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| import os | |
| import logging | |
| logging.getLogger('matplotlib').setLevel(logging.WARNING) | |
| logging.basicConfig(level=logging.DEBUG, | |
| format='%(asctime)s %(levelname)s %(message)s') | |
| import torch | |
| import numpy as np | |
| import random | |
| import librosa | |
| from cosyvoice.utils.file_utils import load_wav | |
| from cosyvoice.cli.cosyvoice import CosyVoice | |
| cosyvoice= CosyVoice('speech_tts/CosyVoice-300M') | |
| cosyvoice_sft= CosyVoice('speech_tts/CosyVoice-300M-SFT') | |
| cosyvoice_instruct= CosyVoice('speech_tts/CosyVoice-300M-Instruct') | |
| example_tts_text = ["我们走的每一步，都是我们策略的一部分；你看到的所有一切，包括我此刻与你交谈，所做的一切，所说的每一句话，都有深远的含义。", | |
| "那位喜剧演员真有才，[laughter]一开口就让全场观众爆笑。", | |
| "他搞的一个恶作剧，让大家<laughter>忍俊不禁</laughter>。"] | |
| example_prompt_text = ["我是通义实验室语音团队全新推出的生成式语音大模型，提供舒适自然的语音合成能力。", | |
| "I am a newly launched generative speech large model by the Qwen Voice Team of the Tongyi Laboratory, offering comfortable and natural text-to-speech synthesis capabilities."] | |
| prompt_sr, target_sr = 16000, 22050 | |
| default_data = np.zeros(target_sr) | |
| def set_all_random_seed(seed): | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| max_val = 0.8 | |
| def postprocess(speech, top_db=60, hop_length=220, win_length=440): | |
| speech, _ = librosa.effects.trim( | |
| speech, top_db=top_db, | |
| frame_length=win_length, | |
| hop_length=hop_length | |
| ) | |
| if speech.abs().max() > max_val: | |
| speech = speech / speech.abs().max() * max_val | |
| speech = torch.concat([speech, torch.zeros(1, int(target_sr * 0.2))], dim=1) | |
| return speech | |
| def use_instruct(text): | |
| for symbol in ['<endofprompt>', '<laughter>', '</laughter>', '<strong>', '</strong>', '[laughter]', '[breath]']: | |
| if symbol in text: | |
| return True | |
| return False | |