forked from mrq/tortoise-tts
168 lines
9.4 KiB
Python
168 lines
9.4 KiB
Python
import argparse
|
|
import os
|
|
import random
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torchaudio
|
|
from models.dvae import DiscreteVAE
|
|
from models.autoregressive import UnifiedVoice
|
|
from tqdm import tqdm
|
|
|
|
from models.arch_util import TorchMelSpectrogram
|
|
from models.discrete_diffusion_vocoder import DiscreteDiffusionVocoder
|
|
from models.text_voice_clip import VoiceCLIP
|
|
from utils.audio import load_audio
|
|
from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
|
|
from utils.tokenizer import VoiceBpeTokenizer
|
|
|
|
|
|
def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200):
|
|
"""
|
|
Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
|
|
"""
|
|
return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
|
|
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):
|
|
"""
|
|
Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip.
|
|
"""
|
|
with torch.no_grad():
|
|
mel = dvae_model.decode(mel_codes)[0]
|
|
|
|
# Pad MEL to multiples of 2048//spectrogram_compression_factor
|
|
msl = mel.shape[-1]
|
|
dsl = 2048 // spectrogram_compression_factor
|
|
gap = dsl - (msl % dsl)
|
|
if gap > 0:
|
|
mel = torch.nn.functional.pad(mel, (0, gap))
|
|
|
|
output_shape = (mel.shape[0], 1, mel.shape[-1] * spectrogram_compression_factor)
|
|
return diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input})
|
|
|
|
|
|
def load_conditioning(path, sample_rate=22050, cond_length=44100):
|
|
rel_clip = load_audio(path, sample_rate)
|
|
gap = rel_clip.shape[-1] - cond_length
|
|
if gap < 0:
|
|
rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
|
|
elif gap > 0:
|
|
rand_start = random.randint(0, gap)
|
|
rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
|
|
mel_clip = TorchMelSpectrogram()(rel_clip.unsqueeze(0)).squeeze(0)
|
|
return mel_clip.unsqueeze(0).cuda(), rel_clip.unsqueeze(0).cuda()
|
|
|
|
|
|
def fix_autoregressive_output(codes, stop_token):
|
|
"""
|
|
This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
|
|
trained on and what the autoregressive code generator creates (which has no padding or end).
|
|
This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
|
|
a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
|
|
and copying out the last few codes.
|
|
|
|
Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
|
|
"""
|
|
# Strip off the autoregressive stop token and add padding.
|
|
stop_token_indices = (codes == stop_token).nonzero()
|
|
if len(stop_token_indices) == 0:
|
|
print("No stop tokens found, enjoy that output of yours!")
|
|
return
|
|
else:
|
|
codes[stop_token_indices] = 83
|
|
stm = stop_token_indices.min().item()
|
|
codes[stm:] = 83
|
|
if stm - 3 < codes.shape[0]:
|
|
codes[-3] = 45
|
|
codes[-2] = 45
|
|
codes[-1] = 248
|
|
|
|
return codes
|
|
|
|
|
|
if __name__ == '__main__':
|
|
preselected_cond_voices = {
|
|
'simmons': ['Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav'],
|
|
'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'],
|
|
'dan_carlin': ['Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav', 'Y:\\clips\\books1\\15_dchha16 Nazi Tidbits\\00036.wav'],
|
|
'libri_test': ['Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav'],
|
|
}
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-autoregressive_model_path', type=str, help='Autoregressive model checkpoint to load.', default='.models/unified_voice.pth')
|
|
parser.add_argument('-clip_model_path', type=str, help='CLIP model checkpoint to load.', default='.models/clip.pth')
|
|
parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='./models/diffusion_vocoder.pth')
|
|
parser.add_argument('-dvae_model_path', type=str, help='DVAE model checkpoint to load.', default='./models/dvae.pth')
|
|
parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
|
|
parser.add_argument('-cond_preset', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dan_carlin')
|
|
parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=32)
|
|
parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=2)
|
|
parser.add_argument('-num_outputs', type=int, help='Number of outputs to produce.', default=2)
|
|
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/')
|
|
args = parser.parse_args()
|
|
os.makedirs(args.output_path, exist_ok=True)
|
|
|
|
print("Loading GPT TTS..")
