diff --git a/extensions_built_in/captioner/AceStepCaptioner.py b/extensions_built_in/captioner/AceStepCaptioner.py
index 1f7f78f..80bc9ce 100644
--- a/extensions_built_in/captioner/AceStepCaptioner.py
+++ b/extensions_built_in/captioner/AceStepCaptioner.py
@@ -425,6 +425,14 @@ class AceStepCaptioner(BaseCaptioner):
)
return item
+ @staticmethod
+ def _place_inputs(model, inputs):
+ """Move a processor batch onto the model; the thinker also reads its
+ 2D pad mask from the model. Called again before every decode pass."""
+ inputs = inputs.to(model.device).to(model.dtype)
+ model._pad_mask_2d = inputs.get("attention_mask", None)
+ return inputs
+
def _generate(
self,
model,
@@ -434,17 +442,17 @@ class AceStepCaptioner(BaseCaptioner):
retries: tuple = (),
break_loops: bool = False,
file_path: str = "",
+ max_new_tokens: int = MAX_NEW_TOKENS,
) -> str:
- inputs = inputs.to(model.device).to(model.dtype)
+ inputs = self._place_inputs(model, inputs)
# a generate that dies between static-cache creation and its first
# forward leaves a half-built cache that breaks every later call
stale_cache = getattr(model, "_cache", None)
if stale_cache is not None and not stale_cache.is_initialized:
del model._cache
- model._pad_mask_2d = inputs.get("attention_mask", None)
input_len = inputs["input_ids"].shape[1]
# greedy decode loops on repeated phrases without a penalty
- gen_kwargs = {"max_new_tokens": MAX_NEW_TOKENS, "repetition_penalty": repetition_penalty}
+ gen_kwargs = {"max_new_tokens": max_new_tokens, "repetition_penalty": repetition_penalty}
if model.generation_config.cache_implementation == "static":
if input_len + 16 < STATIC_MAX_LENGTH:
from transformers.generation import MaxLengthCriteria, StoppingCriteriaList
@@ -453,7 +461,7 @@ class AceStepCaptioner(BaseCaptioner):
"repetition_penalty": repetition_penalty,
"max_length": STATIC_MAX_LENGTH,
"stopping_criteria": StoppingCriteriaList(
- [MaxLengthCriteria(max_length=min(input_len + MAX_NEW_TOKENS, STATIC_MAX_LENGTH))]
+ [MaxLengthCriteria(max_length=min(input_len + max_new_tokens, STATIC_MAX_LENGTH))]
),
}
else:
@@ -473,7 +481,7 @@ class AceStepCaptioner(BaseCaptioner):
kwargs = dict(gen_kwargs)
if breaker is not None:
kwargs["logits_processor"] = LogitsProcessorList([breaker])
- model._pad_mask_2d = inputs.get("attention_mask", None)
+ self._place_inputs(model, inputs)
generated = model.generate(**inputs, **kwargs)
return strip_eos(generated[0, input_len:]), breaker
@@ -533,25 +541,29 @@ class AceStepCaptioner(BaseCaptioner):
file_path=file_path,
)
- def get_audio_caption(self, inputs) -> str:
+ def get_audio_caption(self, inputs, file_path: str = "") -> str:
if self.caption_config.low_vram and self.model.device != torch.device("cpu"):
self.model.to("cpu")
if self.model2.device == torch.device("cpu"):
self.model2.to(self.device_torch)
return self._generate(self.model2, self.processor2, inputs, CAPTIONER_REPETITION_PENALTY)
+ def _transcribe_item(self, item: dict) -> str:
+ """Raw transcriber text for a prepped item: '# Languages ... # Lyrics ...'."""
+ return self.get_audio_lyrics(item["lyrics_inputs"], item["file"])
+
def _caption_item(self, item: dict) -> str:
if self.caption_config.caption_format == "yue2":
- lyrics = clean_lyrics(self.get_audio_lyrics(item["lyrics_inputs"], item["file"]))
+ lyrics = clean_lyrics(self._transcribe_item(item))
if self.caption_config.fixed_caption is not None:
caption = self.caption_config.fixed_caption
else:
- caption = self.get_audio_caption(item["caption_inputs"])
+ caption = self.get_audio_caption(item["caption_inputs"], item["file"])
caption = " ".join(caption.split())
return f"{caption}\n[Lyrics]\n{lyrics}"
analysis = item["analysis"]
- lyrics = self.get_audio_lyrics(item["lyrics_inputs"], item["file"])
+ lyrics = self._transcribe_item(item)
language = "en"
if "# Languages" in lyrics and "# Lyrics" in lyrics:
@@ -563,7 +575,7 @@ class AceStepCaptioner(BaseCaptioner):
if self.caption_config.fixed_caption is not None:
caption = self.caption_config.fixed_caption
else:
- caption = self.get_audio_caption(item["caption_inputs"])
+ caption = self.get_audio_caption(item["caption_inputs"], item["file"])
output = f"\n{caption}\n\n"
output += f"\n{lyrics}\n\n"
diff --git a/extensions_built_in/captioner/MossMusicCaptioner.py b/extensions_built_in/captioner/MossMusicCaptioner.py
new file mode 100644
index 0000000..d7559f3
--- /dev/null
+++ b/extensions_built_in/captioner/MossMusicCaptioner.py
@@ -0,0 +1,169 @@
+"""MOSS-Music captioner: one 8B audio-language model (OpenMOSS-Team/MOSS-Music-8B-Instruct)
+does both the lyric transcription and the description that AceStepCaptioner
+runs two Qwen2.5-Omni checkpoints for. Output layouts, loop breaking and the
+worker pipeline are inherited from AceStepCaptioner."""
