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| import os | |
| import re | |
| import sys | |
| import torch | |
| import json | |
| import numpy as np | |
| from omegaconf import OmegaConf | |
| from codeclm.trainer.codec_song_pl import CodecLM_PL | |
| from codeclm.models import CodecLM | |
| from separator import Separator | |
| def check_language_by_text(text): | |
| chinese_pattern = re.compile(r'[\u4e00-\u9fff]') | |
| english_pattern = re.compile(r'[a-zA-Z]') | |
| chinese_count = len(re.findall(chinese_pattern, text)) | |
| english_count = len(re.findall(english_pattern, text)) | |
| chinese_ratio = chinese_count / len(text) | |
| english_ratio = english_count / len(text) | |
| if chinese_ratio >= 0.2: | |
| return "zh" | |
| elif english_ratio >= 0.5: | |
| return "en" | |
| else: | |
| return "en" | |
| class LeVoInference(torch.nn.Module): | |
| def __init__(self, ckpt_path): | |
| super().__init__() | |
| torch.backends.cudnn.enabled = False | |
| OmegaConf.register_new_resolver("eval", lambda x: eval(x)) | |
| OmegaConf.register_new_resolver("concat", lambda *x: [xxx for xx in x for xxx in xx]) | |
| OmegaConf.register_new_resolver("get_fname", lambda: 'default') | |
| OmegaConf.register_new_resolver("load_yaml", lambda x: list(OmegaConf.load(x))) | |
| cfg_path = os.path.join(ckpt_path, 'config.yaml') | |
| pt_path = os.path.join(ckpt_path, 'model.pt') | |
| self.cfg = OmegaConf.load(cfg_path) | |
| self.cfg.mode = 'inference' | |
| self.max_duration = self.cfg.max_dur | |
| # Define model or load pretrained model | |
| model_light = CodecLM_PL(self.cfg, pt_path, version="v2") | |
| model_light = model_light.eval().cuda() | |
| model_light.audiolm.cfg = self.cfg | |
| self.model_lm = model_light.audiolm | |
| self.model_audio_tokenizer = model_light.audio_tokenizer | |
| self.model_seperate_tokenizer = model_light.seperate_tokenizer | |
| self.model = CodecLM(name = "tmp", | |
| lm = self.model_lm, | |
| audiotokenizer = self.model_audio_tokenizer, | |
| max_duration = self.max_duration, | |
| seperate_tokenizer = self.model_seperate_tokenizer, | |
| ) | |
| self.separator = Separator() | |
| self.default_params = dict( | |
| cfg_coef = 1.5, | |
| temperature = 1.0, | |
| top_k = 50, | |
| top_p = 0.0, | |
| record_tokens = True, | |
| record_window = 50, | |
| extend_stride = 5, | |
| duration = self.max_duration, | |
| ) | |
| self.model.set_generation_params(**self.default_params) | |
| def forward(self, lyric: str, description: str = None, prompt_audio_path: os.PathLike = None, genre: str = None, auto_prompt_path: os.PathLike = None, gen_type: str = "mixed", params = dict()): | |
| params = {**self.default_params, **params} | |
| self.model.set_generation_params(**params) | |
| if prompt_audio_path is not None and os.path.exists(prompt_audio_path): | |
| pmt_wav, vocal_wav, bgm_wav = self.separator.run(prompt_audio_path) | |
| melody_is_wav = True | |
| elif genre is not None and auto_prompt_path is not None: | |
| auto_prompt = torch.load(auto_prompt_path) | |
| lang = check_language_by_text(lyric) | |
| prompt_token = auto_prompt[genre][lang][np.random.randint(0, len(auto_prompt[genre][lang]))] | |
| pmt_wav = prompt_token[:,[0],:] | |
| vocal_wav = prompt_token[:,[1],:] | |
| bgm_wav = prompt_token[:,[2],:] | |
| melody_is_wav = False | |
| else: | |
| pmt_wav = None | |
| vocal_wav = None | |
| bgm_wav = None | |
| melody_is_wav = True | |
| if gen_type == 'bgm': | |
| description = '[Musicality-very-high]' + ', ' + '[Pure-Music]' + ', ' + description.lower() if description else '.' | |
| else: | |
| description = description.lower() if description else '.' | |
| description = '[Musicality-very-high]' + ', ' + description | |
| generate_inp = { | |
| 'lyrics': [lyric.replace(" ", " ")] if gen_type != 'bgm' else '.', | |
| 'descriptions': [description], | |
| 'melody_wavs': pmt_wav, | |
| 'vocal_wavs': vocal_wav, | |
| 'bgm_wavs': bgm_wav, | |
| 'melody_is_wav': melody_is_wav, | |
| } | |
| with torch.autocast(device_type="cuda", dtype=torch.float16): | |
| tokens = self.model.generate(**generate_inp, return_tokens=True) | |
| with torch.no_grad(): | |
| if melody_is_wav: | |
| wav_seperate = self.model.generate_audio(tokens, pmt_wav, vocal_wav, bgm_wav, gen_type=gen_type) | |
| else: | |
| wav_seperate = self.model.generate_audio(tokens, gen_type=gen_type) | |
| return wav_seperate[0] | |