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d81a465 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | from __future__ import annotations
from collections import deque
from dataclasses import dataclass, field
import json
from pathlib import Path
import torch
from safetensors import safe_open
from torch import nn
from .teacher import TeacherAcousticModel
from .sequence import Action
from .sampling import sample_token
from .generation import AdvanceStatus, GenerationResult
class NativeClockTalker(nn.Module):
"""Teacher-initialized acoustic model with an incremental native text clock."""
codebook_size = 2048
hidden_size = 1024
eos_class = 2048
supported_languages = ("chinese", "english", "auto")
@classmethod
@torch.no_grad()
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",
) -> "NativeClockTalker":
teacher_path = Path(teacher_path)
source = TeacherAcousticModel.from_teacher(
teacher_path, native_tokenizer_path, speaker_vector_path,
dtype=dtype, attn_implementation=attn_implementation,
)
model = cls()
for name in ("backbone", "text_embedding", "text_projection", "codec_history_embeddings",
"residual_predictor", "q0_head"):
setattr(model, name, getattr(source, name))
model.codec_bos = source.codec_bos
model.native_teacher_ids = source.native_teacher_ids.tolist()
model.native_teacher_offsets = source.native_teacher_offsets.tolist()
model.codec_eos_head = nn.Linear(model.hidden_size, 1, bias=False, dtype=dtype)
config = json.loads((teacher_path / "config.json").read_text())
talker = config["talker_config"]
vocabulary = json.loads((teacher_path / "vocab.json").read_text())
def text(ids):
return model.text_projection(model.text_embedding(torch.tensor(ids)))
tts_bos, tts_eos, tts_pad = text([config["tts_bos_token_id"], config["tts_eos_token_id"],
config["tts_pad_token_id"]])
role = text([config["im_start_token_id"], config["assistant_token_id"], vocabulary["Ċ"]])
with safe_open(teacher_path / "model.safetensors", framework="pt", device="cpu") as weights:
codec = weights.get_tensor("talker.model.codec_embedding.weight").to(dtype=dtype)
language = [talker["codec_think_id"], talker["codec_think_bos_id"],
talker["codec_language_id"]["chinese"], talker["codec_think_eos_id"]]
prefix = torch.cat((role, codec[language] + tts_pad,
(source.speaker_vector + tts_pad).unsqueeze(0),
(codec[talker["codec_pad_id"]] + tts_bos).unsqueeze(0)), dim=0)
english = prefix.clone()
english[5] = codec[talker["codec_language_id"]["english"]] + tts_pad
auto = torch.cat((role, codec[[talker["codec_nothink_id"],
talker["codec_think_bos_id"], talker["codec_think_eos_id"]]] + tts_pad,
prefix[7:]), dim=0)
model.codec_eos_head.weight.copy_(
weights.get_tensor("talker.codec_head.weight")[talker["codec_eos_token_id"]].unsqueeze(0)
)
model.register_buffer("prefix", prefix.unsqueeze(0), persistent=False)
model.register_buffer("prefix_english", english.unsqueeze(0), persistent=False)
model.register_buffer("prefix_auto", auto.unsqueeze(0), persistent=False)
model.register_buffer("tts_eos", tts_eos.view(1, 1, -1), persistent=False)
model.register_buffer("tts_pad", tts_pad.view(1, 1, -1), persistent=False)
return model
def new_state(self, **kwargs) -> "NativeClockState":
return NativeClockState(model=self, **kwargs)
def prefix_for_language(self, language: str) -> torch.Tensor:
if language == "chinese":
return self.prefix
if language == "english":
return self.prefix_english
if language == "auto":
return self.prefix_auto
raise ValueError(f"Unsupported speech language: {language}")
def mapped_teacher_ids(self, native_id: int) -> list[int]:
if not 0 <= native_id < len(self.native_teacher_offsets) - 1:
return []
start, end = self.native_teacher_offsets[native_id:native_id + 2]
return self.native_teacher_ids[start:end]
def history_features(self, codes: torch.Tensor) -> torch.Tensor:
return sum(embedding(codes[..., group])
for group, embedding in enumerate(self.codec_history_embeddings))
@dataclass
class NativeClockState:
model: NativeClockTalker
language: str = "chinese"
max_frames: int = 1536
max_events: int = 4096
do_sample: bool = False
top_k: int = 50
top_p: float = 1.0
temperature: float = 0.9
repetition_penalty: float = 1.05
sampling_generator: torch.Generator | None = None
pending: deque = field(default_factory=deque)
native_token_ids: list[int] = field(default_factory=list)
native_remaining: list[int] = field(default_factory=list)
teacher_token_ids: list[int] = field(default_factory=list)
teacher_token_owners: list[int] = field(default_factory=list)
consumed_teacher_tokens: int = 0
consumed_tokens: int = 0
frames: list[torch.Tensor] = field(default_factory=list)
actions: list[int] = field(default_factory=list)
frame_conditioned_text_tokens: list[int] = field(default_factory=list)
frame_arrived_text_tokens: list[int] = field(default_factory=list)
frame_available_teacher_tokens: list[int] = field(default_factory=list)
frame_consumed_teacher_tokens: list[int] = field(default_factory=list)
cache: object | None = None
previous_codes: torch.Tensor | None = None
input_ended: bool = False
text_end_consumed: bool = False
text_end_at_codec_frame: int | None = None
ended: bool = False
cancelled: bool = False
event_count: int = 0
suppressed_early_eos: int = 0
q0_eos_generated: bool = False
