forked from mrq/DL-Art-School
183 lines
12 KiB
Python
183 lines
12 KiB
Python
import argparse
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import os
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import random
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import torch
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import torch.nn.functional as F
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import torchaudio
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import yaml
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from tqdm import tqdm
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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 data.util import is_audio_file, find_files_of_type
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from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \
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load_discrete_vocoder_diffuser, wav_to_mel
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from utils.options import Loader
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from utils.util import load_model_from_config
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# Loads multiple conditioning files at random from a folder.
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def load_conditioning_candidates(path, num_conds, sample_rate=22050, cond_length=44100):
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candidates = find_files_of_type('img', path, qualifier=is_audio_file)[0]
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# Sample with replacement. This can get repeats, but more conveniently handles situations where there are not enough candidates.
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related_mels = []
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for k in range(num_conds):
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rel_clip = load_audio(candidates[k], sample_rate)
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gap = rel_clip.shape[-1] - cond_length
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if gap < 0:
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rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
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mel_clip = wav_to_mel(rel_clip.unsqueeze(0)).squeeze(0)
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related_mels.append(mel_clip)
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return torch.stack(related_mels, dim=0).unsqueeze(0).cuda(), rel_clip.unsqueeze(0).cuda()
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def load_conditioning(path, sample_rate=22050, cond_length=44100):
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rel_clip = load_audio(path, sample_rate)
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gap = rel_clip.shape[-1] - cond_length
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if gap < 0:
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rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
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mel_clip = wav_to_mel(rel_clip.unsqueeze(0)).squeeze(0)
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return mel_clip.unsqueeze(0).cuda(), rel_clip.unsqueeze(0).cuda()
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def fix_autoregressive_output(codes, stop_token):
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"""
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This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
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trained on and what the autoregressive code generator creates (which has no padding or end).
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This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
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a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
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and copying out the last few codes.
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Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
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"""
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# Strip off the autoregressive stop token and add padding.
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stop_token_indices = (codes == stop_token).nonzero()
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if len(stop_token_indices) == 0:
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print("No stop tokens found, enjoy that output of yours!")
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return
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else:
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codes[stop_token_indices] = 83
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stm = stop_token_indices.min().item()
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codes[stm:] = 83
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if stm - 3 < codes.shape[0]:
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codes[-3] = 45
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codes[-2] = 45
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codes[-1] = 248
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return codes
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if __name__ == '__main__':
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preselected_cond_voices = {
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'trump': ['D:\\data\\audio\\sample_voices\\trump.wav'],
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'obama': ['D:\\data\\audio\\sample_voices\\obama1.mp3', 'D:\\data\\audio\\sample_voices\\obama2.wav'],
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'ryan_reynolds': ['D:\\data\\audio\\sample_voices\\ryan_reynolds.wav'],
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'ed_sheeran': ['D:\\data\\audio\\sample_voices\\ed_sheeran.wav'],
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'simmons': ['Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav'],
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'news_girl': ['Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00022.wav', 'Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00016.wav'],
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'dan_carlin': ['Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav', 'Y:\\clips\\books1\\15_dchha16 Nazi Tidbits\\00036.wav'],
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'libri_test': ['Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav'],
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'myself': ['D:\\data\\audio\\sample_voices\\myself1.wav', 'D:\\data\\audio\\sample_voices\\myself2.wav'],
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}
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt_diffuse', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_vocoder_22k_level.yml')
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parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
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parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_22k_level\\models\\15000_generator_ema.pth')
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parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae')
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parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts_unified.yml')
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parser.add_argument('-gpt_tts_model_name', type=str, help='Name of the GPT TTS model in opt.', default='gpt')
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parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts_unified_large\\models\\45000_gpt_ema.pth')
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parser.add_argument('-opt_clip', type=str, help='Path to options YAML file used to train the CLIP model', default='X:\\dlas\\experiments\\train_clip_text_to_voice.yml')
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parser.add_argument('-clip_model_name', type=str, help='Name of the CLIP model in opt.', default='clip')
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parser.add_argument('-clip_model_path', type=str, help='CLIP model checkpoint to load.', default='X:\\dlas\\experiments\\train_clip_text_to_voice_masking_bigger_batch\\models\\23500_clip_ema.pth')
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parser.add_argument('-opt_cond_clip', type=str, help='Path to options YAML file used to train the Conditioning CLIP model', default='D:\\dlas\\options\\train_clip_cond_to_voice.yml')
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parser.add_argument('-cond_clip_model_name', type=str, help='Name of the CLIP model in opt.', default='clip')
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parser.add_argument('-cond_clip_model_path', type=str, help='CLIP model checkpoint to load.', default='D:\\dlas\\experiments\\train_clip_cond_to_voice\\models\\42000_clip_ema.pth')
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parser.add_argument('-cond_clip_weight', type=float, help='How much to weight the conditioning CLIP to the text CLIP. Lower means the sample sounds more like the text, higher means it sounds more like the conditioning.',
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default=.3)
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parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
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parser.add_argument('-cond_preset', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='simmons')
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parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=256)
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parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=16)
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parser.add_argument('-num_outputs', type=int, help='Number of outputs to produce.', default=5)
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parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_gpt_tts')
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args = parser.parse_args()
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os.makedirs(args.output_path, exist_ok=True)
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# libritts_text = 'fall passed so quickly, there was so much going on around him, the tree quite forgot to look to himself.'
