forked from mrq/DL-Art-School
update environment and fix a bunch of deps
This commit is contained in:
James Betker
parent
45afefabed
commit
f8108cfdb2
2
.idea/.gitignore
vendored
2
.idea/.gitignore
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@ -1,2 +0,0 @@
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# Default ignored files
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/workspace.xml
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8
.idea/dlas.iml
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.idea/dlas.iml
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@ -0,0 +1,8 @@
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19
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@ -0,0 +1,19 @@
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@ -1,19 +0,0 @@
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@ -2,7 +2,7 @@
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@ -1,9 +1,6 @@
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150
.idea/workspace.xml
Normal file
150
.idea/workspace.xml
Normal file
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@ -0,0 +1,150 @@
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<change beforePath="$PROJECT_DIR$/codes/models/audio/music/unet_diffusion_music_codes.py" beforeDir="false" afterPath="$PROJECT_DIR$/codes/models/audio/music/unet_diffusion_music_codes.py" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/codes/models/audio/tts/diffusion_encoder.py" beforeDir="false" afterPath="$PROJECT_DIR$/codes/models/audio/tts/diffusion_encoder.py" afterDir="false" />
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<method v="2" />
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<option name="SDK_HOME" value="" />
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<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/codes" />
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<option name="IS_MODULE_SDK" value="true" />
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<option name="ADD_CONTENT_ROOTS" value="true" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/codes/trainer/eval/music_diffusion_fid.py" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<SUITE FILE_PATH="coverage/dlas$music_diffusion_fid.coverage" NAME="music_diffusion_fid Coverage Results" MODIFIED="1658727714920" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/codes" />
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<SUITE FILE_PATH="coverage/dlas$arch_util.coverage" NAME="arch_util Coverage Results" MODIFIED="1658726906198" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/codes/" />
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<SUITE FILE_PATH="coverage/dlas$Doctests_in_arch_util.coverage" NAME="Doctests in arch_util Coverage Results" MODIFIED="1658726625256" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/codes/models" />
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@ -483,16 +483,24 @@ class RelativeQKBias(nn.Module):
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"""
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Very simple relative position bias scheme which should be directly added to QK matrix. This bias simply applies to
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the distance from the given element.
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If symmetric=False, a different bias is applied to each side of the input element, otherwise the bias is symmetric.
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"""
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def __init__(self, l, max_positions=4000):
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def __init__(self, l, max_positions=4000, symmetric=True):
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super().__init__()
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if symmetric:
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self.emb = nn.Parameter(torch.randn(l+1) * .01)
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o = torch.arange(0,max_positions)
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c = o.unsqueeze(-1).repeat(1,max_positions)
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r = o.unsqueeze(0).repeat(max_positions,1)
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M = ((-(r-c).abs())+l).clamp(0,l)
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else:
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self.emb = nn.Parameter(torch.randn(l*2+2) * .01)
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a = torch.arange(0,max_positions)
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c = a.unsqueeze(-1) - a
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m = (c >= -l).logical_and(c <= l)
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M = (l+c+1)*m
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self.register_buffer('M', M, persistent=False)
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self.initted = False
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def forward(self, n):
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# Ideally, I'd return this:
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@ -8,7 +8,6 @@ import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision # For debugging, not actually used.
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from x_transformers.x_transformers import RelativePositionBias
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from models.audio.music.gpt_music import GptMusicLower
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from models.audio.music.music_quantizer import MusicQuantizer
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@ -291,10 +290,6 @@ class AttentionBlock(nn.Module):
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
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if relative_pos_embeddings:
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self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
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else:
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self.relative_pos_embeddings = None
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def forward(self, x, mask=None):
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if self.do_checkpoint:
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@ -306,7 +301,7 @@ class AttentionBlock(nn.Module):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
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qkv = self.qkv(self.norm(x))
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h = self.attention(qkv, mask, self.relative_pos_embeddings)
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h = self.attention(qkv, mask)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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@ -6,7 +6,7 @@ from functools import partial
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import torch
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import torch.nn as nn
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from x_transformers.x_transformers import groupby_prefix_and_trim, FixedPositionalEmbedding, default, RotaryEmbedding, \
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DEFAULT_DIM_HEAD, RelativePositionBias, LearnedAlibiPositionalBias, AlibiPositionalBias, ScaleNorm, RMSNorm, Rezero, \
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DEFAULT_DIM_HEAD, RelativePositionBias, LearnedAlibiPositionalBias, AlibiPositionalBias, ScaleNorm, RMSNorm, \
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exists, Attention, FeedForward, Scale, ShiftTokens, GRUGating, Residual, cast_tuple, equals, LayerIntermediates, \
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AttentionLayers, not_equals
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@ -8,7 +8,6 @@ import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision # For debugging, not actually used.
