File size: 9,440 Bytes
aa79b9c | 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 | #!/usr/bin/env python3
# Copyright 2026 Xiaomi Corp. (authors: 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.
"""Training sample processor for OmniVoice.
Converts raw audio/text samples into model-ready tensors: applies prompt/mask
tokenization, randomly drops conditioning, and injects language/instruct tokens.
Used by ``omnivoice.training.builder`` to build the data pipeline.
Contains two processor classes:
- ``OmniVoiceSampleProcessor``: Full processor used for training.
- ``OmniVoiceSimpleSampleProcessor``: Simplified processor (not used for training).
"""
import random
from typing import Any, Dict
import torch
class OmniVoiceSampleProcessor:
"""
Handles the logic of processing a raw sample into tensors
(masking, tokenization, etc.).
"""
def __init__(
self,
text_tokenizer: Any,
num_channels: int,
audio_mask_id: int,
prompt_ratio_range: tuple,
mask_ratio_range: tuple,
drop_cond_ratio: float,
language_ratio: float,
use_pinyin_ratio: float,
instruct_ratio: float,
only_instruct_ratio: float,
):
self.text_tokenizer = text_tokenizer
self.num_channels = num_channels
self.audio_mask_id = audio_mask_id
self.prompt_ratio_range = prompt_ratio_range
self.mask_ratio_range = mask_ratio_range
self.drop_cond_ratio = drop_cond_ratio
self.language_ratio = language_ratio
self.use_pinyin_ratio = use_pinyin_ratio
self.instruct_ratio = instruct_ratio
self.only_instruct_ratio = only_instruct_ratio
def __call__(self, sample: Dict[str, Any]) -> Dict[str, Any]:
# clean_start_token_idx is only used for prompt denoising training,
# where the prompt region is augmented with noises and the model
# needs to learn to recover the clean prompt.
# clean_start_token_idx indicates the start index of the clean generated token.
if "clean_start_token_idx" in sample["label"]:
drop_cond = False
else:
drop_cond = random.uniform(0, 1) < self.drop_cond_ratio
if drop_cond:
prompt_ratio = 0.0
drop_text = True
use_language = False
use_instruct = False
else:
prompt_ratio = random.uniform(*self.prompt_ratio_range)
drop_text = False
use_language = random.uniform(0, 1) < self.language_ratio
use_instruct = random.uniform(0, 1) < self.instruct_ratio
if use_instruct and random.uniform(0, 1) < self.only_instruct_ratio:
prompt_ratio = 0.0
mask_ratio = random.uniform(*self.mask_ratio_range)
# --- Style ---
style = ""
if use_language:
language = sample["label"].get("language_id", "None")
else:
language = "None"
if use_instruct:
instruct = sample["label"].get("instruct", "None")
else:
instruct = "None"
if "clean_start_token_idx" in sample["label"]:
style += "<|denoise|>"
style += f"<|lang_start|>{language}<|lang_end|>"
style += f"<|instruct_start|>{instruct}<|instruct_end|>"
style_inputs = self.text_tokenizer(style, return_tensors="pt").input_ids.repeat(
self.num_channels, 1
)
style_labels = torch.full(
style_inputs.shape, -100
) # Style prompt does not compute loss
# --- Text ---
if (
"text_pinyin" in sample["label"]
and random.uniform(0, 1) < self.use_pinyin_ratio
):
text = sample["label"]["text_pinyin"]
else:
text = sample["label"]["text"]
text_inputs = self.text_tokenizer(
f"<|text_start|>{text}<|text_end|>", return_tensors="pt"
).input_ids.repeat(self.num_channels, 1)
text_labels = torch.full(text_inputs.shape, -100) # Text does not compute loss
# --- Audio ---
audio_tokens = sample["audio_tokens"].long()
# Masking Logic
if "clean_start_token_idx" in sample["label"]:
prompt_length = sample["label"]["clean_start_token_idx"]
else:
prompt_length = int(audio_tokens.shape[1] * prompt_ratio)
