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use script updates to fix output size disparities

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This commit is contained in:
James Betker 2022-02-12 20:00:46 -07:00
parent 15fd60aad3
commit 0c3cc5ebad
2 changed files with 26 additions and 13 deletions
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30
codes/scripts/audio/gen/use_diffuse_tts.py
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@ -9,6 +9,7 @@ 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
import torch.nn.functional as F
def ceil_multiple(base, multiple):
@ -18,6 +19,18 @@ def ceil_multiple(base, multiple):
return base + (multiple - res)
def determine_output_size(codes, base_sample_rate):
aligned_codes_compression_factor = base_sample_rate * 221 // 11025
output_size = codes.shape[-1]*aligned_codes_compression_factor
padded_size = ceil_multiple(output_size, 2048)
padding_added = padded_size - output_size
padding_needed_for_codes = padding_added // aligned_codes_compression_factor
if padding_needed_for_codes > 0:
codes = F.pad(codes, (0, padding_needed_for_codes))
output_shape = (1, 1, padded_size)
return output_shape, codes
if __name__ == '__main__':
conditioning_clips = {
# Male
@ -25,6 +38,7 @@ if __name__ == '__main__':
'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav',
'entangled': 'Y:\\clips\\books1\\3857_25_The_Entangled_Bank__000000000\\00123.wav',
'snowden': 'Y:\\clips\\books1\\7658_Edward_Snowden_-_Permanent_Record__000000004\\00027.wav',
'plants': 'Y:\\clips\\books1\\12028_The_Secret_Life_of_Plants_-_18__000000000\\00399.wav',
# Female
'the_doctor': 'Y:\\clips\\books2\\37062___The_Doctor__000000003\\00206.wav',
'puppy': 'Y:\\clips\\books2\\17830___3_Puppy_Kisses__000000002\\00046.wav',
@ -112,17 +126,17 @@ if __name__ == '__main__':
]
parser = argparse.ArgumentParser()
parser.add_argument('-text', type=str, help='Text to speak.', default='instead of molten iron, jupiter and brown dwarfs have hydrogen, which is under so much pressure that it develops metallic properties.')
parser.add_argument('-opt_code_gen', type=str, help='Path to options YAML file used to train the code_gen model', default='D:\\dlas\\experiments\\train_encoder_build_ctc_alignments_medium\\train_encoder_build_ctc_alignments.yml')
parser.add_argument('-text', type=str, help='Text to speak.', default='my father worked at the airport. he was air traffic control. he always knew when the president was flying in but was not allowed to tell anyone.')
parser.add_argument('-opt_code_gen', type=str, help='Path to options YAML file used to train the code_gen model', default='D:\\dlas\\options\\train_encoder_build_ctc_alignments.yml')
parser.add_argument('-code_gen_model_name', type=str, help='Name of the code_gen model in opt.', default='generator')
parser.add_argument('-code_gen_model_path', type=str, help='Path to saved code_gen model weights', default='D:\\dlas\\experiments\\train_encoder_build_ctc_alignments_medium\\models\\32000_generator_ema.pth')
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.yml')
parser.add_argument('-code_gen_model_path', type=str, help='Path to saved code_gen model weights', default='D:\\dlas\\experiments\\train_encoder_build_ctc_alignments_medium\\models\\50000_generator_ema.pth')
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\train_diffusion_tts5_medium.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='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\73000_generator_ema.pth')
parser.add_argument('-sr_opt', type=str, help='Path to options YAML file used to train the SR diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample.yml')
parser.add_argument('-sr_diffusion_model_name', type=str, help='Name of the SR diffusion model in opt.', default='generator')
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample_continued\\models\\45000_generator_ema.pth')
parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='the_doctor')
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample_continued\\models\\53500_generator_ema.pth')
parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='plants')
parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_tts')
parser.add_argument('-device', type=str, help='Device to run on', default='cuda')
@ -135,8 +149,6 @@ if __name__ == '__main__':
print("Loading provided conditional audio..")
sr_cond = load_audio(conditioning_clips[args.cond], sr_sample_rate).to(args.device)
if sr_cond.shape[-1] > 88000:
sr_cond = sr_cond[:,:88000]
cond_mel = wav_to_mel(sr_cond)
cond = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate)
torchaudio.save(os.path.join(args.output_path, 'cond_base.wav'), cond.cpu(), base_sample_rate)
@ -163,7 +175,7 @@ if __name__ == '__main__':
aligned_codes = code.to(args.device)
print("Performing initial diffusion..")
output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))
output_shape, aligned_codes = determine_output_size(aligned_codes, base_sample_rate)
diffusion = diffusion.cuda()
output_base = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
model_kwargs={'tokens': aligned_codes,

9
codes/scripts/audio/gen/use_diffuse_voice_translation.py
View File

@ -28,7 +28,7 @@ def get_ctc_codes_for(src_clip_path):
return torch.argmax(logits, dim=-1), clip
def determine_output_size(codes, base_sample_rate)
def determine_output_size(codes, base_sample_rate):
aligned_codes_compression_factor = base_sample_rate * 221 // 11025
output_size = codes.shape[-1]*aligned_codes_compression_factor
padded_size = ceil_multiple(output_size, 2048)
@ -47,6 +47,7 @@ if __name__ == '__main__':
'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav',
'entangled': 'Y:\\clips\\books1\\3857_25_The_Entangled_Bank__000000000\\00123.wav',
'snowden': 'Y:\\clips\\books1\\7658_Edward_Snowden_-_Permanent_Record__000000004\\00027.wav',
'plants': 'Y:\\clips\\books1\\12028_The_Secret_Life_of_Plants_-_18__000000000\\00399.wav',
# Female
'the_doctor': 'Y:\\clips\\books2\\37062___The_Doctor__000000003\\00206.wav',
'puppy': 'Y:\\clips\\books2\\17830___3_Puppy_Kisses__000000002\\00046.wav',
@ -55,13 +56,13 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-src_clip', type=str, help='Path to the audio files to translate', default='D:\\tortoise-tts\\voices')
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.yml')
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\train_diffusion_tts5_medium.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='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\73000_generator_ema.pth')
parser.add_argument('-sr_opt', type=str, help='Path to options YAML file used to train the SR diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample.yml')
parser.add_argument('-sr_diffusion_model_name', type=str, help='Name of the SR diffusion model in opt.', default='generator')
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample_continued\\models\\45000_generator_ema.pth')
parser.add_argument('-voice', type=str, help='Type of conditioning voice', default='simmons')
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample_continued\\models\\53500_generator_ema.pth')
parser.add_argument('-voice', type=str, help='Type of conditioning voice', default='plants')
parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_voice_translation')
parser.add_argument('-device', type=str, help='Device to run on', default='cuda')