forked from mrq/DL-Art-School
295 lines
17 KiB
Python
295 lines
17 KiB
Python
import os
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import os.path as osp
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import torch
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import torchaudio
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from pytorch_fid.fid_score import calculate_frechet_distance
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from torch import distributed
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from tqdm import tqdm
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from transformers import Wav2Vec2ForCTC
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import torch.nn.functional as F
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import numpy as np
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import trainer.eval.evaluator as evaluator
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from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes
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from data.audio.unsupervised_audio_dataset import load_audio
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from data.audio.voice_tokenizer import VoiceBpeTokenizer
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from models.clip.mel_text_clip import MelTextCLIP
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from models.audio.tts.tacotron2 import text_to_sequence
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from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \
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convert_mel_to_codes, load_univnet_vocoder, wav_to_univnet_mel
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from trainer.injectors.audio_injectors import denormalize_tacotron_mel
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from utils.util import ceil_multiple, opt_get
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class AudioDiffusionFid(evaluator.Evaluator):
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"""
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Evaluator produces generate from a diffusion model, then uses a CLIP model to judge the similarity between text & speech.
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This evaluator is kind of a mess. It has been repeatedly modified to work with several different model types, which
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means it is bloated beyond belief. I would not recommend attempting to understand what is going on here.
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"""
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def __init__(self, model, opt_eval, env):
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super().__init__(model, opt_eval, env, uses_all_ddp=True)
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self.real_path = opt_eval['eval_tsv']
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self.data = load_tsv_aligned_codes(self.real_path)
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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self.skip = distributed.get_world_size() # One batch element per GPU.
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else:
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self.skip = 1
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diffusion_steps = opt_get(opt_eval, ['diffusion_steps'], 50)
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diffusion_schedule = opt_get(env['opt'], ['steps', 'generator', 'injectors', 'diffusion', 'beta_schedule', 'schedule_name'], None)
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if diffusion_schedule is None:
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print("Unable to infer diffusion schedule from master options. Getting it from eval (or guessing).")
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diffusion_schedule = opt_get(opt_eval, ['diffusion_schedule'], 'cosine')
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conditioning_free_diffusion_enabled = opt_get(opt_eval, ['conditioning_free'], False)
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conditioning_free_k = opt_get(opt_eval, ['conditioning_free_k'], 1)
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self.diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_steps, schedule=diffusion_schedule,
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enable_conditioning_free_guidance=conditioning_free_diffusion_enabled,
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conditioning_free_k=conditioning_free_k)
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self.dev = self.env['device']
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mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
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self.local_modules = {}
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if mode == 'tts':
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self.diffusion_fn = self.perform_diffusion_tts
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elif mode == 'original_vocoder':
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self.local_modules['dvae'] = load_speech_dvae().cpu()
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self.diffusion_fn = self.perform_original_diffusion_vocoder
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elif mode == 'vocoder':
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self.local_modules['dvae'] = load_speech_dvae().cpu()
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self.diffusion_fn = self.perform_diffusion_vocoder
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elif 'tts9_mel' in mode:
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mel_means, self.mel_max, self.mel_min, mel_stds, mel_vars = torch.load('../experiments/univnet_mel_norms.pth')
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self.bpe_tokenizer = VoiceBpeTokenizer('../experiments/bpe_lowercase_asr_256.json')
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self.local_modules['dvae'] = load_speech_dvae().cpu()
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self.local_modules['vocoder'] = load_univnet_vocoder().cpu()
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self.diffusion_fn = self.perform_diffusion_tts9_mel_from_codes
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if mode == 'tts9_mel_autoin':
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self.local_modules['autoregressive'] = load_model_from_config("../experiments/train_gpt_tts_unified.yml",
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model_name='gpt',
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also_load_savepoint=False,
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load_path='../experiments/unified_large_diverse_basis.pth',
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device=torch.device('cpu')).cuda().eval()
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self.tts9_codegen = self.tts9_get_autoregressive_codes
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else:
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self.tts9_codegen = self.tts9_get_dvae_codes
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def perform_diffusion_tts(self, audio, codes, text, sample_rate=5500):
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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aligned_codes_compression_factor = sample_rate * 221 // 11025
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output_size = codes.shape[-1]*aligned_codes_compression_factor
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padded_size = ceil_multiple(output_size, 2048)
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padding_added = padded_size - output_size
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padding_needed_for_codes = padding_added // aligned_codes_compression_factor
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if padding_needed_for_codes > 0:
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codes = F.pad(codes, (0, padding_needed_for_codes))
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output_shape = (1, 1, padded_size)
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gen = self.diffuser.p_sample_loop(self.model, output_shape,
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model_kwargs={'tokens': codes.unsqueeze(0),
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'conditioning_input': real_resampled})
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return gen, real_resampled, sample_rate
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def perform_original_diffusion_vocoder(self, audio, codes, text, sample_rate=11025):
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mel = wav_to_mel(audio)
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mel_codes = convert_mel_to_codes(self.local_modules['dvae'], mel)
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back_to_mel = self.local_modules['dvae'].decode(mel_codes)[0]
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orig_audio = audio
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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output_size = real_resampled.shape[-1]
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aligned_mel_compression_factor = output_size // back_to_mel.shape[-1]
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padded_size = ceil_multiple(output_size, 2048)
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padding_added = padded_size - output_size
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padding_needed_for_codes = padding_added // aligned_mel_compression_factor
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if padding_needed_for_codes > 0:
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back_to_mel = F.pad(back_to_mel, (0, padding_needed_for_codes))
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output_shape = (1, 1, padded_size)
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gen = self.diffuser.p_sample_loop(self.model, output_shape,
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model_kwargs={'spectrogram': back_to_mel,
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'conditioning_input': orig_audio.unsqueeze(0)})
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# Pop it back down to 5.5kHz for an accurate comparison with the other diffusers.
