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import gradio as gr |
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from gtts import gTTS |
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from pydub import AudioSegment |
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import pandas as pd |
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import io |
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csv_files = {"A1": "OF3KA1.csv", "A2": "OF3KA2.csv", "B1": "OF3KB1.csv", "B2": "OF3KB2.csv", "C1": "OF3KC1.csv", "5K": "OF5K.csv"} |
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def load_data(level): |
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csv_file_path = f"./{csv_files[level]}" |
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data = pd.read_csv(csv_file_path) |
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return data |
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def generate_speech(t1, level, t2, x, y, t3, audio_option, t4, all_pos, noun, verb, adjective, adverb, preposition): |
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data = load_data(level) |
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x, y = int(x), int(y) |
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if all_pos: |
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filtered_df = data[(data['SID'] >= x) & (data['SID'] <= y)] |
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else: |
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filtered_df = pd.DataFrame() |
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if noun: |
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filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'noun')]) |
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if verb: |
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filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'verb')]) |
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if adjective: |
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filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'adjective')]) |
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if adverb: |
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filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'adverb')]) |
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if preposition: |
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filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'preposition')]) |
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filtered_df = filtered_df.drop_duplicates() |
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combined_audio = AudioSegment.silent(duration=1000) |
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if filtered_df.empty: |
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sentence = "No matching words found." |
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tts = gTTS(text=sentence, lang='en') |
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mp3_fp = io.BytesIO() |
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tts.write_to_fp(mp3_fp) |
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mp3_fp.seek(0) |
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else: |
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for _, row in filtered_df.iterrows(): |
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if audio_option == "Audio without number": |
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sentence = f"{row['WORD']} is {row['POS']}" |
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elif audio_option == "Audio with number": |
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sentence = f"{row['SID']}. {row['WORD']}. {row['WORD']} is {row['POS']}" |
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else: |
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sentence = f"{row['WORD']}" |
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tts = gTTS(text=sentence, lang='en') |
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mp3_fp = io.BytesIO() |
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tts.write_to_fp(mp3_fp) |
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mp3_fp.seek(0) |
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sentence_audio = AudioSegment.from_file(mp3_fp, format="mp3") |
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combined_audio += sentence_audio + AudioSegment.silent(duration=1000) |
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mp3_io = io.BytesIO() |
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combined_audio.export(mp3_io, format='mp3') |
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mp3_io.seek(0) |
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return mp3_io.getvalue() |
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iface = gr.Interface( |
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fn=generate_speech, |
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inputs=[ |
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gr.Markdown("#### [1] Select Level"), |
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gr.Dropdown(label="Select Level", choices=['A1', 'A2', 'B1', 'B2', 'C1', '5K']), |
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gr.Markdown("#### [2] Select Range: A1(1~734), A2(819), B1(769), B2(717), C1(1392), 5K(1392)"), |
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gr.Number(label= "Start Number (x)"), |
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gr.Number(label = "End Number (y)"), |
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gr.Markdown("#### [3] Audio options (text): Two, 'able'. 'Able' is noun."), |
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gr.Radio(label="Audio Option", choices=["Audio with number/POS", "Audio with POS", "Word only"]), |
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gr.Markdown("#### Select Part of Speech"), |
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gr.Checkbox(label="Any", value=False), |
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gr.Checkbox(label="Noun", value=False), |
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gr.Checkbox(label="Verb", value=False), |
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gr.Checkbox(label="Adjective", value=False), |
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gr.Checkbox(label="Adverb", value=False), |
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gr.Checkbox(label="Preposition", value=False) |
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], |
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outputs=gr.Audio(label="Generated Speech"), |
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title="Oxford Learner Vocabulary by CEFR levels: Learn with Sound", |
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description="Choose a level, define the starting and ending numbers, select the audio option, and filter by Part of Speech if desired. The system will create a single audio file. After submission, you have the option to download the audio file." |
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) |
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iface.launch(share=True, debug=True) |
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