Initial commit

zipvoice/models/zipvoice_distill.py

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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,
+        )