+
+import re
+
+from optimum.quanto import freeze
+
+from toolkit.audio.moss_music import HF_REPO, load_moss_music
+from toolkit.basic import flush
+from toolkit.util.quantize import get_qtype, quantize
+
+from .AceStepCaptioner import (
+ CAPTIONER_REPETITION_PENALTY,
+ TRANSCRIBER_REPETITION_PENALTY,
+ TRANSCRIBER_RETRIES,
+ AceStepCaptionConfig,
+ AceStepCaptioner,
+ analyze_audio,
+)
+
+# timestamps are requested on purpose: each line is anchored to the input time
+# markers, so decode has to advance through the song instead of looping on a
+# repeated phrase. They are stripped from the saved caption.
+TRANSCRIBE_PROMPT = "Transcribe the lyrics of this song with timestamps."
+# the model writes multi-section essays with timings, chords and lyric quotes
+# unless framed as a generator prompt; this keeps it to one plain paragraph
+CAPTION_PROMPT = (
+ "Write a prompt that a text-to-music generator could use to recreate this track. One paragraph, "
+ "under 80 words: genre, mood, instrumentation, tempo feel, production style, vocal style. "
+ "No timestamps, section timings, chord names, or lyric quotes."
+)
+LANGUAGE_PROMPT = (
+ "What language are the lyrics sung in? Answer with only the two-letter "
+ "ISO 639-1 language code, or none if there are no lyrics."
+)
+LANGUAGE_MAX_NEW_TOKENS = 8
+
+# the model writes "[00:18.00 - 00:22.00] line"; a looping decode can run the
+# seconds field past 59 ("[00:100.00 - ...]"), so any bracketed run of time
+# characters is treated as a timestamp, plus bare mm:ss spans at a line start
+_TIME = r"(?:\d{1,2}:)?\d{1,3}:\d{1,3}(?:[.:]\d{1,3})?|\d+(?:\.\d+)?\s*s"
+_SPAN = rf"(?:{_TIME})(?:\s*(?:-{{1,3}}>?|–|—|~|to)\s*(?:{_TIME}))?"
+_TIME_CHARS = r"[\d:.\s\-–—>~s]*\d[\d:.\s\-–—>~s]*"
+TIMESTAMP_RE = re.compile(
+ rf"\[{_TIME_CHARS}\]|\({_TIME_CHARS}\)|<{_TIME_CHARS}>|^\s*{_SPAN}\s*[:\-–—|]?",
+ re.MULTILINE,
+)
+
+
+def strip_timestamps(text: str) -> str:
+ """Remove time annotations and tidy the line layout they leave behind."""
+ text = TIMESTAMP_RE.sub("", text)
+ lines = [" ".join(line.split()) for line in text.splitlines()]
+ out = []
+ for line in lines:
+ if line == "" and (not out or out[-1] == ""):
+ continue
+ out.append(line)
+ return "\n".join(out).strip()
+
+
+class MossMusicCaptionConfig(AceStepCaptionConfig):
+ def __init__(self, **kwargs):
+ kwargs.setdefault("model_name_or_path", HF_REPO)
+ super().__init__(**kwargs)
+ self.caption_prompt: str = kwargs.get("caption_prompt") or CAPTION_PROMPT
+ self.transcribe_prompt: str = kwargs.get("transcribe_prompt") or TRANSCRIBE_PROMPT
+ # MOSS transcribes the full mix better than a separated vocal stem
+ self.extract_vocals_before_transcribe = False
+ # leave the model's "[00:18.00 - 00:22.00]" line times in the saved lyrics
+ self.keep_timestamps: bool = bool(kwargs.get("keep_timestamps", False))
+
+
+class MossMusicCaptioner(AceStepCaptioner):
+ caption_config_class = MossMusicCaptionConfig
+ caption_config: MossMusicCaptionConfig
+
+ def load_model(self):
+ self.print_and_status_update("Loading MOSS-Music model")
+ self.model, self.processor = load_moss_music(
+ self.caption_config.model_name_or_path,
+ dtype=self.torch_dtype,
+ device=str(self.device_torch),
+ )
+ if self.caption_config.quantize:
+ self.print_and_status_update("Quantizing MOSS-Music model")
+ quantize(self.model, weights=get_qtype(self.caption_config.qtype))
+ freeze(self.model)
+ flush()
+ # one model does both jobs; AceStepCaptioner's second slot stays empty
+ self.model2 = None
+ self.processor2 = None
+ flush()
+
+ @staticmethod
+ def _place_inputs(model, inputs):
+ inputs = inputs.to(model.device)
+ inputs["audio_data"] = inputs["audio_data"].to(model.dtype)
+ return inputs
+
+ def _make_inputs(self, audio_data, prompt_text: str):
+ inputs = self.processor(text=prompt_text, audios=[audio_data])
+ inputs["audio_input_mask"] = inputs["input_ids"] == self.processor.audio_token_id
+ return inputs
+
+ def _prep_file(self, file_path: str) -> dict:
+ audio_data = self._load_audio(file_path)
+ is_ace_step = self.caption_config.caption_format == "ace_step"
+ item = {
+ "file": file_path,
+ "analysis": analyze_audio(file_path) if is_ace_step else None,