|
|
autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024, heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False).eval()
|
|
autoregressive.load_state_dict(torch.load(args.autoregressive_model_path))
|
|
stop_mel_token = autoregressive.stop_mel_token
|
|
|
|
print("Loading data..")
|
|
tokenizer = VoiceBpeTokenizer()
|
|
text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda()
|
|
text = F.pad(text, (0,1)) # This may not be necessary.
|
|
cond_paths = preselected_cond_voices[args.cond_preset]
|
|
conds = []
|
|
for cond_path in cond_paths:
|
|
c, cond_wav = load_conditioning(cond_path, cond_length=132300)
|
|
conds.append(c)
|
|
conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model.
|
|
|
|
with torch.no_grad():
|
|
print("Performing GPT inference..")
|
|
samples = []
|
|
for b in tqdm(range(args.num_batches)):
|
|
codes = autoregressive.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95,
|
|
temperature=.9, num_return_sequences=args.num_samples//args.num_batches, length_penalty=1)
|
|
padding_needed = 250 - codes.shape[1]
|
|
codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
|
|
samples.append(codes)
|
|
samples = torch.cat(samples, dim=0)
|
|
del autoregressive
|
|
|
|
print("Loading CLIP..")
|
|
clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=8, text_seq_len=120, text_heads=8,
|
|
num_speech_tokens=8192, speech_enc_depth=10, speech_heads=8, speech_seq_len=250).eval()
|
|
clip.load_state_dict(torch.load(args.clip_model_path))
|
|
print("Performing CLIP filtering..")
|
|
for i in range(samples.shape[0]):
|
|
samples[i] = fix_autoregressive_output(samples[i], stop_mel_token)
|
|
clip_results = clip(text.repeat(samples.shape[0], 1),
|
|
torch.full((samples.shape[0],), fill_value=text.shape[1]-1, dtype=torch.long, device='cuda'),
|
|
samples, torch.full((samples.shape[0],), fill_value=samples.shape[1]*1024, dtype=torch.long, device='cuda'),
|
|
return_loss=False)
|
|
best_results = samples[torch.topk(clip_results, k=args.num_outputs).indices]
|
|
|
|
# Delete the autoregressive and clip models to free up GPU memory
|
|
del samples, clip
|
|
|
|
print("Loading DVAE..")
|
|
dvae = DiscreteVAE(positional_dims=1, channels=80, hidden_dim=512, num_resnet_blocks=3, codebook_dim=512, num_tokens=8192, num_layers=2,
|
|
record_codes=True, kernel_size=3, use_transposed_convs=False).eval()
|
|
dvae.load_state_dict(torch.load(args.dvae_model_path))
|
|
print("Loading Diffusion Model..")
|
|
diffusion = DiscreteDiffusionVocoder(model_channels=128, dvae_dim=80, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1],
|
|
spectrogram_conditioning_resolutions=[2,512], attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2,
|
|
conditioning_inputs_provided=True, time_embed_dim_multiplier=4).eval()
|
|
diffusion.load_state_dict(torch.load(args.diffusion_model_path))
|
|
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100)
|
|
|
|
print("Performing vocoding..")
|
|
# Perform vocoding on each batch element separately: Vocoding is very memory (and compute!) intensive.
|
|
for b in range(best_results.shape[0]):
|
|
code = best_results[b].unsqueeze(0)
|
|
wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav, spectrogram_compression_factor=256)
|
|
torchaudio.save(os.path.join(args.output_path, f'gpt_tts_output_{b}.wav'), wav.squeeze(0).cpu(), 22050)
|