weight, gradio version, mono input

README.md

@@ -1,10 +1,10 @@
 ---
-title: De-limiter Demo
+title: De-Limiter Demo
 emoji: 🎶
 colorFrom: black
 colorTo: white
 sdk: gradio
-sdk_version: 3.39.0
+sdk_version: 4.36.1
 app_file: app.py
 pinned: true
 ---

app.py

@@ -50,11 +50,6 @@ def parallel_mix(input, output, mix_coefficient):
     return sr, input[1] * mix_coefficient + output[1] * (1 - mix_coefficient)
 
 
-def int16_to_float32(wav):
-    X = wav / 32768
-    return X
-
-
 def waveform_plot(input, output, prl_mix_ouptut, figsize_x=20, figsize_y=9):
     sr = 44100
     fig, ax = plt.subplots(
@@ -147,17 +142,12 @@ def main(input, mix_coefficient):
 
     meter = pyln.Meter(44100)
 
-    sr, track_audio = input
+    track_audio, sr = librosa.load(input, sr=44100, mono=False)
+    if len(track_audio.shape) == 1:  # mono
+        track_audio = np.stack([track_audio, track_audio], axis=0)
     orig_sr = copy.deepcopy(sr)
-    track_audio = track_audio.T
-    track_name = "gradio_demo"
 
-    if sr != 44100:
-        track_audio = int16_to_float32(track_audio)
-        track_audio = librosa.resample(
-            track_audio, orig_sr=sr, target_sr=44100, res_type="soxr_vhq"
-        )
-        sr = 44100
+    track_name = "gradio_demo"
 
     orig_audio = track_audio.copy()
 
@@ -176,14 +166,10 @@ def main(input, mix_coefficient):
         args, our_model, device, track_audio, track_name, meter, augmented_gain
     )
 
-    max_value = np.abs(estimates).max()
-    if max_value >= 1.0:
-        estimates = estimates / max_value
-        estimates = estimates * db2linear(-0.1, eps=0.0)
+    if np.abs(estimates).max() > 1.0:
+        estimates = estimates / np.abs(estimates).max()
         args.save_output_loudnorm = meter.integrated_loudness(estimates.T)
 
