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2c4c098 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 | #!/usr/bin/env python3
# Copyright 2025 Xiaomi Corporation.
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import torch
import torch.nn.functional as F
from typing import Tuple, Union, List
class InputSegment:
def __init__(
self,
text: str = "",
audio: torch.Tensor = None,
tokenized_text: torch.Tensor = None,
speech_zeroemb_idx: Union[int, List[int]] = 1024,
text_zeroemb_idx: int = 152067,
add_sosp_eosp=True,
) -> None:
has_text = text is not None
has_tokenized_text = tokenized_text is not None
assert has_text or has_tokenized_text, "Text or tokenized text must be provided"
self.audio = audio
self.text = text
self.tokenized_text = tokenized_text
self.speech_zeroemb_idx = speech_zeroemb_idx
self.text_zeroemb_idx = text_zeroemb_idx
self.add_sosp_eosp = add_sosp_eosp
@staticmethod
def insert_between(tensor, i, value=-1):
return torch.scatter(
torch.full(
(1, tensor.shape[1] + (tensor.shape[1] - 1) * i + i),
value,
dtype=tensor.dtype,
),
1,
torch.arange(0, tensor.shape[1], dtype=torch.int64)[None] * (i + 1),
tensor,
)
def to_input_id(
self,
tokenizer,
group_size: int,
audio_channels: int = 8,
) -> Tuple[torch.Tensor, torch.Tensor]:
if self.audio is None:
if self.tokenized_text is None:
tokenized_text = tokenizer(
self.text,
return_tensors="pt",
truncation=True,
max_length=999999,
padding=False,
add_special_tokens=False,
)["input_ids"].int()
else:
tokenized_text = self.tokenized_text.unsqueeze(0)
if group_size > 1:
tokenized_text = self.insert_between(
tokenized_text, group_size - 1, value=-100
)
if isinstance(self.speech_zeroemb_idx, list):
audio_part_input_id = torch.zeros((audio_channels, tokenized_text.shape[1]), dtype=torch.int)
for i, idx in enumerate(self.speech_zeroemb_idx):
audio_part_input_id[i, :] = idx
else:
audio_part_input_id = torch.full(
(audio_channels, tokenized_text.shape[1]), self.speech_zeroemb_idx, dtype=torch.int
)
else:
sosp_token = (
tokenizer.convert_tokens_to_ids("<|sosp|>")
if self.add_sosp_eosp
else None
)
eosp_token = (
tokenizer.convert_tokens_to_ids("<|eosp|>")
if self.add_sosp_eosp
else None
)
audio_part = self.audio.reshape(-1, audio_channels).T # [audio_channels, seqlen]
assert (
audio_part.shape[1] % group_size == 0
), f"Audio shape {audio_part.shape} is not divisible by group_size {group_size}"
text_len = audio_part.shape[1] // group_size
empty_token = self.text_zeroemb_idx
if empty_token is None:
empty_token = tokenizer.eod
tokenized_text = torch.full((1, text_len), empty_token, dtype=torch.int)
tokenized_text = (
torch.cat(
[
torch.tensor([[sosp_token]], dtype=torch.int),
tokenized_text,
torch.tensor([[eosp_token]], dtype=torch.int),
],
dim=1,
)
if self.add_sosp_eosp
else tokenized_text
)
tokenized_text = self.insert_between(
tokenized_text, group_size - 1, value=-100
)
if self.add_sosp_eosp:
if isinstance(self.speech_zeroemb_idx, list):
sosp_part = torch.zeros((audio_channels, group_size), dtype=torch.int)
eosp_part = torch.zeros((audio_channels, group_size), dtype=torch.int)
for i, idx in enumerate(self.speech_zeroemb_idx):
sosp_part[i, :] = idx
eosp_part[i, :] = idx
audio_part_input_id = torch.cat([sosp_part, audio_part, eosp_part], dim=1)
else:
audio_part_input_id = torch.cat(
[
torch.full((audio_channels, group_size), self.speech_zeroemb_idx, dtype=torch.int),
