gptmusic work
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@ -319,7 +319,7 @@ class Downsample(nn.Module):
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downsampling occurs in the inner-two dimensions.
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downsampling occurs in the inner-two dimensions.
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"""
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None):
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def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=2):
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super().__init__()
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super().__init__()
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self.channels = channels
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self.channels = channels
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self.out_channels = out_channels or channels
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self.out_channels = out_channels or channels
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@ -327,16 +327,7 @@ class Downsample(nn.Module):
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self.dims = dims
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self.dims = dims
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ksize = 3
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ksize = 3
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pad = 1
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pad = 1
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if dims == 1:
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stride = factor
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stride = 4
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ksize = 5
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pad = 2
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elif dims == 2:
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stride = 2
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else:
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stride = (1,2,2)
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if factor is not None:
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stride = factor
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if use_conv:
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if use_conv:
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self.op = conv_nd(
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self.op = conv_nd(
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dims, self.channels, self.out_channels, ksize, stride=stride, padding=pad
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dims, self.channels, self.out_channels, ksize, stride=stride, padding=pad
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@ -6,9 +6,11 @@ from transformers import GPT2Config, GPT2Model
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from models.arch_util import AttentionBlock, ResBlock
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from models.arch_util import AttentionBlock, ResBlock
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from models.audio.music.music_quantizer import MusicQuantizer
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from models.audio.music.music_quantizer import MusicQuantizer
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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from models.lucidrains.x_transformers import Encoder
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from models.lucidrains.x_transformers import Encoder
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from models.vqvae.vqvae import Quantize
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from trainer.networks import register_model
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from trainer.networks import register_model
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from utils.util import opt_get, checkpoint
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from utils.util import opt_get, checkpoint, ceil_multiple, print_network
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class ConditioningEncoder(nn.Module):
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class ConditioningEncoder(nn.Module):
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@ -57,66 +59,106 @@ class UpperConditioningEncoder(nn.Module):
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return h.mean(dim=2)
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return h.mean(dim=2)
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class GptMusicLower(nn.Module):
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class UpperQuantizer(nn.Module):
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def __init__(self, dim, layers, dropout=0, num_target_vectors=512, num_target_groups=2, num_upper_vectors=64,
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def __init__(self,
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num_upper_groups=4, fp16=True, freeze_upper_until=0):
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spec_dim,
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embedding_dim,
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num_tokens):
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super().__init__()
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super().__init__()
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attn = []
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def edim(m):
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dd = max(embedding_dim//m, 128, spec_dim)
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return ceil_multiple(dd, 8)
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self.encoder = nn.Sequential(
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ResBlock(spec_dim, out_channels=edim(6), use_conv=True, dims=1, down=True),
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ResBlock(edim(6), out_channels=edim(5), use_conv=True, dims=1, down=True),
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ResBlock(edim(5), out_channels=edim(4), use_conv=True, dims=1, down=True),
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ResBlock(edim(4), out_channels=edim(3), use_conv=True, dims=1, down=True),
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ResBlock(edim(3), out_channels=edim(3), use_conv=True, dims=1),
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ResBlock(edim(3), out_channels=edim(2), use_conv=True, dims=1, down=True),
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ResBlock(edim(2), out_channels=edim(2), use_conv=True, dims=1),
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ResBlock(edim(2), out_channels=embedding_dim, use_conv=True, dims=1, down=True),
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ResBlock(embedding_dim, out_channels=embedding_dim, use_conv=True, dims=1),
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ResBlock(embedding_dim, out_channels=embedding_dim, use_conv=True, dims=1),
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ResBlock(embedding_dim, out_channels=embedding_dim, use_conv=True, dims=1),
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nn.GroupNorm(8, embedding_dim)
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)
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self.quantizer = Quantize(embedding_dim, num_tokens)
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self.codes = torch.zeros((num_tokens*100,), dtype=torch.long)
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self.code_ind = 0
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self.total_codes = 0
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self.internal_step = 0
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self.internal_step = 0
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def forward(self, x):
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h = x
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for lyr in self.encoder:
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h = lyr(h)
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h = h.permute(0,2,1)
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h_quant, commitment_loss, codes = self.quantizer(h)
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self.log_codes(codes)
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return h_quant, commitment_loss
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def log_codes(self, codes):
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# This is so we can debug the distribution of codes being learned.
