import re import types from dataclasses import dataclass from typing import List, Optional, Sequence, Union import numpy as np import torch from transformers import BatchFeature from transformers.processing_utils import ProcessorMixin from transformers.models.whisper.feature_extraction_whisper import ( WhisperFeatureExtractor, ) @dataclass class MelConfig: mel_sr: int = 16000 mel_dim: int = 128 mel_n_fft: int = 400 mel_hop_length: int = 160 mel_dtype: torch.dtype = torch.bfloat16 use_whisper_feature_extractor: bool = True def _normalize_mel_config(mel_config) -> dict[str, object]: default_config = MelConfig() if mel_config is None: source = {} elif isinstance(mel_config, MelConfig): source = { key: getattr(mel_config, key) for key in MelConfig.__dataclass_fields__.keys() } else: source = dict(mel_config) normalized = {} for key in MelConfig.__dataclass_fields__.keys(): value = source.get(key, getattr(default_config, key)) if key == "mel_dtype": if isinstance(value, torch.dtype): value = str(value).removeprefix("torch.") elif isinstance(value, str) and value.startswith("torch."): value = value.removeprefix("torch.") normalized[key] = value return normalized def _build_mel_config(mel_config_dict: dict[str, object]) -> MelConfig: default_config = MelConfig() def _int_value(key: str, default: int) -> int: value = mel_config_dict.get(key, default) if isinstance(value, bool): return int(value) if isinstance(value, (int, str)): return int(value) return default def _bool_value(key: str, default: bool) -> bool: value = mel_config_dict.get(key, default) if isinstance(value, bool): return value if isinstance(value, str): return value.lower() in {"1", "true", "yes", "on"} if isinstance(value, int): return bool(value) return default mel_dtype_value = mel_config_dict.get("mel_dtype", default_config.mel_dtype) if isinstance(mel_dtype_value, str): mel_dtype = getattr(torch, mel_dtype_value.removeprefix("torch.")) elif isinstance(mel_dtype_value, torch.dtype): mel_dtype = mel_dtype_value else: mel_dtype = default_config.mel_dtype return MelConfig( mel_sr=_int_value("mel_sr", default_config.mel_sr), mel_dim=_int_value("mel_dim", default_config.mel_dim), mel_n_fft=_int_value("mel_n_fft", default_config.mel_n_fft), mel_hop_length=_int_value("mel_hop_length", default_config.mel_hop_length), mel_dtype=mel_dtype, use_whisper_feature_extractor=_bool_value( "use_whisper_feature_extractor", default_config.use_whisper_feature_extractor, ), ) class MossAudioProcessor(ProcessorMixin): attributes = ["tokenizer"] tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast") _AUDIO_SPAN_RE = re.compile(r"<\|audio_bos\|>(?:<\|AUDIO\|>)+<\|audio_eos\|>") def __init__( self, tokenizer=None, mel_config=None, enable_time_marker: bool = False, audio_token_id: int = 151654, audio_start_id: int = 151669, audio_end_id: int = 151670, chat_template=None, ): super().__init__(tokenizer, chat_template=chat_template) if tokenizer is None: raise ValueError("MossAudioProcessor requires a tokenizer.") self._base_tokenizer = tokenizer self.mel_config = _normalize_mel_config(mel_config) self.config = _build_mel_config(self.mel_config) self.enable_time_marker = bool(enable_time_marker) self.audio_token_id = int(audio_token_id) self.audio_start_id = int(audio_start_id) self.audio_end_id = int(audio_end_id) self._whisper_feature_extractor = None alias_map = { "<|AUDIO|>": self.audio_token_id, "<|audio_bos|>": self.audio_start_id, "<|audio_eos|>": self.audio_end_id, } orig_convert_tokens_to_ids = tokenizer.convert_tokens_to_ids def _patched_convert_tokens_to_ids(tokenizer_self, tokens): if