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from collections.abc import Mapping
from typing import Dict, List, Optional, Union, cast
import numpy as np
import torch
from transformers import Qwen2Tokenizer, Qwen2TokenizerFast, Wav2Vec2FeatureExtractor
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import (
AllKwargsForChatTemplate,
ProcessingKwargs,
ProcessorMixin,
)
from typing_extensions import Unpack
DEFAULT_SYSTEM_PROMPT = (
"You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, "
"capable of perceiving auditory and visual inputs, as well as generating text and speech."
)
class MiDashengLMSpatialProcessorKwargs(ProcessingKwargs):
_defaults = { # type: ignore
"text_kwargs": {
"padding": True,
"padding_side": "left",
},
"audio_kwargs": {},
}
def calculate_mel_frames_dasheng(
audio_length_samples: int,
n_fft: int = 512,
hop_size: int = 160,
dasheng_subsampling: int = 4,
center=True,
model_subsampling: int = 5,
) -> int:
if center:
audio_length_samples = audio_length_samples + n_fft
return (
int(1 + ((audio_length_samples - n_fft) / hop_size))
// dasheng_subsampling
// model_subsampling
)
class MiDashengLMSpatialProcessor(ProcessorMixin):
attributes = ["feature_extractor", "tokenizer"]
valid_kwargs = [
"chat_template",
"audio_token",
"audio_bos_token",
"audio_eos_token",
"audio_channels",
"sem_n_fft",
"min_audio_duration",
"model_subsampling",
]
feature_extractor_class = "Wav2Vec2FeatureExtractor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(
self,
feature_extractor: Wav2Vec2FeatureExtractor,
tokenizer: Union[Qwen2Tokenizer, Qwen2TokenizerFast],
model_subsampling: int = 5,
audio_channels: int = 2,
sem_n_fft: int = 512,
min_audio_duration: float = 0.2,
chat_template: Optional[Union[str, Dict[str, str]]] = None,
audio_token: Optional[str] = None,
audio_bos_token: Optional[str] = None,
audio_eos_token: Optional[str] = None,
):
assert audio_token is not None or hasattr(tokenizer, "audio_token"), (
"Either `audio_token` must be provided or tokenizer must have `audio_token` attribute."
)
assert audio_bos_token is not None or hasattr(tokenizer, "audio_bos_token"), (
"Either `audio_bos_token` must be provided or tokenizer must have `audio_bos_token` attribute."
)
assert audio_eos_token is not None or hasattr(tokenizer, "audio_eos_token"), (
"Either `audio_eos_token` must be provided or tokenizer must have `audio_eos_token` attribute."
)
if chat_template is None:
chat_template = tokenizer.chat_template
def get_token(token_name: str) -> str:
if not hasattr(tokenizer, token_name):
raise ValueError(
f"Tokenizer does not have attribute `{token_name}`. "
"Please provide it as an argument to the processor."
)
token = getattr(tokenizer, token_name)
if not isinstance(token, str):
raise TypeError(
f"Expected token {token_name} to be a string, but got {type(token)}."
)
return token
self.audio_token = audio_token or get_token("audio_token")
self.audio_bos_token = audio_bos_token or get_token("audio_bos_token")
self.audio_eos_token = audio_eos_token or get_token("audio_eos_token")
self.audio_token_id = cast(int, tokenizer.convert_tokens_to_ids(self.audio_token))
self.model_subsampling = model_subsampling
self.audio_channels = audio_channels
self.sem_n_fft = sem_n_fft
self.min_audio_duration = min_audio_duration
self.sampling_rate = feature_extractor.sampling_rate
super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
self.feature_extractor: Wav2Vec2FeatureExtractor
self.tokenizer: Union[Qwen2Tokenizer, Qwen2TokenizerFast]
self.chat_template: Optional[Union[str, Dict[str, str]]]
def _process_messages_for_chat_template(
self,
conversation,
batch_images,
batch_videos,
batch_video_metadata,
**mm_load_kwargs,
):
if (sr := mm_load_kwargs.get("sampling_rate", None)) is not None:
if sr != self.sampling_rate:
raise ValueError(
f"This model is trained with a sampling rate of {self.sampling_rate}, "
f"but the sampling rate {sr} is used to load audio."
)
return super()._process_messages_for_chat_template(
conversation,
batch_images,
batch_videos,
batch_video_metadata,
**mm_load_kwargs,
)
def _coerce_binaural(self, sample: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
if isinstance(sample, torch.Tensor):
arr = sample.detach().cpu().numpy()
elif isinstance(sample, np.ndarray):
arr = sample
elif isinstance(sample, str):
raise TypeError(
"Expected audio to be a numpy array or torch tensor, but got a string. "
"If you passed audios through `apply_chat_template`, make sure the audio "
"paths are valid URLs (http/https) or existing local files."
)
else:
raise TypeError(
f"Expected audio to be a numpy array, torch tensor, or string, but got {type(sample)}."