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if self.internal_step % 10 == 0:
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codes = codes.flatten()
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l = codes.shape[0]
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i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
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self.codes[i:i+l] = codes.cpu()
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self.code_ind = self.code_ind + l
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if self.code_ind >= self.codes.shape[0]:
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self.code_ind = 0
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self.total_codes += 1
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self.internal_step += 1
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class GptMusicLower(nn.Module):
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def __init__(self, dim, layers, dropout=0, num_target_vectors=8192, num_upper_vectors=32768,
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fp16=True, freeze_upper_until=0, num_vaes=4, vqargs={}):
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super().__init__()
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self.num_vaes = num_vaes
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self.freeze_upper_until = freeze_upper_until
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self.freeze_upper_until = freeze_upper_until
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self.num_groups = num_target_groups
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self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
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self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
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n_inner=dim*2, attn_pdrop=dropout, resid_pdrop=dropout, gradient_checkpointing=True, use_cache=False)
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n_inner=dim*2, attn_pdrop=dropout, resid_pdrop=dropout, gradient_checkpointing=True, use_cache=False)
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self.target_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
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self.target_quantizers = nn.ModuleList([DiscreteVAE(**vqargs).eval() for _ in range(num_vaes)])
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codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
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self.upper_quantizer = UpperQuantizer(256, dim, num_upper_vectors)
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self.upper_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[dim,
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max(512,dim-128),
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max(512,dim-256),
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max(512,dim-384),
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max(512,dim-512),
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max(512,dim-512)], codevector_dim=dim,
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codebook_size=num_upper_vectors, codebook_groups=num_upper_groups, expressive_downsamples=True)
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self.fp16 = fp16
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self.fp16 = fp16
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# Following are unused quantizer constructs we delete to avoid DDP errors (and to be efficient.. of course..)
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self.internal_step = 0
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del self.target_quantizer.decoder
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del self.target_quantizer.up
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del self.upper_quantizer.up
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# Freeze the target quantizer.
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# Freeze the target quantizer.
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for p in self.target_quantizer.parameters():
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for p in self.target_quantizers.parameters():
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p.DO_NOT_TRAIN = True
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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p.requires_grad = False
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self.upper_mixer = Encoder(
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dim=dim,
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depth=4,
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heads=dim//64,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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self.conditioning_encoder = ConditioningEncoder(256, dim, attn_blocks=4, num_attn_heads=dim//64)
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self.conditioning_encoder = ConditioningEncoder(256, dim, attn_blocks=4, num_attn_heads=dim//64)
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self.gpt = GPT2Model(self.config)
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self.gpt = GPT2Model(self.config)
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del self.gpt.wte # Unused, we'll do our own embeddings.
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del self.gpt.wte # Unused, we'll do our own embeddings.
