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@@ -33,3 +33,22 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech_iSRNet/examples/ex_92/s0.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech_iSRNet/examples/ex_92/s0_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech_iSRNet/examples/ex_92/s1.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_1/s0.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech_iSRNet/examples/ex_92/s1_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_1/s0_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_1/s1_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_1/mixture.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_1/s1.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_28/s0_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_28/s0.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_28/mixture.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_28/s1.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_28/s1_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_3/mixture.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_3/s1.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_3/s0_estimate.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_3/s0.wav filter=lfs diff=lfs merge=lfs -text
+MedleyVox-MultiSinger/eval_results/singing_librispeech/examples/ex_3/s1_estimate.wav filter=lfs diff=lfs merge=lfs -text

Dereverb-Echo-MelBand-Sucial/scripts/create_reverb_delay.py

@@ -0,0 +1,76 @@
+import os
+import argparse
+import librosa
+import numpy as np
+import soundfile as sf
+from pedalboard import Pedalboard, Reverb, Delay, HighpassFilter, LowpassFilter
+from random import uniform
+from tqdm import tqdm
+
+
+def random_effect(audio, sr):
+    reverb = Pedalboard([
+        Delay(
+            delay_seconds=uniform(0.001, 0.100), 
+            feedback=0.0, 
+            mix=1.0
+        ),
+        Reverb(
+            room_size=uniform(0.1, 0.8),
+            damping=uniform(0.1, 0.8),
+            wet_level=1.0, 
+            dry_level=0.0, 
+            width=uniform(0.6, 1.0)
+        ),
+        HighpassFilter(cutoff_frequency_hz=uniform(100, 1000)),
+        LowpassFilter(cutoff_frequency_hz=uniform(4000, 12000))
+    ])
+
+    delay = Pedalboard([
+        Delay(
+            delay_seconds=uniform(0.05, 0.500),
+            feedback=uniform(0.1, 0.5),
+            mix=1.0
+        ),
+        Reverb(
+            room_size=uniform(0.05, 0.3),
+            damping=uniform(0.1, 0.8),
+            wet_level=0.2,
+            dry_level=0.8,
+            width=uniform(0.6, 1.0)
+        ),
+        HighpassFilter(cutoff_frequency_hz=uniform(100, 1000)),
+        LowpassFilter(cutoff_frequency_hz=uniform(3000, 10000))
+    ])
+
+    effect = uniform(0.1, 0.4) * reverb(audio, sr) + uniform(0.1, 0.4) * delay(audio, sr)
+    mix = effect + audio
+
+    return mix, effect
+
+
+if __name__ == '__main__':
+    argparser = argparse.ArgumentParser(description='Add random reverb and delay effects to an audio file.')
+    argparser.add_argument('-i', '--input_folder', type=str, default="train", help='Path to the input audio file.')
+    argparser.add_argument('-o', '--output_folder', type=str, default="dataset_train", help='Path to the output audio file.')
+    args = argparser.parse_args()
+
+    index = 1
+    sr = 44100
+    for file in tqdm(os.listdir(args.input_folder)):
+        try:
+            audio, _ = librosa.load(os.path.join(args.input_folder, file), sr=sr)
+            if len(audio.shape) == 1:
+                audio = np.stack([audio, audio], axis=1)
+            effect = random_effect(audio.T, sr)
+        except:
+            print(f"Failed to process file: {file}")
+            continue
+
+        os.makedirs(os.path.join(args.output_folder, str(index)), exist_ok=True)
+
+        sf.write(os.path.join(args.output_folder, str(index), "mixture.wav"), effect[0].T, sr, subtype='PCM_16')
+        sf.write(os.path.join(args.output_folder, str(index), "other.wav"), effect[1].T, sr, subtype='PCM_16')
+        sf.write(os.path.join(args.output_folder, str(index), "dry.wav"), audio, sr, subtype='PCM_16')
+
+        index += 1