+ "lyrics_inputs": self._make_inputs(audio_data, self.caption_config.transcribe_prompt),
+ }
+ if is_ace_step:
+ item["language_inputs"] = self._make_inputs(audio_data, LANGUAGE_PROMPT)
+ if self.caption_config.fixed_caption is None:
+ item["caption_inputs"] = self._make_inputs(
+ audio_data, self.caption_config.caption_prompt
+ )
+ return item
+
+ def get_audio_lyrics(self, inputs, file_path: str = "") -> str:
+ text = self._generate(
+ self.model,
+ self.processor,
+ inputs,
+ TRANSCRIBER_REPETITION_PENALTY,
+ retries=TRANSCRIBER_RETRIES,
+ break_loops=True,
+ file_path=file_path,
+ )
+ return text.strip() if self.caption_config.keep_timestamps else strip_timestamps(text)
+
+ def get_audio_language(self, inputs) -> str:
+ answer = self._generate(
+ self.model,
+ self.processor,
+ inputs,
+ repetition_penalty=1.0,
+ max_new_tokens=LANGUAGE_MAX_NEW_TOKENS,
+ )
+ match = re.search(r"[a-z]{2,3}", answer.lower())
+ code = match.group(0) if match else ""
+ # instrumentals answer none; ACE-Step's default language is en
+ return code if len(code) == 2 and code != "no" else "en"
+
+ def _transcribe_item(self, item: dict) -> str:
+ lyrics = self.get_audio_lyrics(item["lyrics_inputs"], item["file"])
+ if "language_inputs" not in item:
+ return lyrics
+ # the layout AceStepCaptioner._caption_item parses from the ACE transcriber
+ language = self.get_audio_language(item["language_inputs"])
+ return f"# Languages\n{language}\n# Lyrics\n{lyrics}"
+
+ def get_audio_caption(self, inputs, file_path: str = "") -> str:
+ # a tag-list prompt can degenerate into a repeated tag; same loop
+ # breaking as transcription
+ caption = self._generate(
+ self.model,
+ self.processor,
+ inputs,
+ CAPTIONER_REPETITION_PENALTY,
+ retries=TRANSCRIBER_RETRIES,
+ break_loops=True,
+ file_path=file_path,
+ )
+ return strip_timestamps(caption)
diff --git a/extensions_built_in/captioner/__init__.py b/extensions_built_in/captioner/__init__.py
index e838757..e42683f 100644
--- a/extensions_built_in/captioner/__init__.py
+++ b/extensions_built_in/captioner/__init__.py
@@ -13,6 +13,18 @@ class AceStepCaptionerExtension(Extension):
return AceStepCaptioner
+class MossMusicCaptionerExtension(Extension):
+ uid = "MossMusicCaptioner"
+ name = "MOSS-Music Captioner"
+
+ @classmethod
+ def get_process(cls):
+ # import your process class here so it is only loaded when needed and return it
+ from .MossMusicCaptioner import MossMusicCaptioner
+
+ return MossMusicCaptioner
+
+
class Qwen3VLCaptionerExtension(Extension):
uid = "Qwen3VLCaptioner"
name = "Qwen 3VL Captioner"
@@ -63,6 +75,7 @@ class Ideogram4CaptionerExtension(Extension):
AI_TOOLKIT_EXTENSIONS = [
AceStepCaptionerExtension,
+ MossMusicCaptionerExtension,
Qwen3VLCaptionerExtension,
Qwen3OmniCaptionerExtension,
Qwen25OmniCaptionerExtension,
diff --git a/toolkit/audio/moss_music/LICENSE b/toolkit/audio/moss_music/LICENSE
new file mode 100644
index 0000000..52d5be3
--- /dev/null
+++ b/toolkit/audio/moss_music/LICENSE
@@ -0,0 +1,21 @@
+The code in this directory is adapted from MOSS-Music by the OpenMOSS Team
+(https://github.com/OpenMOSS/MOSS-Music), licensed under the Apache License,
+Version 2.0. Modifications for ai-toolkit: transformers 5.x compatibility
+(WhisperEncoderLayer tensor return, generate input preparation), removal of the
+chat-template loader, and direct Qwen2Tokenizer loading.
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/toolkit/audio/moss_music/__init__.py b/toolkit/audio/moss_music/__init__.py
new file mode 100644
index 0000000..a5ebf73
--- /dev/null
+++ b/toolkit/audio/moss_music/__init__.py
@@ -0,0 +1,35 @@
+import torch
+
+from .configuration import MossMusicConfig, MossMusicEncoderConfig
+from .modeling import MossMusicModel
+from .processing import MelConfig, MossMusicProcessor
+
+HF_REPO = "OpenMOSS-Team/MOSS-Music-8B-Instruct"
+
+
+def load_moss_music(
+ name_or_path: str = HF_REPO,
+ dtype: torch.dtype = torch.bfloat16,
+ device="cpu",
+ enable_time_marker: bool = True,
+):
+ """Model + processor from a hub id or local checkpoint dir. Time markers
+ (elapsed seconds interleaved into the audio tokens) are what the model was
+ trained with and keep long transcriptions from looping; leave them on."""
