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# Copyright 2024 Xiaomi Corp. (authors: Wei Kang
# Han Zhu)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.
from typing import List
import torch
from zipvoice.models.modules.solver import DistillEulerSolver
from zipvoice.models.modules.zipformer import TTSZipformer
from zipvoice.models.zipvoice import ZipVoice
class ZipVoiceDistill(ZipVoice):
"""ZipVoice-Distill model."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
required_params = {
"feat_dim",
"fm_decoder_downsampling_factor",
"fm_decoder_num_layers",
"fm_decoder_cnn_module_kernel",
"fm_decoder_dim",
"fm_decoder_feedforward_dim",
"fm_decoder_num_heads",
"query_head_dim",
"pos_head_dim",
"value_head_dim",
"pos_dim",
"time_embed_dim",
}
missing = [p for p in required_params if p not in kwargs]
if missing:
raise ValueError(f"Missing required parameters: {', '.join(missing)}")
self.fm_decoder = TTSZipformer(
in_dim=kwargs["feat_dim"] * 3,
out_dim=kwargs["feat_dim"],
downsampling_factor=kwargs["fm_decoder_downsampling_factor"],
num_encoder_layers=kwargs["fm_decoder_num_layers"],
cnn_module_kernel=kwargs["fm_decoder_cnn_module_kernel"],
encoder_dim=kwargs["fm_decoder_dim"],
feedforward_dim=kwargs["fm_decoder_feedforward_dim"],
num_heads=kwargs["fm_decoder_num_heads"],
query_head_dim=kwargs["query_head_dim"],
pos_head_dim=kwargs["pos_head_dim"],
value_head_dim=kwargs["value_head_dim"],
pos_dim=kwargs["pos_dim"],
use_time_embed=True,
time_embed_dim=kwargs["time_embed_dim"],
use_guidance_scale_embed=True,
)
self.solver = DistillEulerSolver(self, func_name="forward_fm_decoder")
def forward(
self,
tokens: List[List[int]],
features: torch.Tensor,
features_lens: torch.Tensor,
noise: torch.Tensor,
speech_condition_mask: torch.Tensor,
t_start: float,
t_end: float,
num_step: int = 1,
guidance_scale: torch.Tensor = None,
) -> torch.Tensor:
return self.sample_intermediate(
tokens=tokens,
features=features,
features_lens=features_lens,
noise=noise,
speech_condition_mask=speech_condition_mask,
t_start=t_start,
t_end=t_end,
num_step=num_step,
guidance_scale=guidance_scale,
)
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