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a8ef2f8 f470c42 a8ef2f8 f470c42 a8ef2f8 f470c42 a8ef2f8 f470c42 a8ef2f8 f470c42 a8ef2f8 | 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 | from pathlib import Path
import torch
import torchaudio
from torchaudio.compliance.kaldi import fbank
from transformers import ProcessorMixin
from transformers.feature_extraction_utils import BatchFeature
class SalmonnProcessor(ProcessorMixin):
"""Prepare text, audio, and contextual examples for SALMONN-2 inference."""
attributes = ["tokenizer"]
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
audio_placeholder = "<audio>"
model_audio_placeholder = "<|vision_start|><|vision_end|>"
def __init__(self, tokenizer, sample_rate=16000, num_mel_bins=128, chat_template=None):
self.sample_rate = sample_rate
self.num_mel_bins = num_mel_bins
super().__init__(tokenizer, chat_template=chat_template)
@property
def model_input_names(self):
return ["input_ids", "attention_mask", "audio_features", "audio_lengths", "audio_counts"]
def prepare_model(self, model):
"""Register tokenizer-dependent timestamp tokens on a loaded SALMONN-2 model."""
if getattr(model.config, "inject_temporal_embedding_nl", False):
model.register_nl_timestamp_tokenizer(self.tokenizer)
return model
def build_prompt(self, instruction, audio_count=1, context=None):
"""Build the model prompt before chat templating.
Context items may be strings for text-only contextual words or dictionaries
containing ``text`` and, optionally, ``audio``. A context list must be
consistently text-only or audio-text paired.
"""
if not isinstance(instruction, str) or not instruction.strip():
raise ValueError("instruction must be a non-empty string")
if self.audio_placeholder in instruction:
raise ValueError("instruction must not contain the reserved <audio> placeholder")
if not isinstance(audio_count, int) or audio_count < 1:
raise ValueError("audio_count must be a positive integer")
prompt = self.audio_placeholder * audio_count + instruction.strip()
contexts = self._normalize_context(context)
if not contexts:
return prompt
has_audio = [item["audio"] is not None for item in contexts]
if any(has_audio) and not all(has_audio):
raise ValueError("context items must either all include audio or all be text-only")
if all(has_audio):
lines = [
prompt,
"Use the following contextual words and their pronunciations as references while transcribing the speech:",
"<biasing_list>",
*(f'{self.audio_placeholder}{item["text"]}' for item in contexts),
"</biasing_list>.",
]
else:
words = ", ".join(item["text"] for item in contexts)
lines = [
prompt,
"Pay extra attention to the following contextual words:",
"<biasing_list>",
f"[{words}]",
"</biasing_list>.",
]
return "\n".join(lines)
def __call__(
self,
audios,
instruction=None,
context=None,
formatted_prompt=None,
sampling_rate=None,
enable_thinking=False,
return_tensors="pt",
**tokenizer_kwargs,
):
"""Create a model-ready batch for one inference request.
Args:
audios: A path, waveform, or list of primary audio inputs.
instruction: The user instruction associated with the primary audio. Use
either ``instruction`` or ``formatted_prompt``, but not both.
context: Optional contextual words. Each item is either a string or a
``{"text": ..., "audio": ...}`` dictionary.
formatted_prompt: An advanced prompt containing one ``<audio>`` marker
per input audio. This preserves custom audio placement and cannot be
combined with ``instruction`` or ``context``.
sampling_rate: Sampling rate for raw waveform inputs. Audio paths and
``{"array": ..., "sampling_rate": ...}`` inputs carry their own rate.
enable_thinking: Whether to enable Qwen's thinking prompt.
return_tensors: Currently only ``"pt"`` is supported.