+ model = MossMusicModel.from_pretrained(name_or_path, dtype=dtype, device_map=device)
+ model.eval()
+ processor = MossMusicProcessor.from_pretrained(
+ name_or_path, enable_time_marker=enable_time_marker
+ )
+ return model, processor
+
+
+__all__ = [
+ "HF_REPO",
+ "MelConfig",
+ "MossMusicConfig",
+ "MossMusicEncoderConfig",
+ "MossMusicModel",
+ "MossMusicProcessor",
+ "load_moss_music",
+]
diff --git a/toolkit/audio/moss_music/configuration.py b/toolkit/audio/moss_music/configuration.py
new file mode 100644
index 0000000..f47e47b
--- /dev/null
+++ b/toolkit/audio/moss_music/configuration.py
@@ -0,0 +1,135 @@
+from typing import List, Optional
+from dataclasses import dataclass, field
+
+from transformers import PretrainedConfig, Qwen3Config
+
+
+@dataclass
+class MossMusicEncoderConfig:
+ d_model: int = 1280
+ output_dim: int = 1280
+ num_mel_bins: int = 128
+ encoder_layers: int = 32
+ encoder_attention_heads: int = 20
+ encoder_ffn_dim: int = 5120
+ downsample_rate: int = 8
+ downsample_hidden_size: int = 480
+ encoder_attention_window_size: int = 100
+ max_source_positions: int = 1500
+ dropout: float = 0.1
+ attention_dropout: float = 0.1
+ activation_dropout: float = 0.0
+ activation_function: str = "gelu"
+ layer_norm_eps: float = 1e-5
+ _attn_implementation: str = "eager"
+ pretrained_path: str = ""
+
+ n_window: int = 200
+ conv_chunksize: int = 64
+
+ deepstack_encoder_layer_indexes: List[int] = field(
+ default_factory=lambda: [8, 16, 24]
+ )
+
+ @classmethod
+ def from_dict(cls, config_dict):
+ if config_dict is None:
+ return cls()
+ allowed_keys = set(cls.__dataclass_fields__.keys())
+ filtered = {k: v for k, v in config_dict.items() if k in allowed_keys}
+ return cls(**filtered)
+
+ def to_dict(self):
+ return {
+ "d_model": self.d_model,
+ "output_dim": self.output_dim,
+ "num_mel_bins": self.num_mel_bins,
+ "encoder_layers": self.encoder_layers,
+ "encoder_attention_heads": self.encoder_attention_heads,
+ "encoder_ffn_dim": self.encoder_ffn_dim,
+ "downsample_rate": self.downsample_rate,
+ "downsample_hidden_size": self.downsample_hidden_size,
+ "encoder_attention_window_size": self.encoder_attention_window_size,
+ "max_source_positions": self.max_source_positions,
+ "dropout": self.dropout,
+ "attention_dropout": self.attention_dropout,
+ "activation_dropout": self.activation_dropout,
+ "activation_function": self.activation_function,
+ "layer_norm_eps": self.layer_norm_eps,
+ "_attn_implementation": self._attn_implementation,
+ "pretrained_path": self.pretrained_path,
+ "n_window": self.n_window,
+ "conv_chunksize": self.conv_chunksize,
+ "deepstack_encoder_layer_indexes": list(
+ self.deepstack_encoder_layer_indexes or []
+ ),
+ }
+
+
+class MossMusicConfig(PretrainedConfig):
+ model_type = "moss_music"
+ is_composition = True
+
+ def __init__(
+ self,
+ audio_config=None,
+ language_config=None,
+ adapter_hidden_size=8192,
+ ignore_index=-100,
+ deepstack_num_inject_layers: Optional[int] = None,
+ **kwargs,
+ ):
+ if isinstance(audio_config, dict):
+ audio_config = MossMusicEncoderConfig.from_dict(audio_config)
+ elif audio_config is None:
+ audio_config = MossMusicEncoderConfig()
+
+ if isinstance(language_config, dict):
+ language_config = Qwen3Config(**language_config)
+ elif language_config is None:
+ language_config = Qwen3Config()
+
+ self.audio_config = audio_config
+ self.language_config = language_config
+ self.adapter_hidden_size = adapter_hidden_size
+ self.ignore_index = ignore_index
+ self.deepstack_num_inject_layers = deepstack_num_inject_layers
+
+ _propagate_keys = {
+ "num_hidden_layers",
+ "eos_token_id",
+ "bos_token_id",
+ "vocab_size",
+ "tie_word_embeddings",
+ }
+ for key in ("num_hidden_layers", "eos_token_id", "bos_token_id", "vocab_size"):
+ kwargs.setdefault(key, getattr(language_config, key, None))
+ kwargs.setdefault("tie_word_embeddings", False)
+
+ if hasattr(language_config, "to_dict"):
+ _lang_keys = set(language_config.to_dict().keys())
+ for key in list(kwargs.keys()):
+ if key in _lang_keys and key not in _propagate_keys:
+ kwargs.pop(key)
+
+ super().__init__(**kwargs)
+
+ def to_dict(self):
