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| import os | |
| import re | |
| import torch | |
| import numpy as np | |
| import yaml | |
| from pathlib import Path | |
| #### path related code BEGIN #### | |
| def get_session_path(session_id): | |
| return Path(f'output/sessions/{session_id}') | |
| def get_system_voice_preset_path(): | |
| return Path('data/voice_presets') | |
| def get_session_voice_preset_path(session_id): | |
| return Path(f'{get_session_path(session_id)}/voice_presets') | |
| def get_session_audio_path(session_id): | |
| return Path(f'{get_session_path(session_id)}/audio') | |
| def rescale_to_match_energy(segment1, segment2): | |
| ratio = get_energy_ratio(segment1, segment2) | |
| recaled_segment1 = segment1 / ratio | |
| return recaled_segment1.numpy() | |
| #### path related code END #### | |
| def text_to_abbrev_prompt(input_text): | |
| return re.sub(r'[^a-zA-Z_]', '', '_'.join(input_text.split()[:5])) | |
| def get_energy(x): | |
| return np.mean(x ** 2) | |
| def get_energy_ratio(segment1, segment2): | |
| energy1 = get_energy(segment1) | |
| energy2 = max(get_energy(segment2), 1e-10) | |
| ratio = (energy1 / energy2) ** 0.5 | |
| ratio = torch.tensor(ratio) | |
| ratio = torch.clamp(ratio, 0.02, 50) | |
| return ratio | |
| def fade(audio_data, fade_duration=2, sr=32000): | |
| audio_duration = audio_data.shape[0] / sr | |
| # automated choose fade duration | |
| if audio_duration >=8: | |
| # keep fade_duration 2 | |
| pass | |
| else: | |
| fade_duration = audio_duration / 5 | |
| fade_sampels = int(sr * fade_duration) | |
| fade_in = np.linspace(0, 1, fade_sampels) | |
| fade_out = np.linspace(1, 0, fade_sampels) | |
| audio_data_fade_in = audio_data[:fade_sampels] * fade_in | |
| audio_data_fade_out = audio_data[-fade_sampels:] * fade_out | |
| audio_data_faded = np.concatenate((audio_data_fade_in, audio_data[len(fade_in):-len(fade_out)], audio_data_fade_out)) | |
| return audio_data_faded | |
| # def get_key(config='config.yaml'): | |
| # with open('config.yaml', 'r') as file: | |
| # config = yaml.safe_load(file) | |
| # return config['OpenAI-Key'] if 'OpenAI-Key' in config else None | |
| def get_api_key(): | |
| api_key = os.environ.get('OPENAI_KEY') | |
| return api_key | |