re-organization

.gitignore

@@ -0,0 +1 @@
+__pycache__/

README.md

@@ -32,7 +32,7 @@ The input audio is 10s, containing baby coughing, hiccuping, and adult sneezing.
 The latent dimension size of $z$ is reduced to 8 using PCA for visualization.
 
 <p align="center">
-<img src="sanity_check_result_audiomae.png" alt="" width=100%>
+<img src=".fig/sanity_check_result_audiomae.png" alt="" width=100%>
 </p>
 
 The result shows that the presence of labeled sound is clearly captured in the 3rd principal component (PC). 

config.py

@@ -1,16 +1,16 @@
-from transformers import PretrainedConfig
-from typing import Tuple
-
-
-class AudioMAEConfig(PretrainedConfig):
-    model_type = "audiomae"
-
-    def __init__(self,
-                 img_size:Tuple[int,int]=(1024,128),
-                 in_chans:int=1,
-                 num_classes:int=0,
-                 **kwargs,):
-        super().__init__(**kwargs)
-        self.img_size = img_size
-        self.in_chans = in_chans
-        self.num_classes = num_classes
+from transformers import PretrainedConfig
+from typing import Tuple
+
+
+class AudioMAEConfig(PretrainedConfig):
+    model_type = "audiomae"
+
+    def __init__(self,
+                 img_size:Tuple[int,int]=(1024,128),
+                 in_chans:int=1,
+                 num_classes:int=0,
+                 **kwargs,):
+        super().__init__(**kwargs)
+        self.img_size = img_size
+        self.in_chans = in_chans
+        self.num_classes = num_classes

