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import logging | |
import os | |
import time | |
import numpy as np | |
import soundfile as sf | |
from typing import Optional, Generator, Tuple, List | |
from abc import ABC, abstractmethod | |
# Configure logging | |
logger = logging.getLogger(__name__) | |
class TTSBase(ABC): | |
"""Base class for all TTS engines | |
This abstract class defines the interface that all TTS engines must implement. | |
""" | |
def __init__(self, lang_code: str = 'z'): | |
"""Initialize the TTS engine | |
Args: | |
lang_code (str): Language code for the engine | |
""" | |
self.lang_code = lang_code | |
def generate_speech(self, text: str, voice: str = 'default', speed: float = 1.0) -> Optional[str]: | |
"""Generate speech from text | |
Args: | |
text (str): Input text to synthesize | |
voice (str): Voice ID to use | |
speed (float): Speech speed multiplier | |
Returns: | |
Optional[str]: Path to the generated audio file or None if generation fails | |
""" | |
pass | |
def generate_speech_stream(self, text: str, voice: str = 'default', speed: float = 1.0) -> Generator[Tuple[int, np.ndarray], None, None]: | |
"""Generate speech stream from text | |
Args: | |
text (str): Input text to synthesize | |
voice (str): Voice ID to use | |
speed (float): Speech speed multiplier | |
Yields: | |
tuple: (sample_rate, audio_data) pairs for each segment | |
""" | |
pass | |
def _generate_output_path(self, prefix: str = "tts", extension: str = "wav") -> str: | |
"""Generate a unique output path for the audio file | |
Args: | |
prefix (str): Prefix for the filename | |
extension (str): File extension | |
Returns: | |
str: Path to the output file | |
""" | |
timestamp = int(time.time() * 1000) | |
filename = f"{prefix}_{timestamp}.{extension}" | |
output_dir = os.path.join(os.getcwd(), "output") | |
os.makedirs(output_dir, exist_ok=True) | |
return os.path.join(output_dir, filename) | |