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import gradio as gr
from gtts import gTTS
from pydub import AudioSegment
import pandas as pd
import io
csv_files = {"A1": "OF3KA1.csv", "A2": "OF3KA2.csv", "B1": "OF3KB1.csv", "B2": "OF3KB2.csv", "C1": "OF3KC1.csv", "5K": "OF5K.csv"}
def load_data(level):
csv_file_path = f"./{csv_files[level]}"
data = pd.read_csv(csv_file_path)
return data
def generate_speech(t1, level, t2, x, y, t3, audio_option, t4, all_pos, noun, verb, adjective, adverb, preposition):
data = load_data(level)
x, y = int(x), int(y)
if all_pos: # Include all words if "ALL" is selected
filtered_df = data[(data['SID'] >= x) & (data['SID'] <= y)]
else:
# Initialize an empty DataFrame to accumulate results
filtered_df = pd.DataFrame()
# Check each selected POS and append matches to the filtered_df
if noun:
filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'noun')])
if verb:
filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'verb')])
if adjective:
filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'adjective')])
if adverb:
filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'adverb')])
if preposition:
filtered_df = filtered_df.append(data[(data['SID'] >= x) & (data['SID'] <= y) & (data['POS'].str.lower() == 'preposition')])
# Ensure the filtered_df is unique in case of overlapping conditions
filtered_df = filtered_df.drop_duplicates()
# Proceed with generating the speech
combined_audio = AudioSegment.silent(duration=1000) # Initial silence
if filtered_df.empty:
sentence = "No matching words found."
tts = gTTS(text=sentence, lang='en')
mp3_fp = io.BytesIO()
tts.write_to_fp(mp3_fp)
mp3_fp.seek(0)
else:
for _, row in filtered_df.iterrows():
# Adjust the sentence based on the audio_option
if audio_option == "Audio with POS":
sentence = f"{row['WORD']} is {row['POS']}"
elif audio_option == "Audio with number/POS":
sentence = f"{row['SID']}. {row['WORD']}. {row['WORD']} is {row['POS']}"
else: # "Word only"
sentence = f"{row['WORD']}"
tts = gTTS(text=sentence, lang='en')
mp3_fp = io.BytesIO()
tts.write_to_fp(mp3_fp)
mp3_fp.seek(0)
sentence_audio = AudioSegment.from_file(mp3_fp, format="mp3")
# Add a pause after each word
combined_audio += sentence_audio + AudioSegment.silent(duration=1000)
mp3_io = io.BytesIO()
combined_audio.export(mp3_io, format='mp3')
mp3_io.seek(0)
return mp3_io.getvalue()
# Adjust inputs for POS with checkboxes
iface = gr.Interface(
fn=generate_speech,
inputs=[
gr.Markdown("#### [1] Select Level"), # Adding a label for POS options
gr.Dropdown(label="Select Level", choices=['A1', 'A2', 'B1', 'B2', 'C1', '5K']),
gr.Markdown("#### [2] Select Range: A1(1~734), A2(819), B1(769), B2(717), C1(1392), 5K(1392)"),
gr.Number(label= "Start Number (x)"),
gr.Number(label = "End Number (y)"),
gr.Markdown("#### [3] Audio options (text): Two, 'able'. 'Able' is noun."),
gr.Radio(label="Audio Option", choices=["Audio with number/POS", "Audio with POS", "Word only"]),
gr.Markdown("#### Select Part of Speech"), # Adding a label for POS options
gr.Checkbox(label="Any", value=False),
gr.Checkbox(label="Noun", value=False),
gr.Checkbox(label="Verb", value=False),
gr.Checkbox(label="Adjective", value=False),
gr.Checkbox(label="Adverb", value=False),
gr.Checkbox(label="Preposition", value=False)
],
outputs=gr.Audio(label="Generated Speech"),
title="Oxford Learner Vocabulary by CEFR levels: Learn with Sound",
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."
)
iface.launch(share=True, debug=True)
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