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