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import os.path
import app_state
import numpy as np
from pydub.playback import play
from pydub import AudioSegment
from torch.cuda import is_available
APP_NAME = "WeeaBlind"
test_video_name = "./output/download.webm"
default_sample_path = "./output/sample.wav"
test_start_time = 94
test_end_time = 1324
gpu_detected = is_available()
def create_output_dir():
path = './output/files'
if not os.path.exists(path):
os.makedirs(path)
def get_output_path(input, suffix, prefix='', path=''):
filename = os.path.basename(input)
filename_without_extension = os.path.splitext(filename)[0]
return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'output', path, f"{prefix}{filename_without_extension}{suffix}")
def timecode_to_seconds(timecode):
parts = list(map(float, timecode.split(':')))
seconds = parts[-1]
if len(parts) > 1:
seconds += parts[-2] * 60
if len(parts) > 2:
seconds += parts[-3] * 3600
return seconds
def seconds_to_timecode(seconds):
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
seconds = seconds % 60
timecode = ""
if hours:
timecode += f"{hours}:"
if minutes:
timecode += f"{minutes}:"
timecode = f"{timecode}{seconds:05.2f}"
return timecode
# Finds the closest element in an arry to the given value
def find_nearest(array, value):
return (np.abs(np.asarray(array) - value)).argmin()
def sampleVoice(text, output=default_sample_path):
play(AudioSegment.from_file(app_state.sample_speaker.speak(text, output)))
snippet_export_path = get_output_path("video_snippet", "wav") |