From 62c8ed9a2909682d540c0bd37e98ec1427498164 Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 16 Dec 2021 20:47:37 -0700 Subject: [PATCH] move speech utils --- codes/models/gpt_voice/gpt_tts_hf.py | 2 +- codes/scripts/audio/gen/__init__.py | 0 .../audio/{ => gen}/speech_synthesis_utils.py | 5 +---- .../scripts/audio/{ => gen}/use_discrete_vocoder.py | 13 ++++++++----- .../injectors/gaussian_diffusion_injector.py | 4 ++-- 5 files changed, 12 insertions(+), 12 deletions(-) create mode 100644 codes/scripts/audio/gen/__init__.py rename codes/scripts/audio/{ => gen}/speech_synthesis_utils.py (96%) rename codes/scripts/audio/{ => gen}/use_discrete_vocoder.py (84%) diff --git a/codes/models/gpt_voice/gpt_tts_hf.py b/codes/models/gpt_voice/gpt_tts_hf.py index 57818788..1d38fed4 100644 --- a/codes/models/gpt_voice/gpt_tts_hf.py +++ b/codes/models/gpt_voice/gpt_tts_hf.py @@ -147,7 +147,7 @@ def register_gpt_tts_hf(opt_net, opt): if __name__ == '__main__': - gpt = GptTtsHf() + gpt = GptTtsHf(model_dim=1024, heads=16) l = gpt(torch.randint(high=len(symbols), size=(2,100)), torch.randn(2,2,80,800), torch.randint(high=8192, size=(2,200)), diff --git a/codes/scripts/audio/gen/__init__.py b/codes/scripts/audio/gen/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/codes/scripts/audio/speech_synthesis_utils.py b/codes/scripts/audio/gen/speech_synthesis_utils.py similarity index 96% rename from codes/scripts/audio/speech_synthesis_utils.py rename to codes/scripts/audio/gen/speech_synthesis_utils.py index af30b307..de72bf5a 100644 --- a/codes/scripts/audio/speech_synthesis_utils.py +++ b/codes/scripts/audio/gen/speech_synthesis_utils.py @@ -54,7 +54,7 @@ def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusi model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps)) -def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, plt_spec=False, am=None): +def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, plt_spec=False): """ Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip. """ @@ -62,11 +62,8 @@ def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, c dvae_model.eval() with torch.no_grad(): mel = dvae_model.decode(mel_codes)[0] - if plt_spec: plot_spectrogram(mel[0].cpu()) - m=mel[:,:,:am.shape[-1]] - print(torch.nn.MSELoss()(am,m)) # Pad MEL to multiples of 4096//spectrogram_compression_factor msl = mel.shape[-1] diff --git a/codes/scripts/audio/use_discrete_vocoder.py b/codes/scripts/audio/gen/use_discrete_vocoder.py similarity index 84% rename from codes/scripts/audio/use_discrete_vocoder.py rename to codes/scripts/audio/gen/use_discrete_vocoder.py index 6e3c46fb..c7e819ef 100644 --- a/codes/scripts/audio/use_discrete_vocoder.py +++ b/codes/scripts/audio/gen/use_discrete_vocoder.py @@ -3,7 +3,7 @@ import argparse import torchaudio from data.audio.unsupervised_audio_dataset import load_audio -from scripts.audio.speech_synthesis_utils import do_spectrogram_diffusion, \ +from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \ load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes from utils.audio import plot_spectrogram from utils.util import load_model_from_config @@ -19,17 +19,17 @@ def roundtrip_vocoding(dvae, vocoder, diffuser, clip, cond=None, plot_spec=False if plot_spec: plot_spectrogram(mel[0].cpu()) codes = convert_mel_to_codes(dvae, mel) - return do_spectrogram_diffusion(vocoder, dvae, diffuser, codes, cond, spectrogram_compression_factor=128, plt_spec=plot_spec, am=mel) + return do_spectrogram_diffusion(vocoder, dvae, diffuser, codes, cond, spectrogram_compression_factor=128, plt_spec=plot_spec) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae.yml') parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator') - parser.add_argument('-diffusion_model_path', type=str, help='Name of the diffusion model in opt.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\200_generator_ema.pth') + parser.add_argument('-diffusion_model_path', type=str, help='Name of the diffusion model in opt.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth') parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae') parser.add_argument('-input_file', type=str, help='Path to the input audio file.', default='Z:\\clips\\books1\\3_dchha04 Romancing The Tribes\\00036.wav') - parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.', default=None) + parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.', default='Z:\\clips\\books1\\3042_18_Holden__000000000\\00037.wav') args = parser.parse_args() print("Loading DVAE..") @@ -40,7 +40,10 @@ if __name__ == '__main__': print("Loading data..") diffuser = load_discrete_vocoder_diffuser() inp = load_audio(args.input_file, 22050).cuda() - cond = None if args.cond is None else load_audio(args.cond, 22050).cuda() + cond = inp if args.cond is None else load_audio(args.cond, 22050) + if cond.shape[-1] > 44100+10000: + cond = cond[:,10000:54100] + cond = torchaudio.transforms.Resample(22050, 10025)(cond.cpu()).cuda() print("Performing inference..") roundtripped = roundtrip_vocoding(dvae, diffusion, diffuser, inp, cond).cpu() diff --git a/codes/trainer/injectors/gaussian_diffusion_injector.py b/codes/trainer/injectors/gaussian_diffusion_injector.py index 28f2e2df..8119ef82 100644 --- a/codes/trainer/injectors/gaussian_diffusion_injector.py +++ b/codes/trainer/injectors/gaussian_diffusion_injector.py @@ -61,12 +61,12 @@ class GaussianDiffusionInjector(Injector): def closest_multiple(inp, multiple): - div = inp / multiple + div = inp // multiple mod = inp % multiple if mod == 0: return inp else: - return (div+1)*multiple + return int((div+1)*multiple) # Performs inference using a network trained to predict a reverse diffusion process, which nets a image.