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from x_transformers.x_transformers import RelativePositionBias
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from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
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from models.diffusion.nn import (
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@ -298,7 +297,6 @@ class AttentionBlock(nn.Module):
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num_head_channels=-1,
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use_new_attention_order=False,
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do_checkpoint=True,
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relative_pos_embeddings=False,
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):
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super().__init__()
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self.channels = channels
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@ -320,10 +318,6 @@ class AttentionBlock(nn.Module):
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
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if relative_pos_embeddings:
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self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
|
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else:
|
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self.relative_pos_embeddings = None
|
||||
|
||||
def forward(self, x, mask=None):
|
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if self.do_checkpoint:
|
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|
|
@ -335,7 +329,7 @@ class AttentionBlock(nn.Module):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
|
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qkv = self.qkv(self.norm(x))
|
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h = self.attention(qkv, mask, self.relative_pos_embeddings)
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h = self.attention(qkv, mask)
|
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h = self.proj_out(h)
|
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return (x + h).reshape(b, c, *spatial)
|
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|
||||
|
|
|
|||
|
|
@ -10,11 +10,8 @@ from collections import namedtuple
|
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from einops import rearrange, repeat, reduce
|
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from einops.layers.torch import Rearrange
|
||||
|
||||
from entmax import entmax15
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
from x_transformers.autoregressive_wrapper import AutoregressiveWrapper
|
||||
|
||||
DEFAULT_DIM_HEAD = 64
|
||||
|
||||
Intermediates = namedtuple('Intermediates', [
|
||||
|
|
@ -1274,51 +1271,3 @@ class ContinuousTransformerWrapper(nn.Module):
|
|||
if len(res) > 1:
|
||||
return tuple(res)
|
||||
return res[0]
|
||||
|
||||
|
||||
class XTransformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
dim,
|
||||
tie_token_emb=False,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs)
|
||||
dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs)
|
||||
|
||||
assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword'
|
||||
enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs)
|
||||
enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0)
|
||||
enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None)
|
||||
enc_transformer_kwargs['use_pos_emb'] = enc_kwargs.pop('use_pos_emb', True)
|
||||
|
||||
dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs)
|
||||
dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0)
|
||||
dec_transformer_kwargs['use_pos_emb'] = dec_kwargs.pop('use_pos_emb', True)
|
||||
|
||||
self.encoder = TransformerWrapper(
|
||||
**enc_transformer_kwargs,
|
||||
attn_layers=Encoder(dim=dim, **enc_kwargs)
|
||||
)
|
||||
|
||||
self.decoder = TransformerWrapper(
|
||||
**dec_transformer_kwargs,
|
||||
attn_layers=Decoder(dim=dim, cross_attend=True, **dec_kwargs)
|
||||
)
|
||||
|
||||
if tie_token_emb:
|
||||
self.decoder.token_emb = self.encoder.token_emb
|
||||
|
||||
self.decoder = AutoregressiveWrapper(self.decoder)
|
||||
|
||||
@torch.no_grad()
|
||||
def generate(self, seq_in, seq_out_start, seq_len, src_mask=None, src_attn_mask=None, **kwargs):
|
||||
encodings = self.encoder(seq_in, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True)
|
||||
return self.decoder.generate(seq_out_start, seq_len, context=encodings, context_mask=src_mask, **kwargs)
|
||||
|
||||
def forward(self, src, tgt, src_mask=None, tgt_mask=None, src_attn_mask=None):
|
||||
enc = self.encoder(src, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True)
|
||||
out = self.decoder(tgt, context=enc, mask=tgt_mask, context_mask=src_mask)
|
||||
return out
|
||||
|
|
|
|||
|
|
@ -30,11 +30,13 @@ Unidecode==1.0.22
|
|||
tgt == 1.4.4
|
||||
pyworld == 0.2.10
|
||||
audio2numpy
|
||||
SoundFile
|
||||
|
||||
# For text stuff
|
||||
transformers
|
||||
tokenizers
|
||||
jiwer # calculating WER
|
||||
omegaconf
|
||||
|
||||
# lucidrains stuff
|
||||
vector_quantize_pytorch
|
||||
|
|
@ -43,3 +45,4 @@ rotary-embedding-torch
|
|||
axial_positional_embedding
|
||||
g-mlp-pytorch
|
||||
x-clip
|
||||
x_transformers
|
||||
|
|
@ -315,14 +315,15 @@ class MusicDiffusionFid(evaluator.Evaluator):
|
|||
|
||||
if __name__ == '__main__':
|
||||
# For multilevel SR:
|
||||
"""
|
||||
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr.yml', 'generator',
|
||||
also_load_savepoint=False, strict_load=False,
|
||||
load_path='X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr\\models\\18000_generator.pth'
|
||||
load_path='X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr_archived_prev2\\models\\18000_generator.pth'
|
||||
).cuda()
|
||||
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
|
||||
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
|
||||
'diffusion_steps': 256, # basis: 192
|
||||
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': True,
|
||||
'diffusion_steps': 64, # basis: 192
|
||||
'conditioning_free': False, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': True,
|
||||
'diffusion_schedule': 'cosine', 'diffusion_type': 'chained_sr',
|
||||
}
|
||||
|
||||
|
|
@ -334,13 +335,12 @@ if __name__ == '__main__':
|
|||
).cuda()
|
||||
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
|
||||
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
|
||||
'diffusion_steps': 256, # basis: 192
|
||||
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': True,
|
||||
'diffusion_steps': 64, # basis: 192
|
||||
'conditioning_free': False, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': True,
|
||||
'diffusion_schedule': 'cosine', 'diffusion_type': 'from_codes_quant',
|
||||
}
|
||||
"""
|
||||
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 11, 'device': 'cuda', 'opt': {}}
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 12, 'device': 'cuda', 'opt': {}}
|
||||
eval = MusicDiffusionFid(diffusion, opt_eval, env)
|
||||
fds = []
|
||||
for i in range(2):
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user