audio_inputs = audio_tokens.clone()
audio_labels = audio_tokens.clone()
# Apply masking
maskable_region = audio_tokens[:, prompt_length:]
token_mask = torch.rand(maskable_region.shape) < mask_ratio
audio_inputs[:, prompt_length:][token_mask] = self.audio_mask_id
audio_labels[:, prompt_length:][
~token_mask
] = -100 # Only compute loss on masked tokens
if not drop_cond:
audio_labels[:, :prompt_length] = -100 # No loss on prompt region
# --- Concatenation ---
if drop_text:
input_ids = audio_inputs
labels = audio_labels
total_length = input_ids.shape[1]
audio_mask = torch.ones(total_length, dtype=torch.bool)
else:
input_ids = torch.cat([style_inputs, text_inputs, audio_inputs], dim=1)
labels = torch.cat([style_labels, text_labels, audio_labels], dim=1)
total_length = input_ids.shape[1]
audio_start_idx = style_inputs.shape[1] + text_inputs.shape[1]
audio_mask = torch.zeros(total_length, dtype=torch.bool)
audio_mask[audio_start_idx:] = True
return_dict = {
"input_ids": input_ids, # [C, L]
"labels": labels, # [C, L]
"audio_mask": audio_mask, # [L]
"length": total_length,
}
return return_dict
class OmniVoiceSimpleSampleProcessor:
"""
Handles the logic of processing a raw sample into tensors
(masking, tokenization, etc.).
This is a simpler version that does not include language, instructions,
or denoising prompts.
We do not use it for training as OmniVoiceSampleProcessor can cover this case.
We keep it as a reference implementation for users to understand the basic logics.
"""
def __init__(
self,
text_tokenizer: Any,
num_channels: int,
audio_mask_id: int,
prompt_ratio_range: tuple,
mask_ratio_range: tuple,
drop_cond_ratio: float,
):
self.text_tokenizer = text_tokenizer
self.num_channels = num_channels
self.audio_mask_id = audio_mask_id
self.prompt_ratio_range = prompt_ratio_range
self.mask_ratio_range = mask_ratio_range
self.drop_cond_ratio = drop_cond_ratio
def __call__(self, sample: Dict[str, Any]) -> Dict[str, Any]:
drop_cond = random.uniform(0, 1) < self.drop_cond_ratio
mask_ratio = random.uniform(*self.mask_ratio_range)
if drop_cond:
prompt_ratio = 0.0
else:
prompt_ratio = random.uniform(*self.prompt_ratio_range)
# --- Text ---
text = sample["label"]["text"]
text_inputs = self.text_tokenizer(
f"<|text_start|>{text}<|text_end|>", return_tensors="pt"
).input_ids.repeat(self.num_channels, 1)
text_labels = torch.full(text_inputs.shape, -100) # Text does not compute loss
# --- Audio ---
audio_tokens = sample["audio_tokens"].long()
# Masking Logic
prompt_length = int(audio_tokens.shape[1] * prompt_ratio)
audio_inputs = audio_tokens.clone()
audio_labels = audio_tokens.clone()
# Apply masking
maskable_region = audio_tokens[:, prompt_length:]
token_mask = torch.rand(maskable_region.shape) < mask_ratio
audio_inputs[:, prompt_length:][token_mask] = self.audio_mask_id
audio_labels[:, prompt_length:][
~token_mask
] = -100 # Only compute loss on masked tokens
if not drop_cond:
# No loss on prompt region
audio_labels[:, :prompt_length] = -100
# --- Concatenation ---
if drop_cond:
input_ids = audio_inputs
labels = audio_labels
total_length = input_ids.shape[1]
audio_mask = torch.ones(total_length, dtype=torch.bool)
else:
input_ids = torch.cat([text_inputs, audio_inputs], dim=1)
labels = torch.cat([text_labels, audio_labels], dim=1)
total_length = input_ids.shape[1]
audio_start_idx = text_inputs.shape[1]
audio_mask = torch.zeros(total_length, dtype=torch.bool)
audio_mask[audio_start_idx:] = True
return_dict = {
"input_ids": input_ids, # [C, L]
"labels": labels, # [C, L]
"audio_mask": audio_mask, # [L]
"length": total_length,
}
return return_dict
|