"""
if return_tensors != "pt":
raise ValueError('SalmonnProcessor currently supports return_tensors="pt" only')
primary_audios = self._as_audio_list(audios)
if (instruction is None) == (formatted_prompt is None):
raise ValueError("provide exactly one of instruction or formatted_prompt")
if formatted_prompt is not None:
if context is not None:
raise ValueError("context cannot be combined with formatted_prompt")
if not isinstance(formatted_prompt, str) or not formatted_prompt.strip():
raise ValueError("formatted_prompt must be a non-empty string")
if self.model_audio_placeholder in formatted_prompt:
raise ValueError("formatted_prompt must use <audio>, not internal model audio markers")
prompt = formatted_prompt.strip()
ordered_audios = primary_audios
else:
contexts = self._normalize_context(context)
context_audios = [item["audio"] for item in contexts if item["audio"] is not None]
ordered_audios = primary_audios + context_audios
prompt = self.build_prompt(instruction, len(primary_audios), contexts)
expected_placeholders = len(ordered_audios)
if prompt.count(self.audio_placeholder) != expected_placeholders:
raise ValueError(
f"Prompt contains {prompt.count(self.audio_placeholder)} audio placeholders "
f"but received {expected_placeholders} audio inputs"
)
rendered = self.tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
tokenize=False,
add_generation_prompt=True,
enable_thinking=enable_thinking,
).replace(self.audio_placeholder, self.model_audio_placeholder)
tokenizer_kwargs.setdefault("add_special_tokens", False)
encoded = self.tokenizer(rendered, return_tensors="pt", **tokenizer_kwargs)
features = [self._extract_fbank(audio, sampling_rate) for audio in ordered_audios]
audio_lengths = torch.tensor([item.size(0) for item in features], dtype=torch.long)
audio_features = torch.nn.utils.rnn.pad_sequence(features, batch_first=True)
audio_counts = torch.tensor([len(ordered_audios)], dtype=torch.long)
return BatchFeature(
data={
**dict(encoded),
"audio_features": audio_features,
"audio_lengths": audio_lengths,
"audio_counts": audio_counts,
}
)
def decode(self, token_ids, clean_response=True, **kwargs):
kwargs.setdefault("skip_special_tokens", True)
text = self.tokenizer.decode(token_ids, **kwargs)
return self._clean_response(text) if clean_response else text
def batch_decode(self, sequences, clean_response=True, **kwargs):
kwargs.setdefault("skip_special_tokens", True)
texts = self.tokenizer.batch_decode(sequences, **kwargs)
if clean_response:
return [self._clean_response(text) for text in texts]
return texts
def _extract_fbank(self, audio, sampling_rate=None):
waveform, source_rate = self._load_audio(audio, sampling_rate)
if source_rate != self.sample_rate:
waveform = torchaudio.functional.resample(waveform, source_rate, self.sample_rate)
return fbank(
waveform.unsqueeze(0),
sample_frequency=self.sample_rate,
num_mel_bins=self.num_mel_bins,
low_freq=20.0,
high_freq=-400.0,
dither=0.0,
snip_edges=False,
energy_floor=1e-10,
).to(torch.float32)
def _load_audio(self, audio, sampling_rate=None):
if isinstance(audio, (str, Path)):
waveform, source_rate = torchaudio.load(str(Path(audio).expanduser()))
elif isinstance(audio, dict):
if "array" not in audio or "sampling_rate" not in audio:
raise ValueError("audio dictionaries require 'array' and 'sampling_rate' entries")
waveform = torch.as_tensor(audio["array"])
source_rate = audio["sampling_rate"]
elif isinstance(audio, tuple) and len(audio) == 2:
waveform = torch.as_tensor(audio[0])
source_rate = audio[1]
else:
if sampling_rate is None:
raise ValueError("sampling_rate is required for raw waveform inputs")
waveform = torch.as_tensor(audio)
source_rate = sampling_rate
if waveform.ndim == 2:
waveform = waveform.mean(dim=0)
elif waveform.ndim != 1:
raise ValueError("audio waveforms must have shape (samples,) or (channels, samples)")
if waveform.numel() == 0:
raise ValueError("audio waveforms must not be empty")
if not isinstance(source_rate, int) or source_rate <= 0:
raise ValueError("sampling_rate must be a positive integer")
return waveform.to(torch.float32).cpu(), source_rate
def _as_audio_list(self, audios):
if isinstance(audios, list):
if not audios:
raise ValueError("audios must contain at least one audio input")
return audios
return [audios]
def _normalize_context(self, context):
if context is None:
return []
if not isinstance(context, (list, tuple)):
raise TypeError("context must be a list of strings or dictionaries")
normalized = []
for item in context:
if isinstance(item, str):
text, audio = item, None
elif isinstance(item, dict):
text, audio = item.get("text"), item.get("audio")
else:
raise TypeError("each context item must be a string or dictionary")
if not isinstance(text, str) or not text.strip():
raise ValueError("each context item requires non-empty text")
if self.audio_placeholder in text:
raise ValueError("context text must not contain the reserved <audio> placeholder")
normalized.append({"text": text.strip(), "audio": audio})
return normalized
@staticmethod
def _clean_response(text):
return text.replace("<think>", "").replace("</think>", "").strip()
__all__ = ["SalmonnProcessor"]
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