The task is to develop a Python program that converts text into speech using pre-recorded phonemes of the developer's own voice. The goal is to create this system without relying on external APIs or libraries for TTS conversion, showcasing ingenuity and technical skills in natural language processing and sound synthesis.
A text string provided by the user.
The user-provided text is articulated aloud using the pre-recorded samples of the developer's voice.
# Example of phoneme mapping
PHONEME_DIR = 'path_to_phoneme_directory/'
phoneme_map = {
'ا': 'a.wav',
'ب': 'b.wav',
'پا': 'pa.wav',
# More mappings here
}
The script uses a directory containing WAV files for each phoneme. A mapping dictionary associates Persian letters or letter combinations with phoneme file names.
def normalize_text(text):
# Implement text normalization, e.g., remove punctuation, diacritics
normalized_text = text.lower() # Simplified normalization example
return normalized_text
The function cleans input text for consistent processing.
def text_to_phonemes(text):
phonemes = []
for char in text:
if char in phoneme_map:
phonemes.append(phoneme_map[char])
return phonemes
Maps normalized text to phonemes using the defined phoneme map.
def trim_silence(phoneme_file):
# Remove silence from phoneme file
pass
def normalize_volume(phoneme_file):
# Adjust volume for consistency
pass
These functions ensure audio quality by trimming silence and normalizing volume.
def synthesize_speech(phonemes):
# Combine phoneme WAV files into a single audio file
speech_output = 'output.wav'
# Audio processing logic to combine phonemes
pass
Combines phoneme files into a coherent speech output, handling pauses and transitions.
Input Requirement: The user inputs Persian text via console.
Output: The synthesized speech is saved as an audio file.
Customization: Modify phoneme mapping and add recordings for other languages or combinations.
This project provides insight into the mechanics of TTS systems and offers a foundational approach for further enhancements.