isinstance(tokens, (list, tuple)): converted = [ _patched_convert_tokens_to_ids(tokenizer_self, token) for token in tokens ] return converted if isinstance(tokens, list) else tuple(converted) if isinstance(tokens, str) and tokens in alias_map: return alias_map[tokens] return orig_convert_tokens_to_ids(tokens) tokenizer.convert_tokens_to_ids = types.MethodType( _patched_convert_tokens_to_ids, tokenizer ) self._digit_token_ids = { "0": 15, "1": 16, "2": 17, "3": 18, "4": 19, "5": 20, "6": 21, "7": 22, "8": 23, "9": 24, } self.audio_tokens_per_second = 12.5 self.time_marker_every_seconds = 2 self.time_marker_every_audio_tokens = int( self.audio_tokens_per_second * self.time_marker_every_seconds ) @property def model_input_names(self): return [ "input_ids", "attention_mask", "audio_data", "audio_data_seqlens", ] @staticmethod def _conv3_downsample_len(raw_mel_len: int) -> int: def conv_out_len(length: int) -> int: return (length - 1) // 2 + 1 length1 = conv_out_len(int(raw_mel_len)) length2 = conv_out_len(length1) length3 = conv_out_len(length2) return int(length3) def _get_whisper_feature_extractor(self): if self._whisper_feature_extractor is not None: return self._whisper_feature_extractor self._whisper_feature_extractor = WhisperFeatureExtractor( feature_size=int(self.config.mel_dim), sampling_rate=int(self.config.mel_sr), hop_length=int(self.config.mel_hop_length), n_fft=int(self.config.mel_n_fft), ) return self._whisper_feature_extractor def _extract_mel(self, audio: Union[np.ndarray, torch.Tensor]) -> torch.Tensor: if isinstance(audio, np.ndarray): wav = torch.from_numpy(audio) else: wav = audio wav = wav.to(dtype=torch.float32) if wav.dim() == 1: wav = wav.unsqueeze(0) if bool(getattr(self.config, "use_whisper_feature_extractor", False)): fe = self._get_whisper_feature_extractor() wav_np = wav.detach().to("cpu", torch.float32).contiguous().numpy() if wav_np.ndim == 2: wav_np = wav_np[0] feats = fe._np_extract_fbank_features(wav_np[None, ...], device="cpu") mel = torch.from_numpy(feats[0]) else: raise ValueError("MossAudioProcessor requires whisper feature extraction.") return mel.to(dtype=self.config.mel_dtype) def _get_time_marker_token_ids(self, second: int) -> List[int]: return [self._digit_token_ids[digit] for digit in str(second)] def _build_audio_tokens_with_time_markers(self, audio_seq_len: int) -> List[int]: total_duration_seconds = audio_seq_len / self.audio_tokens_per_second num_full_seconds = int(total_duration_seconds) token_ids: List[int] = [] audio_tokens_consumed = 0 for second in range( self.time_marker_every_seconds, num_full_seconds + 1, self.time_marker_every_seconds, ): marker_pos = ( second // self.time_marker_every_seconds ) * self.time_marker_every_audio_tokens audio_segment_len = marker_pos - audio_tokens_consumed if audio_segment_len > 0: token_ids.extend([self.audio_token_id] * audio_segment_len) audio_tokens_consumed += audio_segment_len token_ids.extend(self._get_time_marker_token_ids(second)) remaining = audio_seq_len - audio_tokens_consumed if remaining > 0: token_ids.extend([self.audio_token_id] * remaining) return token_ids def _build_audio_placeholder_ids(self, num_audio_tokens: int) -> List[int]: if self.enable_time_marker: return self._build_audio_tokens_with_time_markers(num_audio_tokens) return [self.audio_token_id] * num_audio_tokens def _build_default_prompt(self, text: str, has_audio: bool) -> str: if has_audio: return ( "<|im_start|>system\n" "You are a helpful assistant.