Dereverb-Echo-MelBand-Sucial/scripts/create_reverb_delay_multi_threads.py

@@ -0,0 +1,71 @@
+import os
+import argparse
+import librosa
+import numpy as np
+import soundfile as sf
+from pedalboard import Pedalboard, Reverb, Delay, HighpassFilter, LowpassFilter
+from random import uniform
+from tqdm import tqdm
+from concurrent.futures import ThreadPoolExecutor
+
+def random_effect(audio, sr):
+    reverb = Pedalboard([
+        Delay(
+            delay_seconds=uniform(0.001, 0.100), 
+            feedback=0.0,
+            mix=1.0
+        ),
+        Reverb(
+            room_size=uniform(0.7, 1.0),
+            damping=uniform(0.7, 1.0),
+            wet_level=1.0, 
+            dry_level=0.0, 
+            width=uniform(0.7, 1.0)
+        ),
+        HighpassFilter(cutoff_frequency_hz=uniform(100, 800)),
+        LowpassFilter(cutoff_frequency_hz=uniform(4000, 15000))
+    ])
+
+    effect = uniform(0.3, 0.6) * reverb(audio, sr)
+    mix = effect + audio
+
+    return mix, effect
+
+def process_file(file, input_folder, output_folder, index, sr):
+    try:
+        audio, _ = librosa.load(os.path.join(input_folder, file), sr=sr)
+        if len(audio.shape) == 1:
+            audio = np.stack([audio, audio], axis=1)
+        effect = random_effect(audio.T, sr)
+    except Exception as e:
+        print(f"Failed to process file: {file}. Error: {e}")
+        return False
+
+    output_path = os.path.join(output_folder, str(index))
+    os.makedirs(output_path, exist_ok=True)
+
+    try:
+        sf.write(os.path.join(output_path, "mixture.wav"), effect[0].T, sr, subtype='PCM_16')
+        sf.write(os.path.join(output_path, "other.wav"), effect[1].T, sr, subtype='PCM_16')
+        sf.write(os.path.join(output_path, "dry.wav"), audio, sr, subtype='PCM_16')
+        os.remove(os.path.join(input_folder, file))
+    except Exception as e:
+        print(f"Failed to save file for {file}. Error: {e}")
+        return False
+
+    return True
+
+if __name__ == '__main__':
+    argparser = argparse.ArgumentParser(description='Add random reverb and delay effects to audio files using multithreading.')
+    argparser.add_argument('-i', '--input_folder', type=str, default="trainset", help='Path to the input folder containing audio files.')
+    argparser.add_argument('-o', '--output_folder', type=str, default="train2", help='Path to the output folder for processed audio files.')
+    argparser.add_argument('-t', '--threads', type=int, default=32, help='Number of threads to use for processing.')
+    args = argparser.parse_args()
+
+    sr = 44100
+    input_files = os.listdir(args.input_folder)
+
+    with ThreadPoolExecutor(max_workers=args.threads) as executor:
+        futures = {executor.submit(process_file, file, args.input_folder, args.output_folder, index, sr): file for index, file in enumerate(input_files, start=1)}
+        for future in tqdm(futures, total=len(input_files)):
+            future.result()

Dereverb-Echo-MelBand-Sucial/scripts/model_fusion.py

@@ -0,0 +1,13 @@
+import torch
+
+model_1 = torch.load('model_1.ckpt', map_location='cpu')
+model_2 = torch.load('model_2.ckpt', map_location='cpu')
+model_3 = torch.load('model_3.ckpt', map_location='cpu')
+
+# Combine the models
+fused_weights = {}
+for key in model_1.keys():
+    fused_weights[key] = 0.5 * model_1[key] + 0.25 * model_2[key] + 0.25 * model_3[key]
+
+# Save the fused model
+torch.save(fused_weights, 'fused_model.ckpt')

Dereverb-MelBand-Anvuew/archive only/8_256_6/deverb_mel_band_roformer_8_256dim_6depth.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3469d6b383b7ae0fff8aa6cb292eedf1eec132d3a960cadeecdec145123566d
+size 438988833

Dereverb-MelBand-Anvuew/archive only/8_256_6/deverb_mel_band_roformer_8_256dim_6depth.yaml