model.py

@@ -1,112 +1,112 @@
-import torch
-import torchaudio
-import torchaudio.transforms as transforms
-from torchaudio.compliance import kaldi
-
-from einops import rearrange
-
-from timm.models.vision_transformer import VisionTransformer
-from transformers import PreTrainedModel
-
-from config import AudioMAEConfig
-
-
-class AudioMAEEncoder(VisionTransformer):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        """
-        - img_size of (1024, 128) = (temporal_length, n_freq_bins) is fixed, as described in the paper
-        - AudoMAE accepts a mono-channel (i.e., in_chans=1)
-        """
-        self.MEAN = -4.2677393  # written on the paper
-        self.STD = 4.5689974  # written on the paper
-
-    def load_wav_file(self, file_path:str):
-        """
-        to use this, `torchaudio` and `ffmpeg` must be installed
-        - `ffmpeg` version must be >=4.4 and <7.
-        - `ffmpeg` installation by `conda install -c conda-forge ffmpeg==6.1.1`
-        """
-        audio, sample_rate = torchaudio.load(file_path)  # audio: (n_channels, length); 
-
-        # length clip
-        audio_len = audio.shape[-1] / sample_rate
-        if audio_len > 10.0:
-            print('[WARNING] AudioMAE only accepts audio length up to 10s. The audio frames exceeding 10s will be clipped.')
-
-        # Check if the audio has multiple channels
-        if audio.shape[0] > 1:
-            # Convert stereo audio to mono by taking the mean across channels
-            # AudioMAE accepts a mono channel.
-            audio = torch.mean(audio, dim=0, keepdim=True)
-
-        # resample the audio into 16khz
-        # AudioMAE accepts 16khz
-        if sample_rate != 16000:
-            converter = transforms.Resample(orig_freq=sample_rate, new_freq=16000)
-            audio = converter(audio)
-        return audio
-    
-    def waveform_to_melspec(self, waveform:torch.FloatTensor):
-        # Compute the Mel spectrogram using Kaldi-compatible features
-        # the parameters are chosen as described in the audioMAE paper (4.2 implementation details)
-        mel_spectrogram = kaldi.fbank(
-            waveform, 
-            num_mel_bins=128, 
-            frame_length=25.0, 
-            frame_shift=10.0, 
-            htk_compat=True, 
-            use_energy=False,
-            sample_frequency=16000, 
-            window_type='hanning',
-            dither=0.0
-        )
-        
-        # Ensure the output shape matches 1x1024x128 by padding or trimming the time dimension
-        expected_frames = 1024  # as described in the paper
-        current_frames = mel_spectrogram.shape[0]
-        if current_frames > expected_frames:
-            mel_spectrogram = mel_spectrogram[:expected_frames, :]
-        elif current_frames < expected_frames:
-            padding = expected_frames - current_frames
-            mel_spectrogram = torch.nn.functional.pad(mel_spectrogram, (0, 0,  # (left, right) for the 1st dim
-                                                                        0, padding),  # (left, right) for the 2nd dim
-                                                                        )
-            
-        # scale
-        # as in the AudioMAE implementation [REF: https://github.com/facebookresearch/AudioMAE/blob/bd60e29651285f80d32a6405082835ad26e6f19f/dataset.py#L300]
-        mel_spectrogram = (mel_spectrogram - self.MEAN) / (self.STD * 2)  # (length, n_freq_bins) = (1024, 128)
-        return mel_spectrogram
-
-    @torch.no_grad()
-    def encode(self, file_path:str):
-        self.eval()
-
-        waveform = self.load_wav_file(file_path)
-        melspec = self.waveform_to_melspec(waveform)  # (length, n_freq_bins) = (1024, 128)
-        melspec = melspec[None,None,:,:]  # (1, 1, length, n_freq_bins) = (1, 1, 1024, 128)
-        z = self.forward_features(melspec)  # (b, 1+n, d); d=768
-        z = z[:,1:,:]  # (b n d); remove [CLS], the class token
-
-        b, c, w, h = melspec.shape  # w: temporal dim; h:freq dim
-        wprime = round(w / self.patch_embed.patch_size[0])  # width in the latent space
-        hprime = round(h / self.patch_embed.patch_size[1])  # height in the latent space
-
-        # reconstruct the temporal and freq dims
-        z = rearrange(z, 'b (w h) d -> b d h w', h=hprime)  # (b d h' w')
-
-        # remove the batch dim
-        z = z[0]  # (d h' w')
-        return z  # (d h' w')
-
-
-
-class PretrainedAudioMAEEncoder(PreTrainedModel):
-    config_class = AudioMAEConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.encoder = AudioMAEEncoder(img_size=config.img_size, in_chans=config.in_chans, num_classes=config.num_classes)
-    
-    def forward(self, file_path:str):
-        return self.encoder.encode(file_path)  # (d h' w')
+import torch
+import torchaudio
+import torchaudio.transforms as transforms
+from torchaudio.compliance import kaldi
+
+from einops import rearrange
+
+from timm.models.vision_transformer import VisionTransformer
+from transformers import PreTrainedModel
+
+from config import AudioMAEConfig
+
+
+class AudioMAEEncoder(VisionTransformer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        """
+        - img_size of (1024, 128) = (temporal_length, n_freq_bins) is fixed, as described in the paper
+        - AudoMAE accepts a mono-channel (i.e., in_chans=1)
+        """
+        self.MEAN = -4.2677393  # written on the paper
+        self.STD = 4.5689974  # written on the paper
+
+    def load_wav_file(self, file_path:str):
+        """
+        to use this, `torchaudio` and `ffmpeg` must be installed
+        - `ffmpeg` version must be >=4.4 and <7.
+        - `ffmpeg` installation by `conda install -c conda-forge ffmpeg==6.1.1`
+        """
+        audio, sample_rate = torchaudio.load(file_path)  # audio: (n_channels, length); 
+
+        # length clip
+        audio_len = audio.shape[-1] / sample_rate
+        if audio_len > 10.0:
+            print('[WARNING] AudioMAE only accepts audio length up to 10s. The audio frames exceeding 10s will be clipped.')
+
+        # Check if the audio has multiple channels
+        if audio.shape[0] > 1:
+            # Convert stereo audio to mono by taking the mean across channels
+            # AudioMAE accepts a mono channel.
+            audio = torch.mean(audio, dim=0, keepdim=True)
+
+        # resample the audio into 16khz
+        # AudioMAE accepts 16khz
+        if sample_rate != 16000:
+            converter = transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+            audio = converter(audio)
+        return audio
+    
+    def waveform_to_melspec(self, waveform:torch.FloatTensor):
+        # Compute the Mel spectrogram using Kaldi-compatible features
+        # the parameters are chosen as described in the audioMAE paper (4.2 implementation details)
+        mel_spectrogram = kaldi.fbank(
+            waveform, 
+            num_mel_bins=128, 
+            frame_length=25.0, 
+            frame_shift=10.0, 
+            htk_compat=True, 
+            use_energy=False,
+            sample_frequency=16000, 
+            window_type='hanning',
+            dither=0.0
+        )
+        
+        # Ensure the output shape matches 1x1024x128 by padding or trimming the time dimension
+        expected_frames = 1024  # as described in the paper
+        current_frames = mel_spectrogram.shape[0]
+        if current_frames > expected_frames:
+            mel_spectrogram = mel_spectrogram[:expected_frames, :]
+        elif current_frames < expected_frames:
+            padding = expected_frames - current_frames
+            mel_spectrogram = torch.nn.functional.pad(mel_spectrogram, (0, 0,  # (left, right) for the 1st dim
+                                                                        0, padding),  # (left, right) for the 2nd dim
+                                                                        )
+            
+        # scale
+        # as in the AudioMAE implementation [REF: https://github.com/facebookresearch/AudioMAE/blob/bd60e29651285f80d32a6405082835ad26e6f19f/dataset.py#L300]
+        mel_spectrogram = (mel_spectrogram - self.MEAN) / (self.STD * 2)  # (length, n_freq_bins) = (1024, 128)
+        return mel_spectrogram
+
+    @torch.no_grad()
+    def encode(self, file_path:str):
+        self.eval()
+
+        waveform = self.load_wav_file(file_path)
+        melspec = self.waveform_to_melspec(waveform)  # (length, n_freq_bins) = (1024, 128)
+        melspec = melspec[None,None,:,:]  # (1, 1, length, n_freq_bins) = (1, 1, 1024, 128)
+        z = self.forward_features(melspec)  # (b, 1+n, d); d=768
+        z = z[:,1:,:]  # (b n d); remove [CLS], the class token
+
+        b, c, w, h = melspec.shape  # w: temporal dim; h:freq dim
+        wprime = round(w / self.patch_embed.patch_size[0])  # width in the latent space
+        hprime = round(h / self.patch_embed.patch_size[1])  # height in the latent space
+
+        # reconstruct the temporal and freq dims
+        z = rearrange(z, 'b (w h) d -> b d h w', h=hprime)  # (b d h' w')
+
+        # remove the batch dim
+        z = z[0]  # (d h' w')
+        return z  # (d h' w')
+
+
+
+class PretrainedAudioMAEEncoder(PreTrainedModel):
+    config_class = AudioMAEConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.encoder = AudioMAEEncoder(img_size=config.img_size, in_chans=config.in_chans, num_classes=config.num_classes)
+    
+    def forward(self, file_path:str):
+        return self.encoder.encode(file_path)  # (d h' w')