File size: 8,590 Bytes
f74eb65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import torch
from safetensors import safe_open
from torch import nn
import torch.nn.functional as F
from .residual_predictor import Qwen3TTSResidualPredictor
def native_teacher_mapping(
native_tokenizer_path: str | Path,
teacher_path: str | Path,
native_vocabulary_size: int | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Map exact ByteLevel pieces to teacher IDs, with empty special-token bags."""
native_path = Path(native_tokenizer_path)
if native_path.is_dir():
native_path = native_path / "tokenizer.json"
native = json.loads(native_path.read_text())["model"]["vocab"]
teacher = json.loads((Path(teacher_path) / "vocab.json").read_text())
visible = list(range(33, 127)) + list(range(161, 173)) + list(range(174, 256))
inverse = {chr(byte): byte for byte in visible}
inverse.update({chr(256 + index): byte for index, byte in
enumerate(byte for byte in range(256) if byte not in visible)})
def raw(piece: str) -> bytes:
return bytes(inverse[character] for character in piece)
teacher_bytes = {raw(piece): index for piece, index in teacher.items()}
by_first_byte: dict[int, dict[bytes, int]] = {}
for piece, index in teacher_bytes.items():
by_first_byte.setdefault(piece[0], {})[piece] = index
widths = {first: sorted({len(piece) for piece in pieces}, reverse=True)
for first, pieces in by_first_byte.items()}
native_by_id = {index: raw(piece) for piece, index in native.items()}
size = native_vocabulary_size or (max(native_by_id) + 1)
flat, offsets = [], [0]
for native_id in range(size):
piece = native_by_id.get(native_id, b"")
if piece in teacher_bytes:
flat.append(teacher_bytes[piece])
else:
position = 0
while position < len(piece):
candidates = by_first_byte[piece[position]]
for width in widths[piece[position]]:
if width > len(piece) - position:
continue
candidate = piece[position:position + width]
if candidate in candidates:
flat.append(candidates[candidate])
position += width
break
else:
raise ValueError(f"Teacher vocabulary cannot represent native token {native_id}")
offsets.append(len(flat))
return torch.tensor(flat, dtype=torch.long), torch.tensor(offsets, dtype=torch.long)
class TeacherTextProjection(nn.Module):
def __init__(self, text_hidden_size: int, hidden_size: int):
super().__init__()
self.linear_fc1 = nn.Linear(text_hidden_size, text_hidden_size)
self.linear_fc2 = nn.Linear(text_hidden_size, hidden_size)
def forward(self, hidden: torch.Tensor) -> torch.Tensor:
return self.linear_fc2(F.silu(self.linear_fc1(hidden)))
class TeacherAcousticModel(nn.Module):
"""Pretrained text, acoustic and residual modules shared by speech models."""
codebook_size = 2048
num_code_groups = 16
def __init__(
self,
config: dict,
native_teacher_ids: torch.Tensor,
native_teacher_offsets: torch.Tensor,
speaker_vector: torch.Tensor,
*,
attn_implementation: str = "eager",
):
super().__init__()
from transformers import Qwen3Config, Qwen3Model
self.config = dict(config)
self.hidden_size = int(config["hidden_size"])
backbone_config = Qwen3Config(
vocab_size=self.codebook_size,
hidden_size=self.hidden_size,
intermediate_size=int(config["intermediate_size"]),
num_hidden_layers=int(config["num_hidden_layers"]),
num_attention_heads=int(config["num_attention_heads"]),
num_key_value_heads=int(config["num_key_value_heads"]),
head_dim=int(config["head_dim"]),
hidden_act=config["hidden_act"],
max_position_embeddings=int(config["max_position_embeddings"]),
rms_norm_eps=float(config["rms_norm_eps"]),
rope_theta=float(config["rope_theta"]),
attention_bias=bool(config["attention_bias"]),
attention_dropout=float(config.get("attention_dropout", 0.0)),
tie_word_embeddings=False,
use_cache=True,
)
backbone_config._attn_implementation = attn_implementation
self.backbone = Qwen3Model(backbone_config)
self.backbone.embed_tokens = None
self.text_embedding = nn.Embedding(int(config["text_vocab_size"]), int(config["text_hidden_size"]))
self.text_projection = TeacherTextProjection(int(config["text_hidden_size"]), self.hidden_size)
self.text_embedding.requires_grad_(False)
self.text_projection.requires_grad_(False)
self.register_buffer("native_teacher_ids", native_teacher_ids, persistent=False)
self.register_buffer("native_teacher_offsets", native_teacher_offsets, persistent=False)
self.register_buffer("speaker_vector", speaker_vector.reshape(self.hidden_size), persistent=False)
self.codec_history_embeddings = nn.ModuleList(
[nn.Embedding(self.codebook_size, self.hidden_size) for _ in range(self.num_code_groups)]
)
self.codec_bos = nn.Parameter(torch.zeros(self.hidden_size))
self.q0_head = nn.Linear(self.hidden_size, self.codebook_size, bias=False)
self.residual_predictor = Qwen3TTSResidualPredictor(
self.hidden_size, self.codebook_size, config["code_predictor_config"]
)
@classmethod
def from_teacher(
cls,
teacher_path: str | Path,
native_tokenizer_path: str | Path,
speaker_vector_path: str | Path,
*,
dtype: torch.dtype = torch.float32,
attn_implementation: str = "eager",
) -> "TeacherAcousticModel":
teacher_path, native_tokenizer_path = Path(teacher_path), Path(native_tokenizer_path)
config = json.loads((teacher_path / "config.json").read_text())["talker_config"]
native_root = native_tokenizer_path if native_tokenizer_path.is_dir() else native_tokenizer_path.parent
native_config = json.loads((native_root / "config.json").read_text())
native_size = int(native_config.get("text_config", native_config)["vocab_size"])
ids, offsets = native_teacher_mapping(native_tokenizer_path, teacher_path, native_size)
speaker = torch.as_tensor(np.load(speaker_vector_path), dtype=torch.float32)
model = cls(config, ids, offsets, speaker, attn_implementation=attn_implementation).to(dtype=dtype)
with safe_open(teacher_path / "model.safetensors", framework="pt", device="cpu") as weights:
with torch.no_grad():
for name, parameter in model.backbone.named_parameters():
parameter.copy_(weights.get_tensor(f"talker.model.{name}"))
model.text_embedding.weight.copy_(weights.get_tensor("talker.model.text_embedding.weight"))
for name, parameter in model.text_projection.named_parameters():
parameter.copy_(weights.get_tensor(f"talker.text_projection.{name}"))
codec = weights.get_tensor("talker.model.codec_embedding.weight")
model.codec_history_embeddings[0].weight.copy_(codec[:model.codebook_size])
model.codec_bos.copy_(codec[int(config["codec_bos_id"])])
model.q0_head.weight.copy_(weights.get_tensor("talker.codec_head.weight")[:model.codebook_size])
model.residual_predictor.q0_embedding.weight.copy_(codec[:model.codebook_size])
for name, parameter in model.residual_predictor.backbone.named_parameters():
parameter.copy_(weights.get_tensor(f"talker.code_predictor.model.{name}"))
for group in range(15):
embedding = weights.get_tensor(f"talker.code_predictor.model.codec_embedding.{group}.weight")
model.codec_history_embeddings[group + 1].weight.copy_(embedding)
if group < 14:
model.residual_predictor.code_embeddings[group].weight.copy_(embedding)
model.residual_predictor.heads[group].weight.copy_(
weights.get_tensor(f"talker.code_predictor.lm_head.{group}.weight")
)
return model
|