@@ -0,0 +1,77 @@
+audio:
+  chunk_size: 352768
+  dim_f: 1024
+  dim_t: 256
+  hop_length: 512
+  n_fft: 2048
+  num_channels: 2
+  sample_rate: 44100
+  min_mean_abs: 0.001
+
+model:
+  dim: 256
+  depth: 6
+  stereo: true
+  num_stems: 1
+  time_transformer_depth: 1
+  freq_transformer_depth: 1
+  linear_transformer_depth: 0
+  num_bands: 60
+  dim_head: 64
+  heads: 8
+  attn_dropout: 0.1
+  ff_dropout: 0.1
+  flash_attn: True
+  dim_freqs_in: 1025
+  sample_rate: 44100  # needed for mel filter bank from librosa
+  stft_n_fft: 2048
+  stft_hop_length: 512
+  stft_win_length: 2048
+  stft_normalized: False
+  mask_estimator_depth: 2
+  multi_stft_resolution_loss_weight: 1.0
+  multi_stft_resolutions_window_sizes: !!python/tuple
+  - 4096
+  - 2048
+  - 1024
+  - 512
+  - 256
+  multi_stft_hop_size: 147
+  multi_stft_normalized: False
+
+training:
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  grad_clip: 0
+  instruments:
+  - noreverb
+  - reverb
+  lr: 5.0e-05
+  patience: 2
+  reduce_factor: 0.95
+  target_instrument: noreverb
+  num_epochs: 1000
+  num_steps: 40000
+  q: 0.95
+  coarse_loss_clip: true
+  ema_momentum: 0.999
+  optimizer: adam
+  other_fix: false # it's needed for checking on multisong dataset if other is actually instrumental
+  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true
+
+augmentations:
+  enable: true # enable or disable all augmentations (to fast disable if needed)
+  loudness: true # randomly change loudness of each stem on the range (loudness_min; loudness_max)
+  loudness_min: 0.5
+  loudness_max: 1.5
+  mixup: false # mix several stems of same type with some probability (only works for dataset types: 1, 2, 3)
+  mixup_probs: !!python/tuple # 2 additional stems of the same type (1st with prob 0.2, 2nd with prob 0.02)
+    - 0.2
+    - 0.02
+  mixup_loudness_min: 0.5
+  mixup_loudness_max: 1.5
+
+inference:
+  batch_size: 1
+  dim_t: 256
+  num_overlap: 4

Dereverb-MelBand-Anvuew/archive only/8_512_12/deverb_mel_band_roformer_8_512dim_12depth.yaml

@@ -0,0 +1,77 @@
+audio:
+  chunk_size: 352768
+  dim_f: 1024
+  dim_t: 801
+  hop_length: 441
+  n_fft: 2048
+  num_channels: 2
+  sample_rate: 44100
+  min_mean_abs: 0.001
+
+model:
+  dim: 512
+  depth: 12
+  stereo: true
+  num_stems: 1
+  time_transformer_depth: 1
+  freq_transformer_depth: 1
+  linear_transformer_depth: 0
+  num_bands: 60
+  dim_head: 64
+  heads: 8
+  attn_dropout: 0.1
+  ff_dropout: 0.1
+  flash_attn: True
+  dim_freqs_in: 1025
+  sample_rate: 44100  # needed for mel filter bank from librosa
+  stft_n_fft: 2048
+  stft_hop_length: 512
+  stft_win_length: 2048
+  stft_normalized: False
+  mask_estimator_depth: 2
+  multi_stft_resolution_loss_weight: 1.0
+  multi_stft_resolutions_window_sizes: !!python/tuple
+  - 4096
+  - 2048
+  - 1024
+  - 512
+  - 256
+  multi_stft_hop_size: 147
+  multi_stft_normalized: False
+
+training:
+  batch_size: 2
+  gradient_accumulation_steps: 1
+  grad_clip: 0
+  instruments:
+  - noreverb
+  - reverb
+  lr: 5.0e-05
+  patience: 2
+  reduce_factor: 0.95
+  target_instrument: noreverb
+  num_epochs: 1000
+  num_steps: 1900
+  q: 0.95
+  coarse_loss_clip: true
+  ema_momentum: 0.999
+  optimizer: adam
+  other_fix: false # it's needed for checking on multisong dataset if other is actually instrumental
+  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true
+
+augmentations:
+  enable: true # enable or disable all augmentations (to fast disable if needed)
+  loudness: true # randomly change loudness of each stem on the range (loudness_min; loudness_max)
+  loudness_min: 0.5
+  loudness_max: 1.5
+  mixup: false # mix several stems of same type with some probability (only works for dataset types: 1, 2, 3)
+  mixup_probs: !!python/tuple # 2 additional stems of the same type (1st with prob 0.2, 2nd with prob 0.02)
+    - 0.2
+    - 0.02
+  mixup_loudness_min: 0.5
+  mixup_loudness_max: 1.5
+
+inference:
+  batch_size: 1
+  dim_t: 801
+  num_overlap: 4

