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import base64
import json
import os
import tempfile
from pathlib import Path
import soundfile as sf
import AIModels
import models
import utilsFileIO
from constants import app_logger, sample_rate_resample
def get_tts(text: str, language: str, tmp_prefix="audio_", tmp_suffix=".wav") -> str:
"""
Generate text-to-speech (TTS) audio for the given text and language.
Args:
text (str): The text to be converted to speech.
language (str): The language of the text. Supported languages are "en" (English) and "de" (German).
tmp_prefix (str, optional): The temporary directory to use for temporary files.
tmp_suffix (str, optional): The temporary directory to use for temporary files.
Returns:
str: The path to the generated audio file.
Raises:
NotImplementedError: If the provided language is not supported.
Notes:
This function uses the Silero TTS model to generate the audio. The model and speaker are selected based on the provided language.
"""
if text is None or len(text) == 0:
raise ValueError(f"cannot read an empty/None text: '{text}'...")
if language is None or len(language) == 0:
raise NotImplementedError(f"Not tested/supported with '{language}' language...")
tmp_dir = Path(tempfile.gettempdir())
try:
model, _, speaker, sample_rate = models.__silero_tts(
language, output_folder=tmp_dir
)
except ValueError:
model, _, sample_rate, _, _, speaker = models.__silero_tts(
language, output_folder=tmp_dir
)
app_logger.info(f"model speaker #0: {speaker} ...")
with tempfile.NamedTemporaryFile(prefix=tmp_prefix, suffix=tmp_suffix, delete=False) as tmp_audio_file:
app_logger.info(f"tmp_audio_file output: {tmp_audio_file.name} ...")
audio_paths = model.save_wav(text=text, speaker=speaker, sample_rate=sample_rate, audio_path=str(tmp_audio_file.name))
app_logger.info(f"audio_paths output: {audio_paths} ...")
return audio_paths
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