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self.embeddings = nn.ModuleList([nn.Embedding(num_target_vectors, dim // num_target_groups) for _ in range(num_target_groups)])
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self.embeddings = nn.ModuleList([nn.Embedding(num_target_vectors, dim // num_vaes) for _ in range(num_vaes)])
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self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_target_groups)])
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self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_vaes)])
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def forward(self, mel, conditioning, return_latent=False):
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def forward(self, mel, conditioning, return_latent=False):
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unused_params = []
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unused_params = []
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with torch.no_grad():
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with torch.no_grad():
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self.target_quantizer.eval()
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codes = []
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codes = self.target_quantizer.get_codes(mel)
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partition_size = mel.shape[1] // len(self.target_quantizers)
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for i, q in enumerate(self.target_quantizers):
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mel_partition = mel[:, i*partition_size:(i+1)*partition_size]
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codes.append(q.get_codebook_indices(mel_partition))
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codes = torch.stack(codes, dim=-1)
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if self.freeze_upper_until > self.internal_step:
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if self.freeze_upper_until > self.internal_step:
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with torch.no_grad():
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with torch.no_grad():
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upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
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self.upper_quantizer = self.upper_quantizer.eval()
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upper_vector, upper_diversity = self.upper_quantizer(mel)
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unused_params.extend(list(self.upper_quantizer.parameters()))
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unused_params.extend(list(self.upper_quantizer.parameters()))
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else:
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else:
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self.upper_quantizer = self.upper_quantizer.train()
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upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
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upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
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upper_vector = self.upper_mixer(upper_vector.permute(0,2,1)).permute(0,2,1) # Allow the upper vector to fully attend to itself (the whole thing is a prior.)
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upper_vector = F.interpolate(upper_vector.permute(0,2,1), size=codes.shape[1], mode='linear')
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upper_vector = F.interpolate(upper_vector, size=codes.shape[1], mode='linear')
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upper_vector = upper_vector.permute(0,2,1)
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upper_vector = upper_vector.permute(0,2,1)
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inputs = codes[:, :-1]
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inputs = codes[:, :-1]
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@ -148,33 +190,24 @@ class GptMusicLower(nn.Module):
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unused_adder = unused_adder + p.mean() * 0
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unused_adder = unused_adder + p.mean() * 0
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losses = losses + unused_adder
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losses = losses + unused_adder
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return losses / self.num_groups, upper_diversity
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return losses / self.num_vaes, upper_diversity
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def get_grad_norm_parameter_groups(self):
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def get_grad_norm_parameter_groups(self):
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groups = {
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groups = {
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'gpt': list(self.gpt.parameters()),
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'gpt': list(self.gpt.parameters()),
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'conditioning': list(self.conditioning_encoder.parameters()),
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'conditioning': list(self.conditioning_encoder.parameters()),
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'upper_mixer': list(self.upper_mixer.parameters()),
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'upper_quantizer': list(self.upper_quantizer.parameters()),
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'upper_quant_down': list(self.upper_quantizer.down.parameters()),
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'target_vqs': list(self.target_quantizers.parameters()),
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'upper_quant_encoder': list(self.upper_quantizer.encoder.parameters()),
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'upper_quant_codebook': [self.upper_quantizer.quantizer.codevectors],
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}
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}