HuggingFace_Repos/anvuew/BS-RoFormer/.gitattributes

@@ -0,0 +1,35 @@
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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
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+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

HuggingFace_Repos/anvuew/BS-RoFormer/README.md

@@ -0,0 +1,5 @@
+---
+license: gpl-3.0
+---
+
+dataset by [bascurtiz](https://github.com/bascurtiz)

HuggingFace_Repos/anvuew/BS-RoFormer/bs_roformer_anvuew_sdr_12.45.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d0f72ad0ac4154f5f4c3cdf230880bbb9a5bd01dc610241a2abfe787f0d7784
+size 204485563

HuggingFace_Repos/anvuew/BS-RoFormer/config.yaml

@@ -0,0 +1,129 @@
+audio:
+  chunk_size: 960000
+  dim_f: 1024
+  dim_t: 801 # don't work (use in model)
+  hop_length: 441 # don't work (use in model)
+  n_fft: 2048
+  num_channels: 2
+  sample_rate: 44100
+  min_mean_abs: 0.0001
+
+model:
+  dim: 256
+  depth: 12
+  stereo: true
+  num_stems: 1
+  time_transformer_depth: 1
+  freq_transformer_depth: 1
+  linear_transformer_depth: 0
+  freqs_per_bands: !!python/tuple
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
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+    - 2
+    - 2
+    - 2
+    - 2
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 4
+    - 12
+    - 12
+    - 12
+    - 12
+    - 12
+    - 12
+    - 12
+    - 12
+    - 24
+    - 24
+    - 24
+    - 24
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+    - 48
+    - 48
+    - 48
+    - 48
+    - 48
+    - 48
+    - 48
+    - 48
+    - 128
+    - 129
+  dim_head: 64
+  heads: 8
+  attn_dropout: 0.0
+  ff_dropout: 0.0
+  flash_attn: true
+  dim_freqs_in: 1025
+  stft_n_fft: 2048
+  stft_hop_length: 512
+  stft_win_length: 2048
+  stft_normalized: false
+  mask_estimator_depth: 2
+  multi_stft_resolution_loss_weight: 1.0
+  multi_stft_resolutions_window_sizes: !!python/tuple
+  - 4096
+  - 2048
+  - 1024
+  - 512
+  - 256
+  multi_stft_hop_size: 147
+  multi_stft_normalized: False
+  mlp_expansion_factor: 4
+  use_torch_checkpoint: True
+  skip_connection: False
+
+
+training:
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  grad_clip: 0
+  instruments: ['vocals', 'instrument']
+  lr: 1.0e-5
+  patience: 5
+  reduce_factor: 0.9
+  target_instrument: vocals
+  num_epochs: 1000
+  num_steps: 1000
+  q: 0.95
+  coarse_loss_clip: true
+  ema_momentum: 0.999
+  optimizer: adam
+  other_fix: false # it's needed for checking on multisong dataset if other is actually instrumental
+  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true
+
+
+inference:
+  batch_size: 2
+  dim_t: 1876
+  num_overlap: 4

HuggingFace_Repos/anvuew/dereverb_mel_band_roformer/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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HuggingFace_Repos/anvuew/dereverb_mel_band_roformer/README.md

