import re from dataclasses import dataclass from typing import List, Optional, Sequence, Union import numpy as np import torch from transformers import BatchEncoding, Qwen2Tokenizer @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 class MossMusicProcessor: """Builds MOSS-Music inputs: Whisper log-mel features plus a Qwen chat prompt whose audio span is one placeholder token per 80ms of audio, with the elapsed seconds written in as digit tokens every 2s (time markers).""" _AUDIO_SPAN_RE = re.compile(r"<\|audio_bos\|>(?:<\|AUDIO\|>)+<\|audio_eos\|>") def __init__( self, tokenizer, *, mel_config: Optional[MelConfig] = None, enable_time_marker: bool = True, audio_token_id: int = 151654, audio_start_id: int = 151669, audio_end_id: int = 151670, ): self.tokenizer = tokenizer 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.enable_time_marker = bool(enable_time_marker) self.config = mel_config or MelConfig() self._whisper_feature_extractor = None self._digit_token_ids = {str(d): 15 + d for d in range(10)} 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 ) self.model_input_names = [ "input_ids", "attention_mask", "audio_data", "audio_data_seqlens", ] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): tokenizer_kwargs = {} for key in ["cache_dir", "revision", "token", "local_files_only"]: if key in kwargs: tokenizer_kwargs[key] = kwargs[key] # the checkpoint's tokenizer_config names Qwen2Tokenizer; loading it # directly skips AutoTokenizer's remote-code prompt for the auto_map tokenizer = Qwen2Tokenizer.from_pretrained( pretrained_model_name_or_path, **tokenizer_kwargs ) return cls( tokenizer, mel_config=kwargs.pop("mel_config", None), enable_time_marker=kwargs.pop("enable_time_marker", True), audio_token_id=kwargs.pop("audio_token_id", 151654), audio_start_id=kwargs.pop("audio_start_id", 151669), audio_end_id=kwargs.pop("audio_end_id", 151670), ) @staticmethod def _conv3_downsample_len(raw_mel_len: int) -> int: def conv_out_len(length: int) -> int: return (length - 1) // 2 + 1 return conv_out_len(conv_out_len(conv_out_len(int(raw_mel_len)))) def _get_whisper_feature_extractor(self): if self._whisper_feature_extractor is not None: return self._whisper_feature_extractor from transformers.models.whisper.feature_extraction_whisper import ( WhisperFeatureExtractor, ) 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, torch.Tensor): audio = audio.detach().cpu().numpy() wav_np = np.ascontiguousarray(audio, dtype=np.float32) if wav_np.ndim == 2: wav_np = wav_np[0] # the raw fbank pass has no 30s chunking, so full songs come through whole fe = self._get_whisper_feature_extractor() feats = fe._np_extract_fbank_features(wav_np[None, ...], device="cpu") return torch.from_numpy(feats[0]).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.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.tokenizer.encode(suffix, add_special_tokens=False)) return input_ids def __call__( self, *, text: Union[str, Sequence[str]], audios: Optional[Sequence[Union[np.ndarray, torch.Tensor]]] = None, audio: Optional[Sequence[Union[np.ndarray, torch.Tensor]]] = None, return_tensors: str = "pt", **kwargs, ): if isinstance(text, (list, tuple)): if len(text) != 1: raise ValueError(f"Expected text batch size 1, got {len(text)}") prompt_text = text[0] else: prompt_text = text 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 self._AUDIO_SPAN_RE.search(prompt_text) is None: prompt_text = self._build_default_prompt(prompt_text, has_audio=bool(audio_list)) input_ids_list = self._build_input_from_prompt(prompt_text, token_lens) input_ids_tensor = torch.tensor([input_ids_list], dtype=torch.long) data = { "input_ids": input_ids_tensor, "attention_mask": torch.ones_like(input_ids_tensor), } if audio_batch is not None: data["audio_data"] = audio_batch data["audio_data_seqlens"] = seqlens_tensor return BatchEncoding(data=data, tensor_type=return_tensors) 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__ = ["MelConfig", "MossMusicProcessor"]