File size: 11,185 Bytes
6aa989f | 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 | from __future__ import annotations
import inspect
import json
import os
import re
from pathlib import Path
from typing import Any, Sequence
import numpy as np
import torch
from transformers import AutoTokenizer
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessorMixin
def _clean_text(text: str) -> str:
return " ".join(str(text).strip().split())
def _as_list(value: Any, batch_size: int, name: str) -> list[Any]:
if isinstance(value, (str, Path)) or value is None or np.isscalar(value):
return [value] * batch_size
if isinstance(value, torch.Tensor) and value.ndim <= 2:
return [value] if batch_size == 1 else list(value)
if isinstance(value, np.ndarray) and value.ndim <= 2:
return [value] if batch_size == 1 else list(value)
values = list(value)
if len(values) != batch_size:
raise ValueError(f"{name} must contain {batch_size} items, got {len(values)}")
return values
def _pad_1d(rows: list[torch.Tensor], pad_value: int) -> tuple[torch.Tensor, torch.Tensor]:
max_len = max((row.numel() for row in rows), default=0)
values = torch.full((len(rows), max_len), pad_value, dtype=torch.long)
mask = torch.zeros((len(rows), max_len), dtype=torch.long)
for idx, row in enumerate(rows):
length = row.numel()
values[idx, :length] = row
mask[idx, :length] = 1
return values, mask
class ArkttsProcessor(ProcessorMixin):
attributes = ["tokenizer"]
tokenizer_class = ("PreTrainedTokenizerFast", "PreTrainedTokenizer")
valid_kwargs = ["num_codebooks", "semantic_begin_id", "audio_sampling_rate"]
def __init__(
self,
tokenizer,
num_codebooks: int = 10,
semantic_begin_id: int = 151678,
audio_sampling_rate: int = 44100,
**kwargs,
):
super().__init__(tokenizer)
self.num_codebooks = int(num_codebooks)
self.semantic_begin_id = int(semantic_begin_id)
self.audio_sampling_rate = int(audio_sampling_rate)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "ArkttsProcessor":
trust_remote_code = bool(kwargs.pop("trust_remote_code", False))
shared_names = {
"cache_dir", "force_download", "local_files_only", "token", "revision", "subfolder"
}
shared = {key: kwargs[key] for key in list(kwargs) if key in shared_names}
config = {}
local_config = os.path.join(str(pretrained_model_name_or_path), "processor_config.json")
if os.path.isfile(local_config):
with open(local_config, "r", encoding="utf-8") as handle:
config = json.load(handle)
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
use_fast=True,
trust_remote_code=trust_remote_code,
fix_mistral_regex=False,
**shared,
)
return cls(
tokenizer=tokenizer,
num_codebooks=config.get("num_codebooks", 10),
semantic_begin_id=config.get("semantic_begin_id", 151678),
audio_sampling_rate=config.get("audio_sampling_rate", 44100),
)
def _encode(self, text: str) -> torch.Tensor:
encode_kwargs = {"add_special_tokens": False}
if "allowed_special" in inspect.signature(self.tokenizer.encode).parameters:
encode_kwargs["allowed_special"] = "all"
return torch.tensor(self.tokenizer.encode(text, **encode_kwargs), dtype=torch.long)
@staticmethod
def _format_reference_text(text: str) -> str:
cleaned = _clean_text(text)
if re.search(r"<\|speaker:\d+\|>", cleaned):
return cleaned
return f"<|speaker:0|>{cleaned}"
def _prompt_segments(self, text: str, reference_text: str | None, has_reference: bool):
target = _clean_text(text)
if not target:
raise ValueError("text must not be empty")
def encode_parts(parts: list[str]) -> torch.Tensor:
return torch.cat([self._encode(part) for part in parts])
if not has_reference:
full = encode_parts([
"<|im_start|>system\n",
"convert the provided text to speech",
"<|im_end|>\n",
"<|im_start|>user\n",
target,
"<|im_end|>\n",
"<|im_start|>assistant\n<|voice|>",
])
return full, self._encode("")
if not reference_text:
raise ValueError("reference_text is required when a reference voice is provided")
prefix = encode_parts([
"<|im_start|>system\n",
"convert the provided text to speech reference to the following:\n\nText:\n",
self._format_reference_text(reference_text),
"\n\nSpeech:\n",
])
suffix = encode_parts([
"<|im_end|>\n",
"<|im_start|>user\n",
target,
"<|im_end|>\n",
"<|im_start|>assistant\n<|voice|>",
])
return prefix, suffix
def _load_audio(self, value: Any, sampling_rate: int | None) -> torch.Tensor:
source_rate = sampling_rate
if isinstance(value, (str, Path)):
try:
import soundfile as sf
except ImportError as exc:
raise ImportError("soundfile is required for reference audio paths") from exc