-    if orig_sr == 44100:
-        orig_audio = int16_to_float32(orig_audio)
     track_lufs = meter.integrated_loudness(orig_audio.T)
     augmented_gain = args.save_output_loudnorm - track_lufs
     orig_audio = orig_audio * db2linear(augmented_gain, eps=0.0)
@@ -220,7 +206,7 @@ with gr.Blocks() as demo:
                 </h1>
               </div>
               <p style="margin-bottom: 10px; font-size: 94%">
-                A demo for "Music De-limiter Networks via Sample-wise Gain Inversion" to appear in WASPAA 2023.<br>
+                A demo for "Music De-Limiter via Sample-wise Gain Inversion" to appear in WASPAA 2023.<br>
                 Upload a stereo music (tested with .wav, .mp3, .m4a) file and then press "De-limit" button to apply the De-limiter.<br>
                 The processing is based on 44.1kHz sample rate. Other sample rate will be automatically resampled to 44.1kHz.<br>
                 Since we use a CPU instead of a GPU, it may require a few seconds to minutes.<br>
@@ -233,74 +219,59 @@ with gr.Blocks() as demo:
             </div>
         """
     )
-    with gr.Row().style(mobile_collapse=False, equal_height=True):
+    with gr.Row():
         with gr.Column():
-            with gr.Box():
-                input_audio = gr.Audio(source="upload", label="De-limiter Input")
-                btn = gr.Button("De-limit")
+            input_audio = gr.Audio(type="filepath", label="De-limiter Input")
+            btn = gr.Button("De-Limit")
         with gr.Column():
-            with gr.Box():
-                loud_norm_input = gr.Audio(
-                    label="Loudness Normalized Input",
-                    show_download_button=True,
-                )
-            with gr.Box():
-                output_audio = gr.Audio(
-                    label="De-limiter Output",
-                    show_download_button=True,
-                )
-            with gr.Box():
-                output_audio_parallel = gr.Audio(
-                    label="Parallel Mix of the Input and its De-limiter Output",
-                    show_download_button=True,
-                )
-                slider = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    step=0.1,
-                    value=0.5,
-                    label="Parallel Mix Coefficient",
-                )
+            loud_norm_input = gr.Audio(
+                label="Loudness Normalized Input (-14LUFS)", show_download_button=True
+            )
+            output_audio = gr.Audio(
+                label="De-limiter Output", show_download_button=True
+            )
+            output_audio_parallel = gr.Audio(
+                label="Parallel Mix of the Input and its De-limiter Output",
+                show_download_button=True,
+            )
+            slider = gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.5,
+                label="Parallel Mix Coefficient",
+            )
             btn.click(
-                main,
+                fn=main,
                 inputs=[input_audio, slider],
                 outputs=[output_audio, loud_norm_input, output_audio_parallel],
             )
             slider.release(
-                parallel_mix,
+                fn=parallel_mix,
                 inputs=[loud_norm_input, output_audio, slider],
                 outputs=output_audio_parallel,
             )
-    with gr.Row().style(mobile_collapse=False, equal_height=True):
+    with gr.Row():
         with gr.Column():
-            with gr.Box():
-                plot = gr.Plot(label="Plots")
-                btn2 = gr.Button("Show Plots")
-                slider_plot_x = gr.Slider(
-                    minimum=1,
-                    maximum=100,
-                    step=1,
-                    value=20,
-                    label="Plot X-axis size",
-                )
-                slider_plot_y = gr.Slider(
-                    minimum=1,
-                    maximum=30,
-                    step=1,
-                    value=9,
-                    label="Plot Y-axis size",
-                )
-                btn2.click(
-                    waveform_plot,
-                    inputs=[
-                        loud_norm_input,
-                        output_audio,
-                        output_audio_parallel,
-                        slider_plot_x,
-                        slider_plot_y,
-                    ],
-                    outputs=plot,
-                )
+            plot = gr.Plot(label="Plots")
+            btn2 = gr.Button("Show Plots")
+            slider_plot_x = gr.Slider(
+                minimum=1, maximum=100, step=1, value=20, label="Plot X-axis size"
+            )
+            slider_plot_y = gr.Slider(
+                minimum=1, maximum=30, step=1, value=9, label="Plot Y-axis size"
+            )
+            btn2.click(
+                fn=waveform_plot,
+                inputs=[
+                    loud_norm_input,
+                    output_audio,
+                    output_audio_parallel,
+                    slider_plot_x,
+                    slider_plot_y,
+                ],
+                outputs=plot,
+            )
 
 if __name__ == "__main__":
     demo.launch()

dataloader/delimit_dataset.py

@@ -15,11 +15,13 @@ from .dataset import (
     MusdbTrainDataset,
     MusdbValidDataset,
     apply_limitaug,
+    # apply_limitaug_loudnorm,
 )
 from utils import (
     load_wav_arbitrary_position_stereo,
     load_wav_specific_position_stereo,
     db2linear,
+    str2bool,
 )
 
 
@@ -213,8 +215,9 @@ class DelimitTrainDataset(MusdbTrainDataset):
         # apply linear mix over source index=0
         # and here, linear mixture is a target unlike in MusdbTrainDataset
         mixture = stems.sum(0)
+        # target_lufs = self.sample_target_lufs()
         mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
-        # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
+        # # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
 
         mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
         mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
@@ -273,10 +276,11 @@ class OzoneTrainDataset(DelimitTrainDataset):
         self.ozone_root = ozone_root
         self.use_fixed = use_fixed
         self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
-        self.list_train_random = glob.glob(
-            f"{self.ozone_root}/ozone_train_random/*.wav"
-        )
-        self.dict_train_random = {}
+        # self.list_train_random = glob.glob(
+        #     f"{self.ozone_root}/ozone_train_random/*.wav"
+        # )
+        # self.dict_train_random = {}
+        self.list_dict_train_random = []
 
