diff --git a/codes/train.py b/codes/train.py index ad4ce62a..a5c16376 100644 --- a/codes/train.py +++ b/codes/train.py @@ -327,7 +327,7 @@ class Trainer: if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_clvp.yml') + parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_diffusion_tts_mel_flat_autoregressive_inputs.yml') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') args = parser.parse_args() opt = option.parse(args.opt, is_train=True) diff --git a/codes/trainer/eval/audio_diffusion_fid.py b/codes/trainer/eval/audio_diffusion_fid.py index af008c9d..16216d5a 100644 --- a/codes/trainer/eval/audio_diffusion_fid.py +++ b/codes/trainer/eval/audio_diffusion_fid.py @@ -12,6 +12,7 @@ import numpy as np import trainer.eval.evaluator as evaluator from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes from data.audio.unsupervised_audio_dataset import load_audio +from data.audio.voice_tokenizer import VoiceBpeTokenizer from models.clip.mel_text_clip import MelTextCLIP from models.audio.tts.tacotron2 import text_to_sequence from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \ @@ -23,6 +24,9 @@ from utils.util import ceil_multiple, opt_get class AudioDiffusionFid(evaluator.Evaluator): """ Evaluator produces generate from a diffusion model, then uses a CLIP model to judge the similarity between text & speech. + + This evaluator is kind of a mess. It has been repeatedly modified to work with several different model types, which + means it is bloated beyond belief. I would not recommend attempting to understand what is going on here. """ def __init__(self, model, opt_eval, env): super().__init__(model, opt_eval, env, uses_all_ddp=True) @@ -53,11 +57,22 @@ class AudioDiffusionFid(evaluator.Evaluator): elif mode == 'vocoder': self.local_modules['dvae'] = load_speech_dvae().cpu() self.diffusion_fn = self.perform_diffusion_vocoder - elif mode == 'tts9_mel': + elif 'tts9_mel' in mode: mel_means, self.mel_max, self.mel_min, mel_stds, mel_vars = torch.load('../experiments/univnet_mel_norms.pth') + self.bpe_tokenizer = VoiceBpeTokenizer('../experiments/bpe_lowercase_asr_256.json') self.local_modules['dvae'] = load_speech_dvae().cpu() self.local_modules['vocoder'] = load_univnet_vocoder().cpu() self.diffusion_fn = self.perform_diffusion_tts9_mel_from_codes + if mode == 'tts9_mel_autoin': + self.local_modules['autoregressive'] = load_model_from_config("../experiments/train_gpt_tts_unified.yml", + model_name='gpt', + also_load_savepoint=False, + load_path='../experiments/unified_large_diverse_basis.pth', + device=torch.device('cpu')).cuda().eval() + self.tts9_codegen = self.tts9_get_autoregressive_codes + else: + self.tts9_codegen = self.tts9_get_dvae_codes + def perform_diffusion_tts(self, audio, codes, text, sample_rate=5500): real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0) @@ -119,25 +134,28 @@ class AudioDiffusionFid(evaluator.Evaluator): 'unaligned_input': torch.tensor(text_codes, device=audio.device).unsqueeze(0)}) return gen, real_resampled, sample_rate + def tts9_get_autoregressive_codes(self, mel, text): + mel_codes = convert_mel_to_codes(self.local_modules['dvae'], mel) + text_codes = torch.LongTensor(self.bpe_tokenizer.encode(text)).unsqueeze(0).to(mel.device) + cond_inputs = mel.unsqueeze(1) + auto_latents = self.local_modules['autoregressive'].forward(cond_inputs, text_codes, + torch.tensor([text_codes.shape[-1]], device=mel.device), + mel_codes, + torch.tensor([mel_codes.shape[-1]], device=mel.device), + text_first=True, raw_mels=None, return_latent=True, + clip_inputs=False) + return auto_latents + + def tts9_get_dvae_codes(self, mel, text): + return convert_mel_to_codes(self.local_modules['dvae'], mel) def perform_diffusion_tts9_mel_from_codes(self, audio, codes, text): SAMPLE_RATE = 24000 mel = wav_to_mel(audio) - mel_codes = convert_mel_to_codes(self.local_modules['dvae'], mel) + mel_codes = self.tts9_codegen(mel, text) real_resampled = torchaudio.functional.resample(audio, 22050, SAMPLE_RATE).unsqueeze(0) univnet_mel = wav_to_univnet_mel(real_resampled, do_normalization=False) # to be used for a conditioning input, but also guides output shape. - - output_size = univnet_mel.shape[-1] - aligned_codes_compression_factor = output_size // mel_codes.shape[-1] - if hasattr(self.model, 'alignment_size'): - padded_size = ceil_multiple(output_size, self.model.alignment_size) - padding_added = padded_size - output_size - padding_needed_for_codes = padding_added // aligned_codes_compression_factor - if padding_needed_for_codes > 0: - mel_codes = F.pad(mel_codes, (0, padding_needed_for_codes)) - output_shape = (1, 100, padded_size) - else: - output_shape = univnet_mel.shape + output_shape = univnet_mel.shape gen_mel = self.diffuser.p_sample_loop(self.model, output_shape, model_kwargs={'aligned_conditioning': mel_codes, 'conditioning_input': univnet_mel}) @@ -265,12 +283,12 @@ if __name__ == '__main__': from utils.util import load_model_from_config # 34k; no conditioning_free: {'frechet_distance': tensor(1.4559, device='cuda:0', dtype=torch.float64), 'intelligibility_loss': tensor(151.9112, device='cuda:0')} # 34k; conditioning_free: {'frechet_distance': tensor(1.4059, device='cuda:0', dtype=torch.float64), 'intelligibility_loss': tensor(118.3377, device='cuda:0')} - diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts_mel_flat.yml', 'generator', + diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts_mel_flat_autoregressive_inputs.yml', 'generator', also_load_savepoint=False, - load_path='X:\\dlas\\experiments\\train_diffusion_tts_mel_flat0\\models\\34000_generator_ema.pth').cuda() + load_path='X:\\dlas\\experiments\\tts_flat_autoregressive_inputs_r2_initial\\models\\500_generator.pth').cuda() opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100, 'conditioning_free': True, 'conditioning_free_k': 1, - 'diffusion_schedule': 'linear', 'diffusion_type': 'tts9_mel'} - env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 560, 'device': 'cuda', 'opt': {}} + 'diffusion_schedule': 'linear', 'diffusion_type': 'tts9_mel_autoin'} + env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 561, 'device': 'cuda', 'opt': {}} eval = AudioDiffusionFid(diffusion, opt_eval, env) print(eval.perform_eval())