@@ -0,0 +1,15 @@
+---
+license: gpl-3.0
+---
+
+专用于人声的去混响模型，适用于 https://github.com/ZFTurbo/Music-Source-Separation-Training
+
+微调自[kim的人声乐器分离模型](https://huggingface.co/KimberleyJSN/melbandroformer)，所以保留了一些分离人声与乐器的能力
+
+训练数据的干声为单声道单人演唱/说话，所以模型会尽可能使分离后的干声为单声道，导致在分离一些人声为立体声或者经过叠轨等处理的音频时可能会出问题，`dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt`是使用为这些情况的样本挑出来的`dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt`训练中期的权重
+
+`dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt`与`dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt`两个权重由于训练代码[bug](https://github.com/ZFTurbo/Music-Source-Separation-Training/commit/0ca5691f22ea71d9afe297926d6e1517cdb38e55)，在训练时混响与人声实际并未对齐，所以这两个模型表现出一定程度的去除人声与乐器分离后的残留（通常是弦乐）以及部分和声（非中央声道）的能力
+
+`dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt`则因修复了对齐bug，去混响的能力更强了，但是去人声乐器分离残留以及和声的能力下降了
+
+

HuggingFace_Repos/anvuew/dereverb_mel_band_roformer/archive only/8_256_6/deverb_mel_band_roformer_8_256dim_6depth.yaml

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+audio:
+  chunk_size: 352768
+  dim_f: 1024
+  dim_t: 256
+  hop_length: 512
+  n_fft: 2048
+  num_channels: 2
+  sample_rate: 44100
+  min_mean_abs: 0.001
+
+model:
+  dim: 256
+  depth: 6
+  stereo: true
+  num_stems: 1
+  time_transformer_depth: 1
+  freq_transformer_depth: 1
+  linear_transformer_depth: 0
+  num_bands: 60
+  dim_head: 64
+  heads: 8
+  attn_dropout: 0.1
+  ff_dropout: 0.1
+  flash_attn: True
+  dim_freqs_in: 1025
+  sample_rate: 44100  # needed for mel filter bank from librosa
+  stft_n_fft: 2048
+  stft_hop_length: 512
+  stft_win_length: 2048
+  stft_normalized: False
+  mask_estimator_depth: 2
+  multi_stft_resolution_loss_weight: 1.0
+  multi_stft_resolutions_window_sizes: !!python/tuple
+  - 4096
+  - 2048
+  - 1024
+  - 512
+  - 256
+  multi_stft_hop_size: 147
+  multi_stft_normalized: False
+
+training:
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  grad_clip: 0
+  instruments:
+  - noreverb
+  - reverb
+  lr: 5.0e-05
+  patience: 2
+  reduce_factor: 0.95
+  target_instrument: noreverb
+  num_epochs: 1000
+  num_steps: 40000
+  q: 0.95
+  coarse_loss_clip: true
+  ema_momentum: 0.999
+  optimizer: adam
+  other_fix: false # it's needed for checking on multisong dataset if other is actually instrumental
+  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true
+
+augmentations:
+  enable: true # enable or disable all augmentations (to fast disable if needed)
+  loudness: true # randomly change loudness of each stem on the range (loudness_min; loudness_max)
+  loudness_min: 0.5
+  loudness_max: 1.5
+  mixup: false # mix several stems of same type with some probability (only works for dataset types: 1, 2, 3)
+  mixup_probs: !!python/tuple # 2 additional stems of the same type (1st with prob 0.2, 2nd with prob 0.02)
+    - 0.2
+    - 0.02
+  mixup_loudness_min: 0.5
+  mixup_loudness_max: 1.5
+
+inference:
+  batch_size: 1
+  dim_t: 256
+  num_overlap: 4

HuggingFace_Repos/anvuew/dereverb_mel_band_roformer/archive only/8_512_12/deverb_mel_band_roformer_8_512dim_12depth.yaml