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return groups
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return groups
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def get_debug_values(self, step, __):
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def get_debug_values(self, step, __):
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self.internal_step = 0
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if self.upper_quantizer.total_codes > 0:
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if self.upper_quantizer.total_codes > 0:
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return {'histogram_upper_codes': self.upper_quantizer.codes[:self.upper_quantizer.total_codes],
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return {'histogram_upper_codes': self.upper_quantizer.codes[:self.upper_quantizer.total_codes]}
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'gumbel_temperature': self.upper_quantizer.quantizer.temperature}
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else:
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else:
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return {}
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return {}
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def update_for_step(self, step, *args):
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self.internal_step = step
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self.upper_quantizer.quantizer.temperature = max(
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self.upper_quantizer.max_gumbel_temperature * self.upper_quantizer.gumbel_temperature_decay**self.internal_step,
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self.upper_quantizer.min_gumbel_temperature,
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)
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class GptMusicUpper(nn.Module):
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class GptMusicUpper(nn.Module):
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def __init__(self, dim, layers, dropout=0, num_upper_vectors=64, num_upper_groups=4, fp16=True):
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def __init__(self, dim, layers, dropout=0, num_upper_vectors=64, num_upper_groups=4, fp16=True):
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@ -263,18 +296,38 @@ def register_music_gpt_upper(opt_net, opt):
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def test_lower():
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def test_lower():
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from models.audio.music.transformer_diffusion8 import TransformerDiffusionWithQuantizer
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model = GptMusicLower(dim=512, layers=12, fp16=False, freeze_upper_until=1000,
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base_diff = TransformerDiffusionWithQuantizer(in_channels=256, out_channels=512, model_channels=2048, block_channels=1024,
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num_target_vectors=8192, num_upper_vectors=8192, num_vaes=4,
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prenet_channels=1024, prenet_layers=6, num_layers=16, input_vec_dim=1024,
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vqargs= {
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dropout=.1, unconditioned_percentage=0, freeze_quantizer_until=6000)
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'positional_dims': 1, 'channels': 64,
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base_diff.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd8/models/47500_generator.pth', map_location=torch.device('cpu')))
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'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
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'num_layers': 0, 'record_codes': True, 'kernel_size': 3, 'use_transposed_convs': False,
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})
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quants = ['X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_low\\models\\7500_generator.pth',
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'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_low\\models\\11000_generator.pth',
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'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_high\\models\\11500_generator.pth',
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'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_high\\models\\11500_generator.pth']
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for i, qfile in enumerate(quants):
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quant_weights = torch.load(qfile)
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model.target_quantizers[i].load_state_dict(quant_weights, strict=True)
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torch.save(model.state_dict(), 'sample.pth')
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print_network(model)
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model = GptMusicLower(512, 8, fp16=False, freeze_upper_until=100)
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model.target_quantizer.load_state_dict(base_diff.quantizer.state_dict(), strict=False)
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torch.save(model.state_dict(), "sample.pth")
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mel = torch.randn(2,256,400)
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mel = torch.randn(2,256,400)
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model(mel, mel)
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model(mel, mel)
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model.get_grad_norm_parameter_groups()
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pg = model.get_grad_norm_parameter_groups()
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t = 0
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for k, vs in pg.items():
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s = 0
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for v in vs:
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m = 1
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for d in v.shape:
