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0844c4a | 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 | """Processor for MOSS-Transcribe-Diarize audio-text inference."""
from __future__ import annotations
from typing import Optional, Union
import numpy as np
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
AUDIO_PAD_TOKEN = "<|audio_pad|>"
AUDIO_START_TOKEN = "<|audio_start|>"
AUDIO_END_TOKEN = "<|audio_end|>"
WHISPER_ENCODER_STRIDE = 2
class MossTranscribeDiarizeProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"common_kwargs": {
"return_tensors": "pt",
},
"audio_kwargs": {},
}
def _audio_to_numpy(audio: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
if torch.is_tensor(audio):
audio = audio.detach().cpu().numpy()
audio = np.asarray(audio, dtype=np.float32)
if audio.ndim > 1:
audio = np.squeeze(audio)
if audio.ndim == 0:
audio = audio.reshape(1)
if audio.ndim != 1:
raise ValueError(f"Expected mono audio with shape (num_samples,), got shape {audio.shape}.")
if audio.shape[0] == 0:
raise ValueError("Audio must contain at least one sample.")
return audio
def _pad_or_trim_audio(audio: np.ndarray, length: int) -> np.ndarray:
if audio.shape[0] > length:
audio = audio[:length]
elif audio.shape[0] < length:
audio = np.pad(audio, (0, length - audio.shape[0]))
return audio.astype(np.float32, copy=False)
def _compute_audio_token_length(num_samples: int, feature_extractor, audio_merge_size: int) -> int:
stride = int(feature_extractor.hop_length) * WHISPER_ENCODER_STRIDE * int(audio_merge_size)
return (int(num_samples) - 1) // stride + 1
def _chunk_audio(
feature_extractor,
audio: Union[np.ndarray, torch.Tensor],
audio_merge_size: int,
) -> tuple[np.ndarray, list[int]]:
audio = _audio_to_numpy(audio)
n_samples = int(feature_extractor.n_samples)
chunks, token_lengths = [], []
for start in range(0, audio.shape[0], n_samples):
chunk = audio[start : start + n_samples]
token_lengths.append(_compute_audio_token_length(chunk.shape[0], feature_extractor, audio_merge_size))
chunks.append(_pad_or_trim_audio(chunk, n_samples))
return np.stack(chunks), token_lengths
def _audios_to_input_features(
feature_extractor,
audios: list[Union[np.ndarray, torch.Tensor]],
*,
audio_merge_size: int,
feature_extractor_kwargs: Optional[dict] = None,
) -> tuple[torch.Tensor, torch.LongTensor, torch.LongTensor]:
feature_batches, feature_lengths, chunk_mapping = [], [], []
feature_extractor_kwargs = dict(feature_extractor_kwargs or {})
feature_extractor_kwargs.update(
{
"sampling_rate": int(feature_extractor.sampling_rate),
"padding": "max_length",
"return_tensors": "pt",
}
)
for audio_idx, audio in enumerate(audios):
chunks, token_lengths = _chunk_audio(feature_extractor, audio, audio_merge_size)
features = feature_extractor(
list(chunks),
**feature_extractor_kwargs,
)["input_features"]
feature_batches.append(features)
feature_lengths.extend(token_lengths)
chunk_mapping.extend([audio_idx] * len(token_lengths))
if feature_batches:
input_features = torch.cat(feature_batches, dim=0)
else:
input_features = torch.empty(
(0, int(feature_extractor.feature_size), int(feature_extractor.nb_max_frames)),
)
length_device = input_features.device
return (
input_features,
torch.tensor(feature_lengths, dtype=torch.long, device=length_device),
torch.tensor(chunk_mapping, dtype=torch.long, device=length_device),
)
class MossTranscribeDiarizeProcessor(ProcessorMixin):
"""Build MOSS-Transcribe-Diarize model inputs from text prompts and raw waveforms.
The model consumes log-mel ``input_features``. This processor owns the raw
waveform preprocessing, audio placeholder expansion, and optional numeric
time anchors inside the audio span.
"""
attributes = ["feature_extractor", "tokenizer"]
feature_extractor_class = "AutoFeatureExtractor"
tokenizer_class = "AutoTokenizer"
model_input_names = [
"input_ids",
"attention_mask",
"input_features",
"audio_feature_lengths",
"audio_chunk_mapping",
]
def __init__(
self,
feature_extractor=None,
tokenizer=None,
audio_tokens_per_second: float = 12.5,
audio_merge_size: int = 4,
time_marker_every_seconds: int = 2,
enable_time_marker: bool = True,
chat_template: Optional[str] = None,
):
if feature_extractor is None:
raise ValueError("MossTranscribeDiarizeProcessor requires a feature_extractor.")
if tokenizer is None:
raise ValueError("MossTranscribeDiarizeProcessor requires a tokenizer.")