+ output = super().to_dict()
+ output["audio_config"] = (
+ self.audio_config.to_dict()
+ if hasattr(self.audio_config, "to_dict")
+ else self.audio_config
+ )
+ output["language_config"] = (
+ self.language_config.to_dict()
+ if hasattr(self.language_config, "to_dict")
+ else self.language_config
+ )
+ output["adapter_hidden_size"] = self.adapter_hidden_size
+ output["ignore_index"] = self.ignore_index
+ output["deepstack_num_inject_layers"] = self.deepstack_num_inject_layers
+ return output
+
+
+__all__ = ["MossMusicEncoderConfig", "MossMusicConfig"]
diff --git a/toolkit/audio/moss_music/modeling.py b/toolkit/audio/moss_music/modeling.py
new file mode 100644
index 0000000..4487678
--- /dev/null
+++ b/toolkit/audio/moss_music/modeling.py
@@ -0,0 +1,568 @@
+from typing import Any, List, Optional, Tuple
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.generation.utils import GenerationMixin
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.qwen3.modeling_qwen3 import Qwen3DecoderLayer, Qwen3Model
+from transformers.models.whisper.modeling_whisper import WhisperEncoderLayer
+
+from .configuration import MossMusicConfig, MossMusicEncoderConfig
+
+
+class SinusoidsPositionEmbedding(nn.Module):
+ def __init__(self, num_positions: int, embedding_dim: int):
+ super().__init__()
+ max_timescale = 10000.0
+ log_timescale_increment = math.log(max_timescale) / (embedding_dim // 2 - 1)
+ inv_timescales = torch.exp(
+ -log_timescale_increment * torch.arange(embedding_dim // 2).float()
+ )
+ self.register_buffer("inv_timescales", inv_timescales, persistent=False)
+
+ def forward(self, seq_len: int, device: torch.device):
+ scaled_time = torch.arange(
+ seq_len, device=device, dtype=self.inv_timescales.dtype
+ ).unsqueeze(1) * self.inv_timescales.unsqueeze(0)
+ sin_emb = torch.sin(scaled_time)
+ cos_emb = torch.cos(scaled_time)
+ pos_emb = torch.cat([sin_emb, cos_emb], dim=1)
+ return pos_emb.unsqueeze(0)
+
+
+class MossMusicEncoder(nn.Module):
+ """Audio encoder with conv-stem downsampling and Whisper transformer layers."""
+
+ def __init__(self, config: MossMusicEncoderConfig):
+ super().__init__()
+ self.config = config
+ self.gelu = nn.GELU()
+
+ self.conv1 = nn.Conv2d(
+ 1,
+ config.downsample_hidden_size,
+ kernel_size=(3, 3),
+ stride=(2, 2),
+ padding=(1, 1),
+ )
+ self.conv2 = nn.Conv2d(
+ config.downsample_hidden_size,
+ config.downsample_hidden_size,
+ kernel_size=(3, 3),
+ stride=(2, 2),
+ padding=(1, 1),
+ )
+ self.conv3 = nn.Conv2d(
+ config.downsample_hidden_size,
+ config.downsample_hidden_size,
+ kernel_size=(3, 3),
+ stride=(2, 2),
+ padding=(1, 1),
+ )
+
+ # 128 mel bins / 8 = 16 after 3 convs with stride=2
+ self.stem_proj = nn.Linear(config.downsample_hidden_size * 16, config.d_model)
+ self.embed_positions = SinusoidsPositionEmbedding(
+ config.max_source_positions, config.d_model
+ )
+ self.layers = nn.ModuleList(
+ [WhisperEncoderLayer(config) for _ in range(config.encoder_layers)]
+ )
+ self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
+ self.out_proj = (
+ nn.Linear(config.d_model, config.output_dim, bias=False)
+ if config.output_dim != config.d_model
+ else nn.Identity()
+ )
+
+ self.deepstack_encoder_layer_indexes = list(
+ config.deepstack_encoder_layer_indexes or []
+ )
+ self._deepstack_capture_map = {
+ layer_idx: capture_idx
+ for capture_idx, layer_idx in enumerate(self.deepstack_encoder_layer_indexes)
+ }
+
+ self.n_window = int(config.n_window)
+ self.chunk_frames = int(self.n_window * 2)
+ self.conv_chunksize = int(config.conv_chunksize)
+
+ @property
+ def dtype(self) -> torch.dtype:
+ return self.conv1.weight.dtype
+
+ @staticmethod
+ def _compute_downsampled_length(lengths: torch.Tensor) -> torch.Tensor:
+ def conv_out_len(L):
+ return (L - 1) // 2 + 1
+
+ return conv_out_len(conv_out_len(conv_out_len(lengths)))
+
+ def _encode_chunk_batch(
+ self,
+ input_features: torch.Tensor,
+ seq_lengths: torch.Tensor,
+ ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+ """Encode a batch of (already padded) chunks through the conv stem and
+ transformer layers. Returns (last_hidden, ordered_deepstack_hidden_states).