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+audio:
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+  dim_f: 1024
+  dim_t: 801
+  hop_length: 441
+  n_fft: 2048
+  num_channels: 2
+  sample_rate: 44100
+  min_mean_abs: 0.001
+
+model:
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+  depth: 12
+  stereo: true
+  num_stems: 1
+  time_transformer_depth: 1
+  freq_transformer_depth: 1
+  linear_transformer_depth: 0
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+  heads: 8
+  attn_dropout: 0.1
+  ff_dropout: 0.1
+  flash_attn: True
+  dim_freqs_in: 1025
+  sample_rate: 44100  # needed for mel filter bank from librosa
+  stft_n_fft: 2048
+  stft_hop_length: 512
+  stft_win_length: 2048
+  stft_normalized: False
+  mask_estimator_depth: 2
+  multi_stft_resolution_loss_weight: 1.0
+  multi_stft_resolutions_window_sizes: !!python/tuple
+  - 4096
+  - 2048
+  - 1024
+  - 512
+  - 256
+  multi_stft_hop_size: 147
+  multi_stft_normalized: False
+
+training:
+  batch_size: 2
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+  grad_clip: 0
+  instruments:
+  - noreverb
+  - reverb
+  lr: 5.0e-05
+  patience: 2
+  reduce_factor: 0.95
+  target_instrument: noreverb
+  num_epochs: 1000
+  num_steps: 1900
+  q: 0.95
+  coarse_loss_clip: true
+  ema_momentum: 0.999
+  optimizer: adam
+  other_fix: false # it's needed for checking on multisong dataset if other is actually instrumental
+  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true
+
+augmentations:
+  enable: true # enable or disable all augmentations (to fast disable if needed)
+  loudness: true # randomly change loudness of each stem on the range (loudness_min; loudness_max)
+  loudness_min: 0.5
+  loudness_max: 1.5
+  mixup: false # mix several stems of same type with some probability (only works for dataset types: 1, 2, 3)
+  mixup_probs: !!python/tuple # 2 additional stems of the same type (1st with prob 0.2, 2nd with prob 0.02)
+    - 0.2
+    - 0.02
+  mixup_loudness_min: 0.5
+  mixup_loudness_max: 1.5
+
+inference:
+  batch_size: 1
+  dim_t: 801
+  num_overlap: 4

HuggingFace_Repos/anvuew/dereverb_mel_band_roformer/dereverb_mel_band_roformer_anvuew.yaml

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+audio:
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+  n_fft: 2048
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+model:
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+  flash_attn: True
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+  stft_win_length: 2048
+  stft_normalized: False
+  mask_estimator_depth: 2
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+  multi_stft_resolutions_window_sizes: !!python/tuple
+  - 4096
+  - 2048
+  - 1024
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+  - 256
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+  multi_stft_normalized: False
+
+training:
+  batch_size: 3
+  gradient_accumulation_steps: 1
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+  instruments:
+  - noreverb
+  - reverb
+  lr: 5.0e-05
+  patience: 2
+  reduce_factor: 0.95
+  target_instrument: noreverb
+  num_epochs: 1000
+  num_steps: 4000
+  q: 0.95
+  coarse_loss_clip: false
+  ema_momentum: 0.999
+  optimizer: adamw
+  other_fix: true # it's needed for checking on multisong dataset if other is actually instrumental
+  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true
+
+augmentations:
+  enable: true # enable or disable all augmentations (to fast disable if needed)
+  loudness: true # randomly change loudness of each stem on the range (loudness_min; loudness_max)
+  loudness_min: 0.1
+  loudness_max: 1.0
+  mixup: false # mix several stems of same type with some probability (only works for dataset types: 1, 2, 3)
+  mixup_probs: !!python/tuple # 2 additional stems of the same type (1st with prob 0.2, 2nd with prob 0.02)
+    - 0.2
+    - 0.02
+  mixup_loudness_min: 0.5
+  mixup_loudness_max: 1.5
+
+inference:
+  batch_size: 1
+  dim_t: 801
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HuggingFace_Repos/anvuew/karaoke_bs_roformer/README.md

@@ -0,0 +1,5 @@
+---
+license: gpl-3.0
+---
+
+dataset by [becruily](https://huggingface.co/becruily)

HuggingFace_Repos/anvuew/karaoke_bs_roformer/karaoke_bs_roformer_anvuew.ckpt

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HuggingFace_Repos/anvuew/karaoke_bs_roformer/karaoke_bs_roformer_anvuew.yaml

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MedleyVox-MultiSinger/eval_results/singing_librispeech/.~lock.all_metrics.csv#

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MedleyVox-MultiSinger/eval_results/singing_librispeech_iSRNet/examples/ex_16/metrics.json

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