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m *= d
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s += m
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t += s
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print(k, s/1000000)
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print(t/1000000)
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def test_upper():
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def test_upper():
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176
codes/models/audio/music/gpt_music2.py
Normal file
176
codes/models/audio/music/gpt_music2.py
Normal file
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@ -0,0 +1,176 @@
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import GPT2Config, GPT2Model
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from models.arch_util import AttentionBlock, ResBlock
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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from trainer.networks import register_model
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from utils.util import opt_get, ceil_multiple, print_network
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class UpperEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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hidden_dim,
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embedding_dim,
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):
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super().__init__()
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attn = []
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def edim(m):
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dd = max(hidden_dim // m, 128, spec_dim)
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return ceil_multiple(dd, 8)
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self.downsampler = nn.Sequential(
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ResBlock(spec_dim, out_channels=edim(6), use_conv=True, dims=1, down=True),
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|
ResBlock(edim(6), out_channels=edim(5), use_conv=True, dims=1, down=True),
|
||||||
|
ResBlock(edim(5), out_channels=edim(4), use_conv=True, dims=1, down=True),
|
||||||
|
ResBlock(edim(4), out_channels=edim(3), use_conv=True, dims=1, down=True),
|
||||||
|
ResBlock(edim(3), out_channels=edim(3), use_conv=True, dims=1),
|
||||||
|
ResBlock(edim(3), out_channels=edim(2), use_conv=True, dims=1, down=True),
|
||||||
|
ResBlock(edim(2), out_channels=edim(2), use_conv=True, dims=1),
|
||||||
|
ResBlock(edim(2), out_channels=hidden_dim, use_conv=True, dims=1, down=True))
|
||||||
|
self.encoder = nn.Sequential(
|
||||||
|
AttentionBlock(hidden_dim, 4, do_activation=True),
|
||||||
|
ResBlock(hidden_dim, out_channels=hidden_dim, use_conv=True, dims=1),
|
||||||
|
AttentionBlock(hidden_dim, 4, do_activation=True),
|
||||||
|
ResBlock(hidden_dim, out_channels=hidden_dim, use_conv=True, dims=1),
|
||||||
|
AttentionBlock(hidden_dim, 4, do_activation=True),
|
||||||
|
ResBlock(hidden_dim, out_channels=hidden_dim, use_conv=True, dims=1),
|
||||||
|
nn.GroupNorm(8, hidden_dim),
|
||||||
|
nn.SiLU(),
|
||||||
|
nn.Conv1d(hidden_dim, embedding_dim, 1)
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
h = self.downsampler(x)
|
||||||
|
h = self.encoder(h)
|
||||||
|
return h
|
||||||
|
|
||||||
|
|
||||||
|
class GptMusicLower(nn.Module):
|
||||||
|
def __init__(self, dim, layers, encoder_out_dim, dropout=0, num_target_vectors=8192, fp16=True, num_vaes=4, vqargs={}):
|
||||||
|
super().__init__()
|
||||||
|
self.num_vaes = num_vaes
|
||||||
|
self.start_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||||
|
self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
|
||||||
|
n_inner=dim*2, attn_pdrop=dropout, resid_pdrop=dropout, gradient_checkpointing=True,
|
||||||
|
use_cache=False)
|
||||||
|
|
||||||
|
self.target_quantizers = nn.ModuleList([DiscreteVAE(**vqargs).eval() for _ in range(num_vaes)])
|
||||||
|
self.upper_encoder = UpperEncoder(256, dim, encoder_out_dim)
|
||||||
|
self.encoder_projector = nn.Conv1d(encoder_out_dim, dim, 1)
|
||||||
|
self.fp16 = fp16
|
||||||
|
|
||||||
|
# Freeze the target quantizer.
|
||||||
|
for p in self.target_quantizers.parameters():
|
||||||
|
p.DO_NOT_TRAIN = True
|
||||||
|
p.requires_grad = False
|
||||||
|
# And delete the decoder, which is unused.
|
||||||
|
for tq in self.target_quantizers:
|
||||||
|
del tq.decoder
|
||||||
|
|
||||||
|
self.gpt = GPT2Model(self.config)
|
||||||
|
del self.gpt.wte # Unused, we'll do our own embeddings.
|
||||||
|
|
||||||
|
self.embeddings = nn.ModuleList([nn.Embedding(num_target_vectors, dim // num_vaes) for _ in range(num_vaes)])
|
||||||
|
self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_vaes)])
|
||||||
|
|
||||||
|
def forward(self, mel, return_latent=False):
|
||||||
|
unused_params = []
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
codes = []
|
||||||
|
partition_size = mel.shape[1] // len(self.target_quantizers)
|
||||||
|
for i, q in enumerate(self.target_quantizers):
|
||||||
|
mel_partition = mel[:, i*partition_size:(i+1)*partition_size]
|
||||||
|
codes.append(q.get_codebook_indices(mel_partition))
|
||||||
|
codes = torch.stack(codes, dim=-1)
|
||||||
|
|
||||||
|
upper_vector = self.upper_encoder(mel)
|
||||||
|
upper_vector = self.encoder_projector(upper_vector)
|
||||||
|
# WTB slerp
|
||||||
|
upper_vector = F.interpolate(upper_vector, size=codes.shape[1], mode='linear')
|
||||||
|
upper_vector = upper_vector.permute(0,2,1)
|
||||||
|
|
||||||
|
inputs = codes[:, :-1]
|
||||||
|
targets = codes
|
||||||
|
upper_vector = upper_vector[:, :-1]
|
||||||
|
h = [embedding(inputs[:, :, i]) for i, embedding in enumerate(self.embeddings)]
|
||||||
|
h = torch.cat(h, dim=-1) + upper_vector
|
||||||
|
|
||||||
|
with torch.autocast(mel.device.type, enabled=self.fp16):
|
||||||
|
# Stick the conditioning embedding on the front of the input sequence.
|
||||||
|
# The transformer will learn how to integrate it.
|
||||||
|
# This statement also serves to pre-pad the inputs by one token, which is the basis of the next-token-prediction task. IOW: this is the "START" token.