super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
self.audio_tokens_per_second = audio_tokens_per_second
self.audio_merge_size = int(audio_merge_size)
self.time_marker_every_seconds = time_marker_every_seconds
self.enable_time_marker = enable_time_marker
self.audio_token = AUDIO_PAD_TOKEN if not hasattr(tokenizer, "audio_token") else tokenizer.audio_token
self.audio_start_token = (
AUDIO_START_TOKEN if not hasattr(tokenizer, "audio_start_token") else tokenizer.audio_start_token
)
self.audio_end_token = AUDIO_END_TOKEN if not hasattr(tokenizer, "audio_end_token") else tokenizer.audio_end_token
resolved_audio_token_id = tokenizer.convert_tokens_to_ids(self.audio_token)
if resolved_audio_token_id is None:
raise ValueError(f"Tokenizer is missing required audio placeholder token {self.audio_token!r}.")
self.audio_token_id = int(resolved_audio_token_id)
self.digit_token_ids = self._get_digit_token_ids()
def _get_digit_token_ids(self) -> dict[str, int]:
digit_token_ids = {}
for digit in "0123456789":
ids = self.tokenizer.encode(digit, add_special_tokens=False)
if len(ids) != 1:
raise ValueError(f"Digit {digit!r} is not a single token: {ids}")
digit_token_ids[digit] = int(ids[0])
return digit_token_ids
def _audio_span_ids(self, audio_seq_len: int) -> list[int]:
audio_seq_len = int(audio_seq_len)
if not self.enable_time_marker or audio_seq_len <= 0 or self.time_marker_every_seconds <= 0:
return [self.audio_token_id] * max(audio_seq_len, 0)
tokens_per_marker = int(self.audio_tokens_per_second * self.time_marker_every_seconds)
if tokens_per_marker <= 0:
return [self.audio_token_id] * audio_seq_len
duration = audio_seq_len / float(self.audio_tokens_per_second)
output, consumed = [], 0
for sec in range(self.time_marker_every_seconds, int(duration) + 1, self.time_marker_every_seconds):
pos = (sec // self.time_marker_every_seconds) * tokens_per_marker
segment_len = pos - consumed
if segment_len > 0:
output.extend([self.audio_token_id] * segment_len)
consumed += segment_len
marker_ids = [self.digit_token_ids[digit] for digit in str(sec)]
output.extend(marker_ids)
remainder = audio_seq_len - consumed
if remainder > 0:
output.extend([self.audio_token_id] * remainder)
return output
def _expand_audio_token(self, text: str, num_audio_tokens: int, max_length: int) -> list[int]:
audio_ids = self._audio_span_ids(num_audio_tokens)
audio_token_count = text.count(self.audio_token)
if audio_token_count != 1:
raise ValueError(
f"Expected exactly one {self.audio_token!r} token per text sample, got {audio_token_count}."
)
before_audio, after_audio = text.split(self.audio_token, maxsplit=1)
before_ids = self.tokenizer.encode(before_audio, add_special_tokens=False)
after_ids = self.tokenizer.encode(after_audio, add_special_tokens=False)
input_ids = before_ids + audio_ids + after_ids
if len(input_ids) > max_length:
raise ValueError(f"Prompt/audio sequence exceeds max_length={max_length}")
return input_ids
def __call__(
self,
text: Union[str, list[str]],
audio,
*,
max_length: int = 131072,
**kwargs: Unpack[MossTranscribeDiarizeProcessorKwargs],
) -> BatchFeature:
return_tensors = kwargs.pop("return_tensors", "pt")
output_kwargs = self._merge_kwargs(
MossTranscribeDiarizeProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if isinstance(text, str):
texts = [text]
else:
texts = list(text)
audios = audio if isinstance(audio, list) else [audio]
if len(texts) != len(audios):
raise ValueError(f"Expected one audio per text prompt, got {len(audios)} audios and {len(texts)} prompts.")
input_features, audio_feature_lengths, audio_chunk_mapping = _audios_to_input_features(
self.feature_extractor,
audios,
audio_merge_size=self.audio_merge_size,
feature_extractor_kwargs=output_kwargs["audio_kwargs"],
)
audio_token_counts = torch.zeros(len(audios), dtype=torch.long, device=audio_feature_lengths.device)
audio_token_counts.scatter_add_(0, audio_chunk_mapping, audio_feature_lengths)
encoded = [
self._expand_audio_token(prompt, int(num_audio_tokens.item()), max_length)
for prompt, num_audio_tokens in zip(texts, audio_token_counts)
]
max_seq_len = max(len(ids) for ids in encoded)
pad_token_id = self.tokenizer.pad_token_id
if pad_token_id is None:
pad_token_id = self.tokenizer.eos_token_id or 0
input_ids, attention_mask = [], []
for ids in encoded:
pad_len = max_seq_len - len(ids)
input_ids.append(ids + [pad_token_id] * pad_len)
attention_mask.append([1] * len(ids) + [0] * pad_len)
target_device = input_features.device
data = {
"input_ids": torch.tensor(input_ids, dtype=torch.long, device=target_device),
"attention_mask": torch.tensor(attention_mask, dtype=torch.long, device=target_device),
"input_features": input_features,
"audio_feature_lengths": audio_feature_lengths,
"audio_chunk_mapping": audio_chunk_mapping,
}
return BatchFeature(data=data, tensor_type=return_tensors)
__all__ = ["MossTranscribeDiarizeProcessor"]
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