)
arr = np.asarray(arr, dtype=np.float32)
C = self.audio_channels
if arr.ndim == 1:
arr = arr[None, :] # (1, T)
elif arr.ndim == 2:
if arr.shape[0] <= C:
pass
elif arr.shape[1] <= C:
arr = arr.T
elif arr.shape[0] > arr.shape[1]:
arr = arr.T
else:
raise ValueError(f"Audio array must be 1D or 2D, got shape {arr.shape}.")
if arr.shape[0] != C:
arr = arr[:1]
if arr.shape[0] == 1 and C > 1:
arr = np.repeat(arr, C, axis=0) # up-mix mono -> C channels
return arr
def _pad_audio_batch(self, audios: List[np.ndarray]):
raw_lengths = [a.shape[-1] for a in audios]
min_t = max(1, int(np.ceil(self.sampling_rate * self.min_audio_duration)))
lengths = [max(length, min_t) for length in raw_lengths]
max_t = max(lengths)
C = audios[0].shape[0]
batch = torch.zeros(len(audios), C, max_t, dtype=torch.float32)
for i, a in enumerate(audios):
batch[i, :, : a.shape[-1]] = torch.from_numpy(a)
return batch, torch.tensor(lengths, dtype=torch.long)
def _load_audio_faithfully(self, src: str) -> np.ndarray:
import torchaudio
wav, sr = torchaudio.load(src) # (C, T), float32
if sr != self.sampling_rate:
wav = torchaudio.functional.resample(wav, sr, self.sampling_rate)
return wav.numpy()
def _preload_audio_in_conversation(self, conversation):
"""Replace `{"type":"audio","path"/"url": ...}` with an in-memory binaural
array (torchaudio) so the base `apply_chat_template` never routes the file
through the mono-forcing `load_audio`. Array inputs are left untouched.
Returns a new conversation; the caller's object is not mutated."""
def fix_message(msg):
content = msg.get("content")
if not isinstance(content, list):
return msg
new_content = []
for part in content:
if (
isinstance(part, dict)
and part.get("type") == "audio"
and "audio" not in part
and ("path" in part or "url" in part)
):
src = part.get("path") or part.get("url")
arr = self._load_audio_faithfully(src)
new_part = {k: v for k, v in part.items() if k not in ("path", "url")}
new_part["audio"] = arr
new_content.append(new_part)
else:
new_content.append(part)
return {**msg, "content": new_content}
if conversation and isinstance(conversation[0], Mapping):
return [fix_message(m) for m in conversation] # single conversation
return [[fix_message(m) for m in conv] for conv in conversation] # batch
def apply_chat_template(
self,
conversation: Union[List[dict], List[List[dict]]],
chat_template: Optional[str] = None,
**kwargs: Unpack[AllKwargsForChatTemplate],
) -> str:
if conversation:
conversation = self._preload_audio_in_conversation(conversation)
if conversation:
first_msgs = (
[conversation[0]]
if isinstance(conversation[0], Mapping)
else [conv[0] for conv in conversation if conv]
)
for first_msg in first_msgs:
if first_msg["role"] != "system":
continue
system_prompt = None
if isinstance(first_msg["content"], str):
system_prompt = first_msg["content"]
elif isinstance(first_msg["content"], list):
for part in first_msg["content"]:
if isinstance(part, dict) and "text" in part:
system_prompt = part["text"]
break
if system_prompt is not None and system_prompt != DEFAULT_SYSTEM_PROMPT:
logging.warning(
"The system prompt has been modified, which may reduce model "
"performance. Prefer the default system prompt by omitting the "
"system role from the input."
)
return super().apply_chat_template(conversation, chat_template, **kwargs)
def __call__(
self,
text: Optional[Union[str, List[str]]] = None,
audio: Optional[Union[List[np.ndarray], List[torch.Tensor]]] = None,
**kwargs: Unpack[MiDashengLMSpatialProcessorKwargs],
) -> BatchFeature:
if text is None:
raise ValueError("You need to specify `text` input to process.")
elif isinstance(text, str):
text = [text]
elif not isinstance(text, list) or not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
if kwargs.get("images", None) is not None or kwargs.get("videos", None) is not None:
raise ValueError("This model does not support images or videos.")
output_kwargs = self._merge_kwargs(
MiDashengLMSpatialProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
audio_inputs: Dict[str, torch.Tensor] = {}
if audio is not None:
binaural = [self._coerce_binaural(sample) for sample in audio]
num_audio_tokens = sum(sample.count(self.audio_token) for sample in text)
num_audios = len(binaural)
if num_audio_tokens != num_audios:
raise ValueError(
f"Found {num_audio_tokens} {self.audio_token} token(s) in provided "
f"text but received {num_audios} audio(s)"
)
audio_tensor, audio_length = self._pad_audio_batch(binaural)
audio_inputs["input_values"] = audio_tensor # (B, C, T)
audio_inputs["audio_length"] = audio_length # (B,)
audio_lengths = audio_length.tolist()
expanded_text = []
for sample in text:
replace_str = []
while self.audio_token in sample:
length = audio_lengths.pop(0)
n_tok = calculate_mel_frames_dasheng(
length, n_fft=self.sem_n_fft, model_subsampling=self.model_subsampling
)
expanded_audio_token = self.audio_token * n_tok
start = sample.find(self.audio_token)
end = start + len(self.audio_token)
has_bos = sample[start - len(self.audio_bos_token) : start] == self.audio_bos_token
has_eos = sample[end : end + len(self.audio_eos_token)] == self.audio_eos_token
if not has_bos and not has_eos:
expanded_audio_token = (
self.audio_bos_token + expanded_audio_token + self.audio_eos_token
)
replace_str.append(expanded_audio_token)
sample = sample.replace(self.audio_token, "<placeholder>", 1)
while "<placeholder>" in sample:
sample = sample.replace("<placeholder>", replace_str.pop(0), 1)
expanded_text.append(sample)
text = expanded_text
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", "pt")
inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
self._check_special_mm_tokens(text, BatchFeature(inputs), modalities=["audio"])
if audio is not None:
inputs.update(audio_inputs)
return BatchFeature(data={**inputs}, tensor_type=return_tensors)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
return list(
dict.fromkeys(tokenizer_input_names + ["input_values", "audio_length"])
)
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