status: AdvanceStatus = AdvanceStatus.WAIT_INPUT
@property
def consumed_native_tokens(self) -> int:
return self.consumed_tokens
def _update_consumed_native(self) -> None:
while self.consumed_tokens < len(self.native_remaining) and self.native_remaining[self.consumed_tokens] == 0:
self.consumed_tokens += 1
def push_token(self, native_id: int) -> None:
if self.input_ended or self.ended:
raise RuntimeError("Cannot append native text after the input has ended")
pieces = self.model.mapped_teacher_ids(int(native_id))
index = len(self.native_token_ids)
self.native_token_ids.append(int(native_id))
self.native_remaining.append(len(pieces))
self.teacher_token_ids.extend(pieces)
self.teacher_token_owners.extend([index] * len(pieces))
self.pending.extend((piece, index) for piece in pieces)
self._update_consumed_native()
def end_input(self) -> None:
self.input_ended = True
finish = end_input
def cancel(self) -> None:
self.cancelled = True
self.ended = True
self.status = AdvanceStatus.FORCED_STOP
self.cache = None
self.previous_codes = None
self.pending.clear()
def _result(self, status: AdvanceStatus) -> GenerationResult:
self.status = status
return GenerationResult(self.frames, status, self.actions, self.consumed_tokens, self.text_end_consumed)
@torch.inference_mode()
def advance(self, max_new_frames: int = 4) -> GenerationResult:
if self.ended:
return self._result(self.status)
produced = 0
while produced < max_new_frames:
if len(self.frames) >= self.max_frames or self.event_count >= self.max_events:
self.ended = True
return self._result(AdvanceStatus.FORCED_STOP)
if self.pending:
teacher_id, native_index = self.pending.popleft()
ids = torch.tensor([[teacher_id]], device=self.model.q0_head.weight.device)
text = self.model.text_projection(self.model.text_embedding(ids))
self.native_remaining[native_index] -= 1
self._update_consumed_native()
self.consumed_teacher_tokens += 1
elif not self.input_ended:
return self._result(AdvanceStatus.WAIT_INPUT)
elif not self.text_end_consumed:
text = self.model.tts_eos
self.text_end_consumed = True
self.text_end_at_codec_frame = len(self.frames)
else:
text = self.model.tts_pad
if self.cache is None:
inputs = torch.cat((self.model.prefix_for_language(self.language),
self.model.codec_bos.view(1, 1, -1) + text), dim=1)
else:
inputs = self.model.history_features(self.previous_codes).unsqueeze(1) + text
output = self.model.backbone(inputs_embeds=inputs, past_key_values=self.cache, use_cache=True)
self.cache = output.past_key_values
hidden = output.last_hidden_state[:, -1]
logits = torch.cat((self.model.q0_head(hidden), self.model.codec_eos_head(hidden)), dim=-1).float()
if self.frames and self.repetition_penalty != 1.0:
seen = torch.tensor(list({int(frame[0]) for frame in self.frames}), device=logits.device)
scores = logits[:, seen]
logits[:, seen] = torch.where(scores < 0, scores * self.repetition_penalty,
scores / self.repetition_penalty)
eos_allowed = self.input_ended and not self.pending and self.text_end_consumed
if not eos_allowed:
self.suppressed_early_eos += int(logits.argmax(-1).item() == self.model.eos_class)
logits[:, self.model.eos_class] = -torch.inf
q0 = sample_token(logits, do_sample=self.do_sample, top_k=self.top_k, top_p=self.top_p,
temperature=self.temperature, generator=self.sampling_generator)
self.event_count += 1
if int(q0.item()) == self.model.eos_class:
self.ended = True
self.q0_eos_generated = True
self.actions.append(int(Action.END))
return self._result(AdvanceStatus.END_AUDIO)
codes = self.model.residual_predictor.generate(hidden, q0, do_sample=self.do_sample,
top_k=self.top_k, top_p=self.top_p, temperature=self.temperature, generator=self.sampling_generator)
self.previous_codes = codes
self.frames.append(codes[0].detach().cpu())
self.actions.append(int(Action.EMIT))
self.frame_conditioned_text_tokens.append(self.consumed_tokens)
self.frame_arrived_text_tokens.append(len(self.native_token_ids))
self.frame_available_teacher_tokens.append(len(self.teacher_token_ids))
self.frame_consumed_teacher_tokens.append(self.consumed_teacher_tokens)
produced += 1
return self._result(AdvanceStatus.PRODUCED)
def speech_unit_report(self) -> dict:
return dict(native_token_ids=self.native_token_ids, native_token_count=len(self.native_token_ids),
teacher_token_ids=self.teacher_token_ids, teacher_token_count=len(self.teacher_token_ids),
teacher_token_owners=self.teacher_token_owners,
consumed_native_tokens=self.consumed_tokens, consumed_teacher_tokens=self.consumed_teacher_tokens)
def report(self) -> dict:
return dict(**self.speech_unit_report(), model_variant="teacher_initialized_native_clock_talker_v1",
language=self.language,
terminal_status=self.status.value, q0_eos_generated=self.q0_eos_generated,
q0_eos_teacher_id=2150, suppressed_early_eos=self.suppressed_early_eos,
text_end_consumed=self.text_end_consumed, text_end_at_codec_frame=self.text_end_at_codec_frame,
frame_arrived_text_tokens=self.frame_arrived_text_tokens,
frame_conditioned_text_tokens=self.frame_conditioned_text_tokens,
frame_available_teacher_tokens=self.frame_available_teacher_tokens,
frame_consumed_teacher_tokens=self.frame_consumed_teacher_tokens,
generated_codec_frames=len(self.frames), cancelled=self.cancelled)
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