1029 add octuple

Amadeus/symbolic_encoding/augmentor.py

@@ -22,6 +22,8 @@ class Augmentor:
       self.chord_idx = self.feature_list.index('chord')
   
   def _get_shift(self, segment):
+    if self.encoding_scheme == 'oct':
+      return 0
     # the pitch vocab has ignore token in 0 index
     if self.encoding_scheme == 'cp' or self.encoding_scheme == 'nb':
       pitch_mask = segment != 0

Amadeus/symbolic_encoding/compile_utils.py

@@ -73,7 +73,7 @@ class VanillaTransformer_compiler():
     for i in range(len(self.data_list)):
       tune_in_idx, tune_name = self.data_list[i]
       tune_in_idx = torch.LongTensor(tune_in_idx)
-      if self.encoding_scheme == 'remi' or self.encoding_scheme == 'cp':
+      if self.encoding_scheme == 'remi' or self.encoding_scheme == 'cp' or self.encoding_scheme == 'oct':
         eos_token = torch.LongTensor(self.eos_token)
       else:
         eos_token = torch.LongTensor(self.eos_token)
@@ -148,7 +148,7 @@ class VanillaTransformer_compiler():
     for i in range(len(self.data_list)):
       tune_in_idx, tune_name = self.data_list[i]
       tune_in_idx = torch.LongTensor(tune_in_idx)
-      if self.encoding_scheme == 'remi' or self.encoding_scheme == 'cp':
+      if self.encoding_scheme == 'remi' or self.encoding_scheme == 'cp' or self.encoding_scheme == 'oct':
         eos_token = torch.LongTensor(self.eos_token)
       else:
         eos_token = torch.LongTensor(self.eos_token)

Amadeus/symbolic_encoding/data_utils.py

@@ -95,11 +95,6 @@ class TuneCompiler(Dataset):
       print(f"Error encoding caption for tune {tune_name}: {e}")
       encoded_caption = self.t5_tokenizer("No caption available", return_tensors='pt', padding='max_length', truncation=True, max_length=128)
     return segment, tensor_mask, tune_name, encoded_caption
-    if self.data_type == 'train':
-      augmented_segment = self.augmentor(segment)
-      return augmented_segment, tensor_mask, tune_name, encoded_caption
-    else:
-      return segment, tensor_mask, tune_name, encoded_caption
   
   def get_segments_with_tune_idx(self, tune_name, seg_order):
     '''
@@ -135,6 +130,7 @@ class IterTuneCompiler(IterableDataset):
     self.data_type = data_type
     self.augmentor = augmentor
     self.eos_token = vocab.eos_token
+    self.vocab = vocab
     self.compile_function = VanillaTransformer_compiler(
       data_list=self.data_list, 
       augmentor=self.augmentor, 
@@ -157,7 +153,7 @@ class IterTuneCompiler(IterableDataset):
         encoded_caption = self.t5_tokenizer(tune_name, return_tensors='pt', padding='max_length', truncation=True, max_length=128)
       except Exception as e:
         encoded_caption = self.t5_tokenizer("No caption available", return_tensors='pt', padding='max_length', truncation=True, max_length=128)
-      if self.data_type == 'train':
+      if self.data_type == 'train' and self.vocab.encoding_scheme != 'oct':
         segment = self.augmentor(segment)
       # use input_ids replace tune_name
       tune_name = encoded_caption['input_ids'][0]  # Use the input_ids from the encoded caption

Amadeus/symbolic_encoding/decoding_utils.py

@@ -1,13 +1,17 @@
+import re
 import os, sys
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 from collections import defaultdict
 
+import torch
 from music21 import converter
 import muspy
 import miditoolkit
 from miditoolkit.midi.containers import Marker, Instrument, TempoChange, Note, TimeSignature
+from symusic import Score
+from miditok import Octuple, TokenizerConfig
 
 from .midi2audio import FluidSynth
 from data_representation.constants import PROGRAM_INSTRUMENT_MAP
@@ -400,5 +404,62 @@ class MidiDecoder4NB(MidiDecoder4REMI):
         music_path = os.path.join(music_path, output_path.split('/')[-1].replace('.mid', '.wav'))
       midi_obj.dump(output_path)
       # save_pianoroll_image_from_midi(output_path, output_path.replace('.mid', '.png'))
-      save_wav_from_midi_fluidsynth(output_path, music_path, gain=self.gain)
+      # save_wav_from_midi_fluidsynth(output_path, music_path, gain=self.gain)
     return midi_obj
+  
+class MidiDecoder4Octuple(MidiDecoder4REMI):
+  def __init__(self, vocab, in_beat_resolution, dataset_name):
+    super().__init__(vocab, in_beat_resolution, dataset_name)
+
+
+
+  def remove_rows_with_exact_0_1_2_3(self, t: torch.Tensor) -> torch.Tensor:
+    """
+    输入:
+        t: torch.Tensor, 形状 (1, N, M)
+    功能:
+        删除包含独立元素 0, 1, 2, 3 的子tensor行
+    返回:
+        torch.Tensor, 同样保持 batch 维度 (1, N_filtered, M)
+    """
+    if t.dim() != 3:
+        raise ValueError("输入 tensor 必须是三维 (batch, seq_len, feature)")
+
+    # 构造一个 mask，True 表示该行不包含 0,1,2,3
+    exclude_vals = torch.tensor([0, 1, 2, 3], device=t.device)
+
+    # 判断每一行是否含有这些值
+    mask = ~((t[0][..., None] == exclude_vals).any(dim=(1, 2)))
+
+    # 过滤行并保留 batch 维
+    filtered_tensor = t[0][mask].unsqueeze(0)
+
+    return filtered_tensor
+  
+  def __call__(self, generated_output, output_path=None):
+    config = TokenizerConfig(
+        use_time_signatures=True,
+        use_tempos=True,
+        use_velocities=True,
+        use_programs=True,
+        remove_duplicated_notes=True,
+        delete_equal_successive_tempo_changes=True,
+        )
+    config.additional_params["max_bar_embedding"] = 512
+    tokenizer = Octuple(config)
+
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    # generated_output =  generated_output[:, 1:generated_output.shape[1]-1, :]  # remove sos token
+    generated_output = self.remove_rows_with_exact_0_1_2_3(generated_output)
+    print(output_path)
+    try:
+      tok_seq = tokenizer.decode(generated_output.squeeze(0).tolist())
+      tok_seq.dump_midi(output_path)
+    except Exception as e:
+      print(generated_output)
+      print(f"  × 生成 MIDI 文件时出错：{output_path} -> {e}")
+      tok_seq = None
+    
+    return tok_seq