+ """
+ if input_features.dim() == 2:
+ input_features = input_features.unsqueeze(0)
+
+ downsampled_lengths = self._compute_downsampled_length(seq_lengths)
+
+ # [B, n_mels, T] -> [B, 1, n_mels, T]
+ x = input_features.unsqueeze(1)
+ x = self.gelu(self.conv1(x))
+ x = self.gelu(self.conv2(x))
+ x = self.gelu(self.conv3(x))
+
+ # [B, C, F, T] -> [B, T, C*F]
+ x = x.permute(0, 3, 1, 2).contiguous().flatten(2)
+ x = self.stem_proj(x)
+
+ max_len = int(downsampled_lengths.max().item())
+ if x.size(1) > max_len:
+ x = x[:, :max_len, :]
+
+ positions = self.embed_positions(x.shape[1], x.device)
+ x = x + positions.to(x.dtype)
+
+ padding_mask = (
+ torch.arange(x.size(1), device=x.device)[None, :]
+ >= downsampled_lengths[:, None]
+ )
+ attention_mask = (1.0 - (~padding_mask).to(dtype=x.dtype)) * torch.finfo(
+ x.dtype
+ ).min
+ attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
+
+ deepstack_hidden_states: List[Optional[torch.Tensor]] = [None] * len(
+ self.deepstack_encoder_layer_indexes
+ )
+ for layer_idx, layer in enumerate(self.layers):
+ x = layer(x, attention_mask)
+ capture_idx = self._deepstack_capture_map.get(layer_idx)
+ if capture_idx is not None:
+ deepstack_hidden_states[capture_idx] = x
+
+ x = self.layer_norm(x)
+ x = self.out_proj(x)
+
+ ordered_deepstack_hidden_states = [
+ h for h in deepstack_hidden_states if h is not None
+ ]
+ if not isinstance(self.out_proj, nn.Identity):
+ ordered_deepstack_hidden_states = [
+ self.out_proj(h) for h in ordered_deepstack_hidden_states
+ ]
+ return x, ordered_deepstack_hidden_states
+
+ def forward(
+ self,
+ input_features: torch.Tensor,
+ feature_lens: Optional[torch.Tensor] = None,
+ output_deepstack_hidden_states: bool = True,
+ ) -> BaseModelOutputWithPast:
+ if input_features.dim() == 3:
+ if feature_lens is None:
+ feature_lens = torch.full(
+ (input_features.size(0),),
+ input_features.size(-1),
+ dtype=torch.long,
+ device=input_features.device,
+ )
+ else:
+ feature_lens = feature_lens.to(
+ device=input_features.device, dtype=torch.long
+ )
+ valid_chunks = [
+ input_features[i, :, : int(feature_lens[i].item())]
+ for i in range(int(input_features.shape[0]))
+ ]
+ input_features = torch.cat(valid_chunks, dim=1)
+ elif input_features.dim() != 2:
+ raise ValueError(
+ f"Expected [n_mels, T] or [B, n_mels, T], got {tuple(input_features.shape)}."
+ )
+
+ if feature_lens is None:
+ feature_lens = torch.tensor(
+ [int(input_features.shape[1])],
+ device=input_features.device,
+ dtype=torch.long,
+ )
+ else:
+ feature_lens = feature_lens.to(
+ device=input_features.device, dtype=torch.long
+ )
+
+ chunk_frames = int(self.chunk_frames)
+ chunk_num = torch.ceil(
+ feature_lens.to(torch.float32) / float(chunk_frames)
+ ).long()
+ chunk_lengths = torch.full(
+ (int(chunk_num.sum().item()),),
+ chunk_frames,
+ dtype=torch.long,
+ device=feature_lens.device,
+ )
+ tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:]
+ chunk_lengths[tail_chunk_index] = feature_lens % chunk_frames
+ chunk_lengths[chunk_lengths == 0] = chunk_frames
+
+ chunk_list = input_features.T.split(chunk_lengths.tolist(), dim=0)
+ padded_feature = nn.utils.rnn.pad_sequence(
+ chunk_list, batch_first=True
+ ).transpose(1, 2)
+
+ feature_lens_after_cnn = self._compute_downsampled_length(chunk_lengths)
+ t_down_max = (
+ int(feature_lens_after_cnn.max().item())
+ if feature_lens_after_cnn.numel() > 0
+ else 0
+ )
+ padded_mask_after_cnn = nn.utils.rnn.pad_sequence(
+ [
+ torch.ones(int(L.item()), dtype=torch.bool, device=padded_feature.device)
+ for L in feature_lens_after_cnn
+ ],
+ batch_first=True,
+ )
+ if padded_mask_after_cnn.shape[1] < t_down_max:
+ padded_mask_after_cnn = F.pad(
+ padded_mask_after_cnn,
+ (0, t_down_max - padded_mask_after_cnn.shape[1]),
+ value=False,
+ )
+
+ num_deepstack = len(self.deepstack_encoder_layer_indexes)
+ padded_embeds: List[torch.Tensor] = []
+ deepstack_padded_embeds: List[List[torch.Tensor]] = [
+ [] for _ in range(num_deepstack)
+ ]
+ for feat_chunk, len_chunk in zip(
+ padded_feature.split(self.conv_chunksize, dim=0),
+ chunk_lengths.split(self.conv_chunksize, dim=0),
+ ):
+ out, deepstack_outs = self._encode_chunk_batch(feat_chunk, len_chunk)
+ if out.shape[1] < t_down_max:
+ out = F.pad(out, (0, 0, 0, t_down_max - out.shape[1]))
+ padded_embeds.append(out)
+ if output_deepstack_hidden_states and num_deepstack > 0:
+ if len(deepstack_outs) != num_deepstack:
+ raise RuntimeError(
+ "Deepstack output count does not match configured layer indexes."