|
||||||
|
h = torch.cat([self.start_token.repeat(h.shape[0], 1, 1), h], dim=1)
|
||||||
|
|
||||||
|
h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
|
||||||
|
|
||||||
|
if return_latent:
|
||||||
|
return h.float()
|
||||||
|
|
||||||
|
losses = 0
|
||||||
|
for i, head in enumerate(self.heads):
|
||||||
|
logits = head(h).permute(0,2,1)
|
||||||
|
loss = F.cross_entropy(logits, targets[:,:,i])
|
||||||
|
losses = losses + loss
|
||||||
|
|
||||||
|
unused_adder = 0
|
||||||
|
for p in unused_params:
|
||||||
|
unused_adder = unused_adder + p.mean() * 0
|
||||||
|
losses = losses + unused_adder
|
||||||
|
|
||||||
|
return losses / self.num_vaes
|
||||||
|
|
||||||
|
def get_grad_norm_parameter_groups(self):
|
||||||
|
groups = {
|
||||||
|
'gpt': list(self.gpt.parameters()),
|
||||||
|
'heads': list(self.heads.parameters()),
|
||||||
|
'embeddings': list(self.embeddings.parameters()),
|
||||||
|
'upper_latent_encoder': list(self.upper_encoder.encoder.parameters()),
|
||||||
|
'upper_latent_downsampler': list(self.upper_encoder.downsampler.parameters()),
|
||||||
|
}
|
||||||
|
return groups
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def register_music_gpt_lower2(opt_net, opt):
|
||||||
|
return GptMusicLower(**opt_get(opt_net, ['kwargs'], {}))
|
||||||
|
|
||||||
|
|
||||||
|
def test_lower():
|
||||||
|
model = GptMusicLower(dim=1024, encoder_out_dim=256, layers=16, fp16=False, num_target_vectors=8192, num_vaes=4,
|
||||||
|
vqargs= {'positional_dims': 1, 'channels': 64,
|
||||||
|
'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
|
||||||
|
'num_layers': 0, 'record_codes': True, 'kernel_size': 3, 'use_transposed_convs': False,
|
||||||
|
})
|
||||||
|
quants = ['X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_low\\models\\7500_generator.pth',
|
||||||
|
'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_low\\models\\11000_generator.pth',
|
||||||
|
'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_high\\models\\11500_generator.pth',
|
||||||
|
'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_high\\models\\11500_generator.pth']
|
||||||
|
for i, qfile in enumerate(quants):
|
||||||
|
quant_weights = torch.load(qfile)
|
||||||
|
model.target_quantizers[i].load_state_dict(quant_weights, strict=False)
|
||||||
|
torch.save(model.state_dict(), 'sample.pth')
|
||||||
|
print_network(model)
|
||||||
|
|
||||||
|
mel = torch.randn(2,256,400)
|
||||||
|
model(mel)
|
||||||
|
pg = model.get_grad_norm_parameter_groups()
|
||||||
|
|
||||||
|
t = 0
|
||||||
|
for k, vs in pg.items():
|
||||||
|
s = 0
|
||||||
|
for v in vs:
|
||||||
|
m = 1
|
||||||
|
for d in v.shape:
|
||||||
|
m *= d
|
||||||
|
s += m
|
||||||
|
t += s
|
||||||
|
print(k, s/1000000)
|
||||||
|
print(t/1000000)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
test_lower()
|
|
@ -339,7 +339,7 @@ class Trainer:
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
parser = argparse.ArgumentParser()
|
parser = argparse.ArgumentParser()
|
||||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_tts_unified_alignment.yml')
|
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_gpt.yml')
|
||||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||||
args = parser.parse_args()
|
args = parser.parse_args()
|
||||||
opt = option.parse(args.opt, is_train=True)
|
opt = option.parse(args.opt, is_train=True)
|
||||||
|
|
Loading…
Reference in New Issue
Block a user