         # Load information of pre-generated random training examples
         list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
@@ -286,34 +290,66 @@ class OzoneTrainDataset(DelimitTrainDataset):
                 reader = csv.reader(f)
                 next(reader)
                 for row in reader:
-                    self.dict_train_random[row[0]] = {
-                        "max_threshold": float(row[1]),
-                        "max_character": float(row[2]),
-                        "vocals": {
-                            "name": row[3],
-                            "start_sec": float(row[4]),
-                            "gain": float(row[5]),
-                            "channelswap": bool(row[6]),
-                        },
-                        "bass": {
-                            "name": row[7],
-                            "start_sec": float(row[8]),
-                            "gain": float(row[9]),
-                            "channelswap": bool(row[10]),
-                        },
-                        "drums": {
-                            "name": row[11],
-                            "start_sec": float(row[12]),
-                            "gain": float(row[13]),
-                            "channelswap": bool(row[14]),
-                        },
-                        "other": {
-                            "name": row[15],
-                            "start_sec": float(row[16]),
-                            "gain": float(row[17]),
-                            "channelswap": bool(row[18]),
-                        },
-                    }
+                    self.list_dict_train_random.append(
+                        {
+                            row[0]: {
+                                "max_threshold": float(row[1]),
+                                "max_character": float(row[2]),
+                                "vocals": {
+                                    "name": row[3],
+                                    "start_sec": float(row[4]),
+                                    "gain": float(row[5]),
+                                    "channelswap": str2bool(row[6]),
+                                },
+                                "bass": {
+                                    "name": row[7],
+                                    "start_sec": float(row[8]),
+                                    "gain": float(row[9]),
+                                    "channelswap": str2bool(row[10]),
+                                },
+                                "drums": {
+                                    "name": row[11],
+                                    "start_sec": float(row[12]),
+                                    "gain": float(row[13]),
+                                    "channelswap": str2bool(row[14]),
+                                },
+                                "other": {
+                                    "name": row[15],
+                                    "start_sec": float(row[16]),
+                                    "gain": float(row[17]),
+                                    "channelswap": str2bool(row[18]),
+                                },
+                            }
+                        }
+                    )
+                    # self.dict_train_random[row[0]] = {
+                    #     "max_threshold": float(row[1]),
+                    #     "max_character": float(row[2]),
+                    #     "vocals": {
+                    #         "name": row[3],
+                    #         "start_sec": float(row[4]),
+                    #         "gain": float(row[5]),
+                    #         "channelswap": str2bool(row[6]),
+                    #     },
+                    #     "bass": {
+                    #         "name": row[7],
+                    #         "start_sec": float(row[8]),
+                    #         "gain": float(row[9]),
+                    #         "channelswap": str2bool(row[10]),
+                    #     },
+                    #     "drums": {
+                    #         "name": row[11],
+                    #         "start_sec": float(row[12]),
+                    #         "gain": float(row[13]),
+                    #         "channelswap": str2bool(row[14]),
+                    #     },
+                    #     "other": {
+                    #         "name": row[15],
+                    #         "start_sec": float(row[16]),
+                    #         "gain": float(row[17]),
+                    #         "channelswap": str2bool(row[18]),
+                    #     },
+                    # }
 
     def __getitem__(self, idx):
         use_fixed_prob = random.random()
@@ -341,16 +377,24 @@ class OzoneTrainDataset(DelimitTrainDataset):
         else:
             # Random examples
             # Load mixture_limited (pre-generated)
-            audio_path = random.choice(self.list_train_random)
-            seg_name = os.path.basename(audio_path).replace(".wav", "")
+            # audio_path = random.choice(self.list_train_random)
+
+            dict_seg = random.choice(self.list_dict_train_random)
+            seg_name = list(dict_seg.keys())[0]
+            audio_path = f"{self.ozone_root}/ozone_train_random/{seg_name}.wav"
+            dict_seg_info = dict_seg[seg_name]
+
+            # seg_name = os.path.basename(audio_path).replace(".wav", "")
             mixture_limited, sr = librosa.load(
                 audio_path, sr=self.sample_rate, mono=False
             )
 
             # Load mixture_unlimited (from the original musdb18, using metadata)
             audio_sources = []
+            # dict_seg_info = self.dict_train_random[seg_name]
+
             for source in self.sources:
-                dict_seg_info = self.dict_train_random[seg_name]
+                # dict_seg_info = self.dict_train_random[seg_name]
                 dict_seg_source_info = dict_seg_info[source]
                 audio_path = (
                     f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
@@ -380,6 +424,9 @@ class OzoneTrainDataset(DelimitTrainDataset):
 