+ )
+ for capture_idx, ds in enumerate(deepstack_outs):
+ if ds.shape[1] < t_down_max:
+ ds = F.pad(ds, (0, 0, 0, t_down_max - ds.shape[1]))
+ deepstack_padded_embeds[capture_idx].append(ds)
+
+ if padded_embeds:
+ padded_embed = torch.cat(padded_embeds, dim=0)
+ else:
+ padded_embed = torch.empty(
+ (0, t_down_max, self.config.output_dim),
+ device=padded_feature.device,
+ )
+
+ valid_tokens = padded_embed[padded_mask_after_cnn] # [N_valid, D]
+ last_hidden_state = valid_tokens.unsqueeze(0) # [1, N_valid, D]
+
+ deepstack_states: Optional[Tuple[torch.Tensor, ...]] = None
+ if output_deepstack_hidden_states and num_deepstack > 0:
+ collected: List[torch.Tensor] = []
+ for chunks_list in deepstack_padded_embeds:
+ if chunks_list:
+ ds = torch.cat(chunks_list, dim=0)
+ collected.append(ds[padded_mask_after_cnn].unsqueeze(0))
+ else:
+ collected.append(
+ torch.empty(
+ (1, 0, self.config.output_dim),
+ device=padded_feature.device,
+ dtype=padded_embed.dtype,
+ )
+ )
+ deepstack_states = tuple(collected)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=last_hidden_state,
+ hidden_states=deepstack_states,
+ )
+
+
+class GatedMLP(nn.Module):
+ def __init__(self, input_size, hidden_size, output_size):
+ super().__init__()
+ self.gate_proj = nn.Linear(input_size, hidden_size, bias=False)
+ self.up_proj = nn.Linear(input_size, hidden_size, bias=False)
+ self.down_proj = nn.Linear(hidden_size, output_size, bias=False)
+ self.act_fn = nn.SiLU()
+
+ def forward(self, x):
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class MossMusicPreTrainedModel(PreTrainedModel):
+ config_class = MossMusicConfig
+ config: MossMusicConfig
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Qwen3DecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = False
+ _supports_attention_backend = True
+ _can_record_outputs = {"hidden_states": Qwen3DecoderLayer}
+
+
+class MossMusicModel(MossMusicPreTrainedModel, GenerationMixin):
+ config_class = MossMusicConfig
+ _tied_weights_keys: List[str] = []
+
+ def __init__(self, config: MossMusicConfig):
+ super().__init__(config)
+
+ self.audio_encoder = MossMusicEncoder(config.audio_config)
+ self.language_model = Qwen3Model(config.language_config)
+
+ self.audio_adapter = GatedMLP(
+ input_size=config.audio_config.output_dim,
+ hidden_size=config.adapter_hidden_size,
+ output_size=config.language_config.hidden_size,
+ )
+
+ deepstack_k = len(
+ getattr(config.audio_config, "deepstack_encoder_layer_indexes", []) or []
+ )
+ if config.deepstack_num_inject_layers is not None:
+ deepstack_k = min(deepstack_k, int(config.deepstack_num_inject_layers))
+ self.deepstack_audio_merger_list = nn.ModuleList(
+ [
+ GatedMLP(
+ input_size=config.audio_config.output_dim,
+ hidden_size=config.adapter_hidden_size,
+ output_size=config.language_config.hidden_size,
+ )
+ for _ in range(deepstack_k)
+ ]
+ )
+
+ self.vocab_size = config.language_config.vocab_size
+ self.lm_head = nn.Linear(
+ config.language_config.hidden_size, self.vocab_size, bias=False
+ )
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def get_audio_features(self, input_features, feature_lens):
+ audio_outputs = self.audio_encoder(
+ input_features=input_features,
+ feature_lens=feature_lens,
+ output_deepstack_hidden_states=True,
+ )
+ deepstack = (
+ list(audio_outputs.hidden_states)
+ if audio_outputs.hidden_states is not None
+ else None
+ )
+ return audio_outputs.last_hidden_state, deepstack
+
+ def _apply_deepstack_to_hidden_states(
+ self,
+ hidden_states: torch.Tensor,
+ audio_input_mask: torch.Tensor,
+ deepstack_embeds: torch.Tensor,
+ ) -> torch.Tensor:
+ audio_input_mask = audio_input_mask.to(hidden_states.device)
+ deepstack_embeds = deepstack_embeds.to(hidden_states.device, hidden_states.dtype)
+ flat = deepstack_embeds.reshape(-1, deepstack_embeds.shape[-1])
+ hs = hidden_states.clone()
+ hs[audio_input_mask] = hs[audio_input_mask] + flat
+ return hs
+
+ def _register_llm_deepstack_hooks(
+ self,
+ audio_input_mask: torch.Tensor,
+ deepstack_audio_embeds: List[torch.Tensor],
+ ):
+ if deepstack_audio_embeds is None or len(deepstack_audio_embeds) == 0:
+ return []
+
+ layers = getattr(self.language_model, "layers", None)
+ if layers is None:
+ raise RuntimeError(
+ "Qwen3Model does not expose `.layers`; cannot register DeepStack hooks."