array, source_rate = sf.read(str(value), dtype="float32", always_2d=True)
array = array.mean(axis=1)
audio = torch.from_numpy(np.asarray(array, dtype=np.float32))
else:
if isinstance(value, dict):
source_rate = value.get("sampling_rate", source_rate)
value = value.get("array")
if isinstance(value, (tuple, list)) and len(value) == 2 and np.isscalar(value[1]):
value, source_rate = value
audio = torch.as_tensor(value, dtype=torch.float32)
if audio.ndim == 2:
audio = audio.mean(dim=0)
if audio.ndim != 1:
raise ValueError(f"reference audio must be mono or channels-first, got {tuple(audio.shape)}")
if audio.numel() == 0:
raise ValueError("reference audio must not be empty")
if source_rate is None:
raise ValueError("sampling_rate is required for reference audio arrays")
if int(source_rate) != self.audio_sampling_rate:
try:
from torchaudio.functional import resample
except ImportError as exc:
raise ImportError("torchaudio is required to resample reference audio") from exc
audio = resample(audio, int(source_rate), self.audio_sampling_rate)
return audio.contiguous()
def __call__(
self,
text: str | Sequence[str],
reference_text: str | Sequence[str] | None = None,
reference_audio: Any = None,
reference_codes: Any = None,
sampling_rate: int | Sequence[int] | None = None,
return_tensors: str = "pt",
**kwargs,
) -> BatchFeature:
if kwargs:
raise TypeError(f"Unexpected processor arguments: {sorted(kwargs)}")
if return_tensors != "pt":
raise ValueError("ArkttsProcessor currently supports return_tensors='pt' only")
texts = [text] if isinstance(text, str) else list(text)
if not texts:
raise ValueError("text batch must not be empty")
batch_size = len(texts)
ref_texts = _as_list(reference_text, batch_size, "reference_text")
if reference_audio is not None and reference_codes is not None:
raise ValueError("Provide reference_audio or reference_codes, not both")
has_reference = reference_audio is not None or reference_codes is not None
prefix_rows, suffix_rows = zip(*[
self._prompt_segments(item, ref_texts[idx], has_reference)
for idx, item in enumerate(texts)
])
prefix_ids, prefix_mask = _pad_1d(list(prefix_rows), self.tokenizer.pad_token_id)
suffix_ids, suffix_mask = _pad_1d(list(suffix_rows), self.tokenizer.pad_token_id)
data: dict[str, torch.Tensor] = {
"prefix_input_ids": prefix_ids,
"prefix_attention_mask": prefix_mask,
"suffix_input_ids": suffix_ids,
"suffix_attention_mask": suffix_mask,
}
if reference_codes is not None:
code_items = _as_list(reference_codes, batch_size, "reference_codes")
loaded = []
for item in code_items:
if isinstance(item, (str, Path)):
item = np.load(str(item))
codes = torch.as_tensor(item, dtype=torch.long)
if codes.ndim != 2 or codes.shape[0] != self.num_codebooks or codes.shape[1] == 0:
raise ValueError(
f"reference codes must have shape [{self.num_codebooks}, T>0], got {tuple(codes.shape)}"
)
if codes.min() < 0 or codes.max() >= 4096:
raise ValueError("reference codes must be in [0, 4095]")
loaded.append(codes)
max_frames = max(item.shape[1] for item in loaded)
padded = torch.full((batch_size, self.num_codebooks, max_frames), -1, dtype=torch.long)
lengths = torch.empty(batch_size, dtype=torch.long)
for idx, codes in enumerate(loaded):
lengths[idx] = codes.shape[1]
padded[idx, :, : codes.shape[1]] = codes
data["reference_codes"] = padded
data["reference_code_lengths"] = lengths
if reference_audio is not None:
audio_items = _as_list(reference_audio, batch_size, "reference_audio")
rate_items = _as_list(sampling_rate, batch_size, "sampling_rate")
loaded_audio = [self._load_audio(item, rate_items[idx]) for idx, item in enumerate(audio_items)]
max_samples = max(item.numel() for item in loaded_audio)
padded_audio = torch.zeros((batch_size, 1, max_samples), dtype=torch.float32)
lengths = torch.empty(batch_size, dtype=torch.long)
for idx, audio in enumerate(loaded_audio):
lengths[idx] = audio.numel()
padded_audio[idx, 0, : audio.numel()] = audio
data["reference_audio_values"] = padded_audio
data["reference_audio_lengths"] = lengths
return BatchFeature(data=data)
@property
def model_input_names(self) -> list[str]:
return [
"prefix_input_ids", "prefix_attention_mask", "suffix_input_ids",
"suffix_attention_mask", "reference_codes", "reference_code_lengths",
"reference_audio_values", "reference_audio_lengths",
]
def batch_decode(self, *args, **kwargs):
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
return self.tokenizer.decode(*args, **kwargs)
__all__ = ["ArkttsProcessor"]
|