Sy03andYueqian Lin
58007c0084
Signed-off-by: Sysy <1370724210@qq.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
# SPDX-License-Identifier: MIT
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# SPDX-FileCopyrightText: Copyright contributors to the Amphion project
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"""Inference-only EnhancedCodec decoder for IndexTTS 2.5.
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The checkpoint originates from the Amphion EnhancedCodec implementation
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vendored by IndexTTS 2.5. The encoder and training paths are deliberately not
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constructed because vLLM-Omni only performs semantic-code decoding here.
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"""
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from __future__ import annotations
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import logging
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from collections.abc import Mapping
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from pathlib import Path
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from typing import Any
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .quantizer import ResidualVQDecoder
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from .vocos import VocosBackbone
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logger = logging.getLogger(__name__)
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def _config_value(
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cfg: Mapping[str, Any] | Any | None,
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name: str,
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default: Any,
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) -> Any:
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if cfg is None:
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return default
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if isinstance(cfg, Mapping):
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return cfg.get(name, default)
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return getattr(cfg, name, default)
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class EnhancedCodec(nn.Module):
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"""Subset of EnhancedCodec required for ``decode(codes)``."""
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def __init__(
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self,
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codebook_size: int = 8192,
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hidden_size: int = 1024,
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codebook_dim: int = 8,
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vocos_dim: int = 384,
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vocos_intermediate_dim: int = 2048,
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vocos_num_layers: int = 12,
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num_quantizers: int = 1,
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downsample_scale: int | None = 2,
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cfg: Mapping[str, Any] | Any | None = None,
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):
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super().__init__()
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codebook_size = int(_config_value(cfg, "codebook_size", codebook_size))
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hidden_size = int(_config_value(cfg, "hidden_size", hidden_size))
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codebook_dim = int(_config_value(cfg, "codebook_dim", codebook_dim))
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vocos_dim = int(_config_value(cfg, "vocos_dim", vocos_dim))
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vocos_intermediate_dim = int(_config_value(cfg, "vocos_intermediate_dim", vocos_intermediate_dim))
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vocos_num_layers = int(_config_value(cfg, "vocos_num_layers", vocos_num_layers))
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num_quantizers = int(_config_value(cfg, "num_quantizers", num_quantizers))
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downsample_scale = _config_value(
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cfg,
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"downsample_scale",
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downsample_scale,
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)
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self.hidden_size = hidden_size
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self.num_quantizers = num_quantizers
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self.downsample_scale = downsample_scale
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self.decoder = nn.Sequential(
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VocosBackbone(
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input_channels=hidden_size,
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dim=vocos_dim,
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intermediate_dim=vocos_intermediate_dim,
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num_layers=vocos_num_layers,
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),
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nn.Linear(vocos_dim, hidden_size),
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)
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self.quantizer = ResidualVQDecoder(
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input_dim=hidden_size,
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num_quantizers=num_quantizers,
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codebook_size=codebook_size,
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codebook_dim=codebook_dim,
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)
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if downsample_scale is not None and downsample_scale > 1:
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self.up = nn.Conv1d(
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hidden_size,
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hidden_size,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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def decode(self, codes: torch.Tensor) -> torch.Tensor:
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"""Decode `[B,T]` or `[N,B,T]` codes to `[B,T*scale,hidden]`."""
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if codes.ndim == 2:
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codes = codes.unsqueeze(0)
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if codes.ndim != 3:
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raise ValueError(f"EnhancedCodec codes must have shape [B,T] or [N,B,T], got {tuple(codes.shape)}")
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if codes.shape[0] != self.num_quantizers:
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raise ValueError(f"EnhancedCodec expected {self.num_quantizers} codebooks, got {codes.shape[0]}")
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hidden = self.quantizer.vq2emb(codes)
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hidden = self.decoder(hidden)
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if self.downsample_scale is not None and self.downsample_scale > 1:
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hidden = hidden.transpose(1, 2)
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hidden = F.interpolate(
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hidden,
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scale_factor=self.downsample_scale,
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mode="nearest",
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)
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hidden = self.up(hidden).transpose(1, 2)
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return hidden
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def load_checkpoint(self, checkpoint_path: str) -> None:
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path = Path(checkpoint_path)
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if not path.is_file():
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raise FileNotFoundError(f"EnhancedCodec checkpoint not found: {path}")
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checkpoint = torch.load(path, map_location="cpu", weights_only=True)
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saved_state = checkpoint.get("model", checkpoint)
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own_state = self.state_dict()
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missing = [
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name
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for name, value in own_state.items()
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if name not in saved_state or saved_state[name].shape != value.shape
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]
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if missing:
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raise RuntimeError("EnhancedCodec checkpoint is missing required decoder weights: " + ", ".join(missing))
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self.load_state_dict(
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{name: saved_state[name] for name in own_state},
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strict=True,
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)
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logger.info(
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"Loaded %d inference-only EnhancedCodec tensors from %s",
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len(own_state),
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path,
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)
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