+ )
+
+ num_inject = len(deepstack_audio_embeds)
+ handles = []
+
+ for layer_idx, layer in enumerate(layers):
+ if layer_idx >= num_inject:
+ break
+
+ def _make_llm_hook(k: int):
+ def _hook(_module, _inputs, _output):
+ if isinstance(_output, (tuple, list)):
+ hs = _output[0]
+ new_hs = self._apply_deepstack_to_hidden_states(
+ hs, audio_input_mask, deepstack_audio_embeds[k]
+ )
+ return (new_hs,) + tuple(_output[1:])
+ else:
+ return self._apply_deepstack_to_hidden_states(
+ _output, audio_input_mask, deepstack_audio_embeds[k]
+ )
+
+ return _hook
+
+ handles.append(layer.register_forward_hook(_make_llm_hook(layer_idx)))
+
+ return handles
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ audio_data: Optional[torch.FloatTensor] = None,
+ audio_data_seqlens: Optional[torch.Tensor] = None,
+ audio_input_mask: Optional[torch.Tensor] = None,
+ **kwargs: Any,
+ ) -> CausalLMOutputWithPast:
+ # generate() still passes return_dict; transformers 5 models ignore it
+ kwargs.pop("return_dict", None)
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ hook_handles = []
+ if audio_data is not None:
+ if audio_input_mask is None:
+ raise ValueError(
+ "audio_input_mask is required when audio_data is provided."
+ )
+
+ audio_embeds, deepstack = self.get_audio_features(audio_data, audio_data_seqlens)
+ audio_embeds = self.audio_adapter(audio_embeds)
+
+ audio_token_count = int(audio_input_mask.to(torch.int32).sum().item())
+ if audio_token_count != int(audio_embeds.shape[1]):
+ raise ValueError(
+ f"Audio token count mismatch: audio_input_mask has {audio_token_count} audio tokens, "
+ f"but audio_embeds has length {int(audio_embeds.shape[1])}."
+ )
+
+ mask_expanded = audio_input_mask.unsqueeze(-1).expand_as(inputs_embeds)
+ inputs_embeds = inputs_embeds.clone()
+ inputs_embeds.masked_scatter_(mask_expanded, audio_embeds)
+
+ if deepstack is not None and len(self.deepstack_audio_merger_list) > 0:
+ deepstack_audio_embeds = []
+ for i, x in enumerate(deepstack[: len(self.deepstack_audio_merger_list)]):
+ ds = self.deepstack_audio_merger_list[i](x)
+ if int(ds.shape[1]) != audio_token_count:
+ raise ValueError(
+ f"DeepStack audio seq_len mismatch at index {i}: "
+ f"expected {audio_token_count}, got {int(ds.shape[1])}."
+ )
+ deepstack_audio_embeds.append(ds)
+
+ try:
+ hook_handles = self._register_llm_deepstack_hooks(
+ audio_input_mask, deepstack_audio_embeds
+ )
+ except Exception:
+ for h in hook_handles:
+ h.remove()
+ raise
+
+ try:
+ outputs = self.language_model(
+ input_ids=None,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ **kwargs,
+ )
+ finally:
+ for h in hook_handles:
+ h.remove()
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.ignore_index)
+ shift_logits = shift_logits.view(-1, self.config.language_config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ next_sequence_length=None,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ is_first_iteration=False,
+ **kwargs,
+ ):
+ # audio is encoded once into the prompt embeds; re-sending it on decode
+ # steps would re-run the encoder against a stale audio_input_mask
+ if not is_first_iteration:
+ for key in ("audio_data", "audio_data_seqlens", "audio_input_mask"):
+ kwargs.pop(key, None)
+ return super().prepare_inputs_for_generation(
+ input_ids,
+ next_sequence_length=next_sequence_length,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ is_first_iteration=is_first_iteration,
+ **kwargs,
+ )
+
+
+__all__ = [
+ "MossMusicEncoderConfig",
+ "MossMusicConfig",
+ "MossMusicModel",
+]
diff --git a/toolkit/audio/moss_music/processing.py b/toolkit/audio/moss_music/processing.py
new file mode 100644
index 0000000..46e017c
--- /dev/null
+++ b/toolkit/audio/moss_music/processing.py
@@ -0,0 +1,250 @@
+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"]
diff --git a/ui/src/components/CaptionSimpleJob.tsx b/ui/src/components/CaptionSimpleJob.tsx
index ef05ec1..4c0ee6d 100644
--- a/ui/src/components/CaptionSimpleJob.tsx
+++ b/ui/src/components/CaptionSimpleJob.tsx
@@ -283,6 +283,13 @@ const CaptionSimpleJob: React.FC = ({ jobConfig, setJobConfig, gpuIDs, se
onChange={value => setJobConfig(value, 'config.process[0].caption.extract_vocals_before_transcribe')}
/>
)}
+ {additionalSections.includes('caption.keep_timestamps') && (
+ setJobConfig(value, 'config.process[0].caption.keep_timestamps')}
+ />
+ )}
{additionalSections.includes('caption.thinking') && (