<|im_end|>\n" "<|im_start|>user\n" "<|audio_bos|><|AUDIO|><|audio_eos|>\n" f"{text}<|im_end|>\n" "<|im_start|>assistant\n" ) return ( "<|im_start|>system\n" "You are a helpful assistant.<|im_end|>\n" "<|im_start|>user\n" f"{text}<|im_end|>\n" "<|im_start|>assistant\n" ) def _build_input_from_prompt(self, prompt: str, token_lens: List[int]) -> List[int]: spans = list(self._AUDIO_SPAN_RE.finditer(prompt)) if len(spans) != len(token_lens): raise ValueError( f"Audio placeholder count mismatch: found {len(spans)} spans in text, " f"but got {len(token_lens)} audio inputs." ) input_ids: List[int] = [] cursor = 0 for index, match in enumerate(spans): prefix = prompt[cursor : match.start()] if prefix: input_ids.extend( self._base_tokenizer.encode(prefix, add_special_tokens=False) ) input_ids.append(self.audio_start_id) input_ids.extend(self._build_audio_placeholder_ids(int(token_lens[index]))) input_ids.append(self.audio_end_id) cursor = match.end() suffix = prompt[cursor:] if suffix: input_ids.extend( self._base_tokenizer.encode(suffix, add_special_tokens=False) ) return input_ids def __call__( self, *args, text: Union[str, Sequence[str], None] = None, audios: Optional[Sequence[Union[np.ndarray, torch.Tensor]]] = None, audio: Optional[Sequence[Union[np.ndarray, torch.Tensor]]] = None, return_tensors: str = "pt", **kwargs, ) -> BatchFeature: _ = args, kwargs if isinstance(text, str): prompt_text: Optional[str] = text elif isinstance(text, (list, tuple)): if len(text) != 1: raise ValueError(f"Expected text batch size 1, got {len(text)}") prompt_text = text[0] if not isinstance(prompt_text, str): raise TypeError("Expected text batch size 1 with string content.") elif text is None: prompt_text = None else: raise TypeError("MossAudioProcessor text must be a string or a batch of one string.") audio_list = audios if audios is not None else audio audio_list = [] if audio_list is None else list(audio_list) mels: List[torch.Tensor] = [] raw_lengths: List[int] = [] token_lens: List[int] = [] for one_audio in audio_list: mel = self._extract_mel(one_audio) raw_len = int(mel.shape[-1]) mels.append(mel) raw_lengths.append(raw_len) token_lens.append(self._conv3_downsample_len(raw_len)) if mels: max_length = max(raw_lengths) audio_batch = torch.zeros( (len(mels), self.config.mel_dim, max_length), dtype=self.config.mel_dtype, ) for index, mel in enumerate(mels): audio_batch[index, :, : mel.shape[-1]] = mel seqlens_tensor = torch.tensor(raw_lengths, dtype=torch.long) else: audio_batch = None seqlens_tensor = None if prompt_text is None: raise ValueError( "MossAudioProcessor requires text input. Apply a chat template before calling the processor if needed." ) if self._AUDIO_SPAN_RE.search(prompt_text) is None and audio_list: prompt_text = self._build_default_prompt(prompt_text, has_audio=True) elif self._AUDIO_SPAN_RE.search(prompt_text) is None and not audio_list: prompt_text = self._build_default_prompt(prompt_text, has_audio=False) input_ids_list = self._build_input_from_prompt(prompt_text, token_lens) input_ids_tensor = torch.tensor([input_ids_list], dtype=torch.long) attention_mask_tensor = torch.ones_like(input_ids_tensor) data = { "input_ids": input_ids_tensor, "attention_mask": attention_mask_tensor, } if audio_batch is not None and seqlens_tensor is not None: data["audio_data"] = audio_batch data["audio_data_seqlens"] = seqlens_tensor return BatchFeature(data=data, tensor_type=return_tensors) def batch_decode(self, *args, **kwargs): return self._base_tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): return self._base_tokenizer.decode(*args, **kwargs) __all__ = ["MelConfig", "MossAudioProcessor"]