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Running
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Zero
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# Copyright (c) 2024 Alibaba Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import torch
import torch.nn as nn
from inspiremusic.music_tokenizer.env import AttrDict
from inspiremusic.music_tokenizer.models import Encoder
from inspiremusic.music_tokenizer.models import Generator
from inspiremusic.music_tokenizer.models import Quantizer
class VQVAE(nn.Module):
def __init__(self,
config_path,
ckpt_path,
with_encoder=False):
super(VQVAE, self).__init__()
ckpt = torch.load(ckpt_path)
with open(config_path) as f:
data = f.read()
json_config = json.loads(data)
self.h = AttrDict(json_config)
self.quantizer = Quantizer(self.h)
self.generator = Generator(self.h)
self.generator.load_state_dict(ckpt['generator'])
self.quantizer.load_state_dict(ckpt['quantizer'])
if with_encoder:
self.encoder = Encoder(self.h)
self.encoder.load_state_dict(ckpt['encoder'])
def forward(self, x):
# x is the codebook
# x.shape (B, T, Nq)
quant_emb = self.quantizer.embed(x)
return self.generator(quant_emb)
def encode(self, x):
batch_size = x.size(0)
if len(x.shape) == 3 and x.shape[-1] == 1:
x = x.squeeze(-1)
c = self.encoder(x.unsqueeze(1))
q, loss_q, c = self.quantizer(c)
c = [code.reshape(batch_size, -1) for code in c]
# shape: [N, T, 4]
return torch.stack(c, -1)
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