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import os
import json
import random
import librosa
import numpy as np
import gradio as gr
from typing import Any, List, Dict, Tuple
from utils import meow_stretch, get_word_lengths
from config import config, BaseConfig
COUNTER = 0
''' Gradio Input/Output Configurations '''
inputs: str = 'text'
outputs: gr.Audio = gr.Audio()
def load_meows(cfg: BaseConfig) -> List[Dict[str, Any]]:
meow_dir = os.path.dirname(cfg.manifest_path)
with open(cfg.manifest_path, mode='r') as fr:
lines = fr.readlines()
items = []
for line in lines:
item = json.loads(line)
item['audio'], item['rate'] = librosa.load(os.path.join(meow_dir, item['audio_filepath']), sr=None)
items.append(item)
return items
def extract_meows_weights(items: List[Dict[str, Any]]) -> Tuple[List[np.ndarray], List[float]]:
meows = [item['audio'] for item in items]
weights = [item['weight'] for item in items]
return meows, weights
''' Load meows '''
meow_items = load_meows(config)
meows, weights = extract_meows_weights(meow_items)
def predict(text: str) -> str:
word_lengths = get_word_lengths(text)
selected_meows = random.choices(meows, weights=weights, k=len(word_lengths))
transformed_meows = [
meow_stretch(
meow, wl,
init_factor=config.init_factor,
add_factor=config.add_factor,
power_factor=config.power_factor
) for meow, wl in zip(selected_meows, word_lengths)
]
result_meows = np.concatenate(transformed_meows, axis=0)
return (config.sample_rate, result_meows)
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