         return mixture_limited, mixture_loudnorm
 
+    # def __len__(self):
+    #     return 100
+
 
 class DelimitValidDataset(MusdbValidDataset):
     def __init__(
@@ -387,7 +434,7 @@ class DelimitValidDataset(MusdbValidDataset):
         target: str = "vocals",
         root: str = None,
         delimit_valid_root: str = None,
-        valid_target_lufs: float = -8.05,  # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
+        valid_target_lufs: float = -8.05,  # From the Table 1 of the paper, the average loudness of commerical music.
         target_loudnorm_lufs: float = -14.0,
         delimit_valid_L_root: str = None,  # This will be used when using the target as compressed (normal_L) mixture.
         use_custom_limiter: bool = False,
@@ -556,6 +603,7 @@ class OzoneValidDataset(MusdbValidDataset):
         song_name = os.path.basename(track_path)
         for k, source in enumerate(self.sources):
             audio_path = f"{track_path}/{source}.wav"
+            # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
             audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
             audio_sources.append(audio)
 

weight/all.json

@@ -1,21 +1,5 @@
 {
     "args": {
-        "classifier_params": {
-            "chosen_source_mean": 0.7,
-            "chosen_source_std": 0.15,
-            "classifier_activation": "softmax",
-            "classifier_n_classes": 4,
-            "classifier_n_srcs": 4,
-            "freeze_when_mixit": true,
-            "melspec_power": 2.0,
-            "model_name": "hrnet_w18_small",
-            "n_mels": 128,
-            "other_source_mean": 0.3,
-            "other_source_std": 0.15,
-            "pretrained_model": false,
-            "use_one_source_prob": 0.2,
-            "use_stereo": true
-        },
         "conv_tasnet_params": {
             "bn_chan": 128,
             "decoder_activation": "sigmoid",
@@ -26,6 +10,7 @@
             "n_blocks": 5,
             "n_filters": 512,
             "n_repeats": 2,
+            "norm_type": {},
             "skip_chan": 128,
             "stride": 64
         },
@@ -54,12 +39,12 @@
             "continual_train": false,
             "delimit_valid_L_root": null,
             "delimit_valid_root": null,
-            "exp_name": "convtasnet_35",
-            "output_directory": "/data2/personal/jeon/delimit/results",
-            "ozone_root": "/data5/personal/jeon/delimit/data",
+            "exp_name": "delimit_6_s",
+            "output_directory": "/path/to/delimit/results",
+            "ozone_root": "/path/to/delimit/data",
             "pretrained_classifier": null,
             "resume": null,
-            "root": "/data1/Music/musdb18hq"
+            "root": "/path/to/musdb18hq"
         },
         "gpu": 0,
         "hyperparams": {
@@ -75,24 +60,6 @@
             "patience": 50,
             "weight_decay": 0.01
         },
-        "img_check": "/data2/personal/jeon/delimit/results/img_check/convtasnet_35",
-        "invest_unet_params": {
-            "bn_factor": 16,
-            "f_down_layers": null,
-            "first_conv_activation": "relu",
-            "input_channels": 4,
-            "internal_channels": 24,
-            "kernel_size_f": 3,
-            "kernel_size_t": 3,
-            "last_activation": "identity",
-            "min_bn_units": 16,
-            "n_blocks": 7,
-            "n_internal_layers": 5,
-            "t_down_layers": null,
-            "tfc_tdf_activation": "relu",
-            "tfc_tdf_bias": true,
-            "tif_init_mode": null
-        },
         "model_loss_params": {
             "architecture": "conv_tasnet_mask_on_output",
             "efficient_mixit_threshold": null,
@@ -110,7 +77,7 @@
             ]
         },
         "ngpus_per_node": 1,
-        "output": "/data2/personal/jeon/delimit/results/checkpoint/convtasnet_35",
+        "output": "/path/to/delimit/results/checkpoint/delimit_6_s",
         "resume": {},
         "sample_rate": {},
         "sys_params": {
@@ -126,832 +93,815 @@
             "dataset": "delimit",
             "target": "all",
             "train": true
-        },
-        "umx_params": {
-            "activation": "relu",
-            "dropout_rate": 0.05,
-            "hidden_size": 512,
-            "instead_tanh_activation": "tanh",
-            "lstm_dropout_rate": 0.4,
-            "nb_layers": 3,
-            "normalization": "bn",
-            "umx_get_statistics": false
-        },
-        "wandb_params": {
-            "entity": "vinyne",
-            "project": "delimit",
-            "rerun_id": null,
-            "sweep": false,
-            "use_wandb": true
         }
     },
-    "best_epoch": 183,
-    "best_loss": -14.165373802185059,
+    "best_epoch": 196,
+    "best_loss": -14.249421119689941,
     "epochs_trained": 200,
-    "num_bad_epochs": 17,
+    "num_bad_epochs": 4,
     "train_loss_history": [
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