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real_resampled = torchaudio.functional.resample(real_resampled.squeeze(0), sample_rate, 5500).unsqueeze(0)
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gen = torchaudio.functional.resample(gen.squeeze(0), sample_rate, 5500).unsqueeze(0)
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return gen, real_resampled, 5500
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def perform_diffusion_vocoder(self, audio, codes, text, sample_rate=5500):
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mel = wav_to_mel(audio)
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mel_codes = convert_mel_to_codes(self.local_modules['dvae'], mel)
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text_codes = text_to_sequence(text)
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real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
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output_size = real_resampled.shape[-1]
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aligned_codes_compression_factor = output_size // mel_codes.shape[-1]
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padded_size = ceil_multiple(output_size, 2048)
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padding_added = padded_size - output_size
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padding_needed_for_codes = padding_added // aligned_codes_compression_factor
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if padding_needed_for_codes > 0:
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mel_codes = F.pad(mel_codes, (0, padding_needed_for_codes))
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output_shape = (1, 1, padded_size)
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gen = self.diffuser.p_sample_loop(self.model, output_shape,
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model_kwargs={'tokens': mel_codes,
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'conditioning_input': audio.unsqueeze(0),
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'unaligned_input': torch.tensor(text_codes, device=audio.device).unsqueeze(0)})
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return gen, real_resampled, sample_rate
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def tts9_get_autoregressive_codes(self, mel, text):
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mel_codes = convert_mel_to_codes(self.local_modules['dvae'], mel)
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text_codes = torch.LongTensor(self.bpe_tokenizer.encode(text)).unsqueeze(0).to(mel.device)
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cond_inputs = mel.unsqueeze(1)
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auto_latents = self.local_modules['autoregressive'].forward(cond_inputs, text_codes,
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torch.tensor([text_codes.shape[-1]], device=mel.device),
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mel_codes,
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torch.tensor([mel_codes.shape[-1]], device=mel.device),
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text_first=True, raw_mels=None, return_latent=True,
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clip_inputs=False)
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return auto_latents
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def tts9_get_dvae_codes(self, mel, text):
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return convert_mel_to_codes(self.local_modules['dvae'], mel)
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def perform_diffusion_tts9_mel_from_codes(self, audio, codes, text):
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SAMPLE_RATE = 24000
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mel = wav_to_mel(audio)
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mel_codes = self.tts9_codegen(mel, text)
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real_resampled = torchaudio.functional.resample(audio, 22050, SAMPLE_RATE).unsqueeze(0)
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univnet_mel = wav_to_univnet_mel(real_resampled, do_normalization=False) # to be used for a conditioning input, but also guides output shape.
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output_shape = univnet_mel.shape
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gen_mel = self.diffuser.p_sample_loop(self.model, output_shape,
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model_kwargs={'aligned_conditioning': mel_codes,
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'conditioning_input': univnet_mel})
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# denormalize mel
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gen_mel = denormalize_tacotron_mel(gen_mel)
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gen_wav = self.local_modules['vocoder'].inference(gen_mel)
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real_dec = self.local_modules['vocoder'].inference(univnet_mel)
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return gen_wav.float(), real_dec, SAMPLE_RATE
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def load_projector(self):
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"""
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Builds the CLIP model used to project speech into a latent. This model has fixed parameters and a fixed loading
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path for the time being.
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"""
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model = MelTextCLIP(dim_text=512, dim_latent=512, dim_speech=512, num_text_tokens=148, text_enc_depth=8,
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text_seq_len=400, text_heads=8, speech_enc_depth=10, speech_heads=8, speech_seq_len=1000,
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text_mask_percentage=.15, voice_mask_percentage=.15)
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weights = torch.load('../experiments/clip_text_to_voice_for_speech_fid.pth')
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model.load_state_dict(weights)
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return model
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def project(self, projector, sample, sample_rate):
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sample = torchaudio.functional.resample(sample, sample_rate, 22050)
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mel = wav_to_mel(sample)
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return projector.get_speech_projection(mel).squeeze(0) # Getting rid of the batch dimension means it's just [hidden_dim]
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def load_w2v(self):
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return Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli")
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def intelligibility_loss(self, w2v, sample, real_sample, sample_rate, real_text):
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"""
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Measures the differences between CTC losses using wav2vec2 against the real sample and the generated sample.