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print("Loading GPT TTS..")
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with open(args.opt_gpt_tts, mode='r') as f:
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gpt_opt = yaml.load(f, Loader=Loader)
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gpt_opt['networks'][args.gpt_tts_model_name]['kwargs']['checkpointing'] = False # Required for beam search
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gpt = load_model_from_config(preloaded_options=gpt_opt, model_name=args.gpt_tts_model_name, also_load_savepoint=False, load_path=args.gpt_tts_model_path).cuda().eval()
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stop_mel_token = gpt.stop_mel_token
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print("Loading data..")
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tokenizer = VoiceBpeTokenizer('../experiments/bpe_lowercase_asr_256.json')
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text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda()
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text = F.pad(text, (0,1)) # This may not be necessary.
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cond_paths = preselected_cond_voices[args.cond_preset]
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conds = []
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for cond_path in cond_paths:
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c, cond_wav = load_conditioning(cond_path, cond_length=132300)
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conds.append(c)
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conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model.
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with torch.no_grad():
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print("Performing GPT inference..")
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samples = []
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ctc_codes = []
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samples_per_batch = args.num_samples//args.num_batches
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for b in tqdm(range(args.num_batches)):
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codes, attentions = gpt.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95,
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temperature=.9, num_return_sequences=samples_per_batch, length_penalty=1,
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return_attentions=True)
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padding_needed = 250 - codes.shape[1]
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codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
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samples.append(codes)
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ctc_codes.extend(gpt.convert_attentions_to_aligned_codes(text, attentions, codes, conds.shape[1]))
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samples = torch.cat(samples, dim=0)
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print("Loading CLIP..")
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clip = load_model_from_config(args.opt_clip, model_name=args.clip_model_name, also_load_savepoint=False, load_path=args.clip_model_path).cuda().eval()
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cond_clip = load_model_from_config(args.opt_cond_clip, model_name=args.cond_clip_model_name, also_load_savepoint=False, load_path=args.cond_clip_model_path).cuda().eval()
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print("Performing CLIP filtering..")
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for i in range(samples.shape[0]):
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samples[i] = fix_autoregressive_output(samples[i], stop_mel_token)
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clip_results = clip(text.repeat(samples.shape[0], 1),
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torch.full((samples.shape[0],), fill_value=text.shape[1]-1, dtype=torch.long, device='cuda'),
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samples, torch.full((samples.shape[0],), fill_value=samples.shape[1]*1024, dtype=torch.long, device='cuda'),
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return_loss=False)
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cond_clip_results = cond_clip(conds[:, -1], samples, torch.full((samples.shape[0],), fill_value=samples.shape[1]*1024,
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dtype=torch.long, device='cuda'), return_loss=False)
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clip_results = clip_results * (1-args.cond_clip_weight) + cond_clip_results * args.cond_clip_weight
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best_indices = torch.topk(clip_results, k=args.num_outputs).indices
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best_results = samples[best_indices]
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best_codes = [ctc_codes[i] for i in best_indices]
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# Delete the GPT TTS and associated models to free up GPU memory before diffusion.
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del samples, clip, gpt
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print("Loading DVAE..")
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dvae = load_model_from_config(args.opt_diffuse, args.dvae_model_name).cuda()
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print("Loading Diffusion Model..")
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diffusion = load_model_from_config(args.opt_diffuse, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path).cuda()
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100)
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print("Performing vocoding..")
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# Perform vocoding on each batch element separately: Vocoding is very memory intensive.
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for b in range(best_results.shape[0]):
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code = best_results[b].unsqueeze(0)
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wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav,
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spectrogram_compression_factor=256, plt_spec=False)
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torchaudio.save(os.path.join(args.output_path, f'gpt_tts_output_{b}.wav'), wav.squeeze(0).cpu(), 22050)
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