95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
# Copyright 2024 Xiaomi Corp. (authors: Wei Kang
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# Han Zhu)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List
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import torch
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from zipvoice.models.modules.solver import DistillEulerSolver
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from zipvoice.models.modules.zipformer import TTSZipformer
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from zipvoice.models.zipvoice import ZipVoice
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class ZipVoiceDistill(ZipVoice):
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"""ZipVoice-Distill model."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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required_params = {
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"feat_dim",
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"fm_decoder_downsampling_factor",
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"fm_decoder_num_layers",
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"fm_decoder_cnn_module_kernel",
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"fm_decoder_dim",
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"fm_decoder_feedforward_dim",
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"fm_decoder_num_heads",
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"query_head_dim",
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"pos_head_dim",
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"value_head_dim",
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"pos_dim",
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"time_embed_dim",
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}
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missing = [p for p in required_params if p not in kwargs]
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if missing:
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raise ValueError(f"Missing required parameters: {', '.join(missing)}")
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self.fm_decoder = TTSZipformer(
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in_dim=kwargs["feat_dim"] * 3,
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out_dim=kwargs["feat_dim"],
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downsampling_factor=kwargs["fm_decoder_downsampling_factor"],
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num_encoder_layers=kwargs["fm_decoder_num_layers"],
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cnn_module_kernel=kwargs["fm_decoder_cnn_module_kernel"],
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encoder_dim=kwargs["fm_decoder_dim"],
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feedforward_dim=kwargs["fm_decoder_feedforward_dim"],
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num_heads=kwargs["fm_decoder_num_heads"],
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query_head_dim=kwargs["query_head_dim"],
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pos_head_dim=kwargs["pos_head_dim"],
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value_head_dim=kwargs["value_head_dim"],
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pos_dim=kwargs["pos_dim"],
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use_time_embed=True,
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time_embed_dim=kwargs["time_embed_dim"],
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use_guidance_scale_embed=True,
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)
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self.solver = DistillEulerSolver(self, func_name="forward_fm_decoder")
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def forward(
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self,
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tokens: List[List[int]],
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features: torch.Tensor,
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features_lens: torch.Tensor,
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noise: torch.Tensor,
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speech_condition_mask: torch.Tensor,
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t_start: float,
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t_end: float,
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num_step: int = 1,
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guidance_scale: torch.Tensor = None,
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) -> torch.Tensor:
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return self.sample_intermediate(
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tokens=tokens,
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features=features,
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features_lens=features_lens,
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noise=noise,
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speech_condition_mask=speech_condition_mask,
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t_start=t_start,
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t_end=t_end,
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num_step=num_step,
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guidance_scale=guidance_scale,
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)
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