foul_words, (sample_rate, audio_data)]
+
+examples = [
+        ["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_hate_audio_1.mp3", 'Arabic', 'transcribe', 'word'],
+        ["arabic_english_audios/audios/arabic_hate_audio_2.flac", '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("")
+    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():
+                clear_button = gr.ClearButton(value="Clear")
+                submit_button = gr.Button("Submit", variant="primary", )
+            
+        with gr.Column():
+            transcript_output = gr.Text(label="Transcript")
+            timestamp_output = gr.Text(label="Timestamps")
+            foul_words = gr.Text(label="Foul Words")
+            output_audio = gr.Audio(label="Output Audio", type="numpy")
+    
+    examples = gr.Examples(examples, inputs=[audio_input, audio_language, task, timestamp_type], outputs=[transcript_output, timestamp_output,  foul_words, output_audio], fn=transcribe, examples_per_page=20)
+
+    submit_button.click(fn=transcribe, inputs=[audio_input, audio_language, task, timestamp_type], outputs=[transcript_output, timestamp_output, foul_words, output_audio])
+    clear_button.add([audio_input, audio_language, task, timestamp_type, transcript_output, timestamp_output, foul_words, output_audio])
+
+
+if __name__ == "__main__":
+    demo.launch()