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"""
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text_codes = torch.tensor(text_to_sequence(real_text), device=sample.device)
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results = []
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for s in [sample, real_sample]:
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s = torchaudio.functional.resample(s, sample_rate, 16000)
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norm_s = (s - s.mean()) / torch.sqrt(s.var() + 1e-7)
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norm_s = norm_s.squeeze(1)
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loss = w2v(input_values=norm_s, labels=text_codes).loss
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results.append(loss)
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gen_loss, real_loss = results
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return gen_loss - real_loss
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def compute_frechet_distance(self, proj1, proj2):
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# I really REALLY FUCKING HATE that this is going to numpy. Why does "pytorch_fid" operate in numpy land. WHY?
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proj1 = proj1.cpu().numpy()
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proj2 = proj2.cpu().numpy()
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mu1 = np.mean(proj1, axis=0)
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mu2 = np.mean(proj2, axis=0)
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sigma1 = np.cov(proj1, rowvar=False)
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sigma2 = np.cov(proj2, rowvar=False)
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return torch.tensor(calculate_frechet_distance(mu1, sigma1, mu2, sigma2))
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def perform_eval(self):
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save_path = osp.join(self.env['base_path'], "../", "audio_eval", str(self.env["step"]))
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os.makedirs(save_path, exist_ok=True)
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projector = self.load_projector().to(self.env['device'])
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projector.eval()
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w2v = self.load_w2v().to(self.env['device'])
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w2v.eval()
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for k, mod in self.local_modules.items():
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self.local_modules[k] = mod.to(self.env['device'])
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# Attempt to fix the random state as much as possible. RNG state will be restored before returning.
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rng_state = torch.get_rng_state()
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torch.manual_seed(5)
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self.model.eval()
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with torch.no_grad():
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gen_projections = []
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real_projections = []
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intelligibility_losses = []
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for i in tqdm(list(range(0, len(self.data), self.skip))):
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path, text, codes = self.data[i + self.env['rank']]
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audio = load_audio(path, 22050).to(self.dev)
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codes = codes.to(self.dev)
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sample, ref, sample_rate = self.diffusion_fn(audio, codes, text)
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gen_projections.append(self.project(projector, sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory.
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real_projections.append(self.project(projector, ref, sample_rate).cpu())
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intelligibility_losses.append(self.intelligibility_loss(w2v, sample, ref, sample_rate, text))
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torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_gen.wav"), sample.squeeze(0).cpu(), sample_rate)
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torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_real.wav"), ref.squeeze(0).cpu(), sample_rate)
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gen_projections = torch.stack(gen_projections, dim=0)
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real_projections = torch.stack(real_projections, dim=0)
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intelligibility_loss = torch.stack(intelligibility_losses, dim=0).mean()
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frechet_distance = torch.tensor(self.compute_frechet_distance(gen_projections, real_projections), device=self.env['device'])
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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distributed.all_reduce(frechet_distance)
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frechet_distance = frechet_distance / distributed.get_world_size()
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distributed.all_reduce(intelligibility_loss)
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intelligibility_loss = intelligibility_loss / distributed.get_world_size()
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self.model.train()
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torch.set_rng_state(rng_state)
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# Put modules used for evaluation back into CPU memory.
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for k, mod in self.local_modules.items():
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self.local_modules[k] = mod.cpu()
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return {"frechet_distance": frechet_distance, "intelligibility_loss": intelligibility_loss}
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"""
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if __name__ == '__main__':
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from utils.util import load_model_from_config
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diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text.yml', 'generator',
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also_load_savepoint=False, load_path='X:\\dlas\\experiments\\train_diffusion_tts7_dvae_thin_with_text\\models\\56500_generator_ema.pth').cuda()
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opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100,
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'conditioning_free': False, 'conditioning_free_k': 1,
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'diffusion_schedule': 'linear', 'diffusion_type': 'vocoder'}
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env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 2, 'device': 'cuda', 'opt': {}}
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eval = AudioDiffusionFid(diffusion, opt_eval, env)
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print(eval.perform_eval())
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"""
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if __name__ == '__main__':
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from utils.util import load_model_from_config
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# 34k; no conditioning_free: {'frechet_distance': tensor(1.4559, device='cuda:0', dtype=torch.float64), 'intelligibility_loss': tensor(151.9112, device='cuda:0')}
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# 34k; conditioning_free: {'frechet_distance': tensor(1.4059, device='cuda:0', dtype=torch.float64), 'intelligibility_loss': tensor(118.3377, device='cuda:0')}
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diffusion = load_model_from_config('X:\\dlas\\experiments\\train_diffusion_tts_mel_flat_autoregressive_inputs.yml', 'generator',
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also_load_savepoint=False,
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load_path='X:\\dlas\\experiments\\tts_flat_autoregressive_inputs_r2_initial\\models\\500_generator.pth').cuda()
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opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv', 'diffusion_steps': 100,
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'conditioning_free': True, 'conditioning_free_k': 1,
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'diffusion_schedule': 'linear', 'diffusion_type': 'tts9_mel_autoin'}
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env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 561, 'device': 'cuda', 'opt': {}}
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eval = AudioDiffusionFid(diffusion, opt_eval, env)
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print(eval.perform_eval())
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