audio_part,
torch.full((audio_channels, group_size), self.speech_zeroemb_idx, dtype=torch.int),
],
dim=1,
)
else:
audio_part_input_id = audio_part
input_ids = torch.cat(
[tokenized_text, audio_part_input_id], dim=0
) # [n_rvq + 1, seqlen]
return input_ids
class StreamingInputSegment:
def __init__(
self,
text: str = "",
audio: torch.Tensor = None,
tokenized_text: torch.Tensor = None,
speech_zeroemb_idx: Union[int, List[int]] = 1024,
text_zeroemb_idx: int = 152067,
text_segment_size: int = 5,
audio_segment_size: int = 5,
tokenizer=None,
group_size=None,
audio_channels=None,
) -> None:
has_text = text is not None
has_tokenized_text = tokenized_text is not None
assert has_text or has_tokenized_text, "Text or tokenized text must be provided"
self.audio = audio
self.text = text
self.tokenized_text = tokenized_text
self.speech_zeroemb_idx = speech_zeroemb_idx
self.text_zeroemb_idx = text_zeroemb_idx
self.text_segment_size = text_segment_size
self.audio_segment_size = audio_segment_size
self.tokenizer = tokenizer
self.group_size = group_size
self.audio_channels = audio_channels
def to_input_id(
self,
tokenizer,
group_size: int,
audio_channels: int = 8,
):
if self.tokenized_text is None:
tokenized_text = tokenizer(
self.text,
return_tensors="pt",
truncation=True,
max_length=999999,
padding=False,
add_special_tokens=False,
)["input_ids"].int() # [1, seqlen]
else:
tokenized_text = self.tokenized_text.unsqueeze(0)
tokenized_text = tokenized_text.squeeze(0)
text_segments = tokenized_text.split(self.text_segment_size, dim=0)
audio_segments = self.audio.split(self.audio_segment_size*group_size*audio_channels, dim=0)
tokenized_segments = []
tokenized_segments.append(
InputSegment(
text='<|sostm|>',
speech_zeroemb_idx=self.speech_zeroemb_idx,
text_zeroemb_idx=self.text_zeroemb_idx,
),
)
eot_tokens = tokenizer(
"<|eot|>",
return_tensors="pt",
truncation=True,
max_length=999999,
padding=False,
add_special_tokens=False,
)["input_ids"][0].to(text_segments[-1])
text_segments = text_segments[:-1] + (torch.cat([text_segments[-1], eot_tokens], dim=0),)
length = min(len(text_segments), len(audio_segments))
for i in range(length):
text_segment = text_segments[i]
audio_segment = audio_segments[i]
tokenized_segments.append(
InputSegment(
tokenized_text=text_segment,
speech_zeroemb_idx=self.speech_zeroemb_idx,
text_zeroemb_idx=self.text_zeroemb_idx,
),
)
tokenized_segments.append(
InputSegment(
audio=audio_segment,
add_sosp_eosp=False,
speech_zeroemb_idx=self.speech_zeroemb_idx,
text_zeroemb_idx=self.text_zeroemb_idx,
),
)
for j in range(length, len(text_segments)):
tokenized_segments.append(
InputSegment(
tokenized_text=text_segments[j],
speech_zeroemb_idx=self.speech_zeroemb_idx,
text_zeroemb_idx=self.text_zeroemb_idx,
),
)
for j in range(length, len(audio_segments)):
tokenized_segments.append(
InputSegment(
audio=audio_segments[j],
add_sosp_eosp=False,
speech_zeroemb_idx=self.speech_zeroemb_idx,
text_zeroemb_idx=self.text_zeroemb_idx,
),
)
tokenized_segments.append(
InputSegment(
text="<|eostm|>",
speech_zeroemb_idx=self.speech_zeroemb_idx,
text_zeroemb_idx=self.text_zeroemb_idx,
),
)
input_ids = [
seg.to_input_id(
self.tokenizer,
self.group_size,
self.audio_channels,
)
for seg in tokenized_segments
]
input_ids = torch.cat(input_ids, dim=1).type(torch.int64) # [n_rvq + 1, seqlen]
return input_ids
|