{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":91844,"databundleVersionId":11361821,"sourceType":"competition"}],"dockerImageVersionId":30919,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Extracting Non-Vocal Audio: A Step-by-Step Approach\nIn this approach, we start by obtaining raw audio data, which may contain multiple sound sources, including vocals and background elements. To analyze and extract non-vocal components effectively, we follow these steps:\n\n1. Acquire the Audio Data:\n   The process begins by loading the audio file into a suitable format, ensuring that it is properly preprocessed for segmentation. This step involves converting the audio to a consistent sample rate and channel format, making it ready for further analysis.\n2. Segment the Vocal Source:\n   Using a source separation technique, such as HDemucs, the vocal component is isolated from the rest of the audio. This step helps in distinguishing the speech or singing parts from the background sounds, such as instrumental music or environmental noise.\n\n3. Detect the First Non-Silent Index:\n   Once the vocal source has been separated, we analyze the remaining signal to determine where the first significant sound begins after any initial silence. This is achieved by scanning the waveform and identifying the first index where the amplitude surpasses a defined silence threshold.\n\n4. Extract the Non-Vocal Source:\n   Using the detected index, we extract the segment of the separated source that contains the desired non-vocal components. This ensures that any leading silence is removed, and only relevant background sounds or instrumental elements are retained for further processing or analysis.\n\n![Untitled 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"},"0bcd34b1-09ce-4980-8a83-d56ac1ca3c87.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Import Packages","metadata":{}},{"cell_type":"code","source":"from pathlib import Path\nimport numpy as np \nimport pandas as pd \n\nimport torch\nimport torchaudio\nfrom torchaudio.pipelines import HDEMUCS_HIGH_MUSDB_PLUS\nfrom torchaudio.utils import download_asset\nfrom torchaudio.transforms import Fade\n\nfrom librosa.effects import trim\nimport matplotlib.pyplot as plt\n\nfrom IPython.display import Audio","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-03-19T09:32:40.283135Z","iopub.execute_input":"2025-03-19T09:32:40.283473Z","iopub.status.idle":"2025-03-19T09:32:40.288128Z","shell.execute_reply.started":"2025-03-19T09:32:40.283447Z","shell.execute_reply":"2025-03-19T09:32:40.287061Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Data Paths","metadata":{}},{"cell_type":"code","source":"audio_root = Path('/kaggle/input/birdclef-2025/train_audio')\ntrain_df = pd.read_csv('/kaggle/input/birdclef-2025/train.csv')\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T09:34:17.312760Z","iopub.execute_input":"2025-03-19T09:34:17.313124Z","iopub.status.idle":"2025-03-19T09:34:17.402536Z","shell.execute_reply.started":"2025-03-19T09:34:17.313097Z","shell.execute_reply":"2025-03-19T09:34:17.401864Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Utility Functions","metadata":{}},{"cell_type":"code","source":"def plot_spectrogram(stft, title=\"Spectrogram\"):\n    magnitude = stft.abs()\n    spectrogram = 20 * torch.log10(magnitude + 1e-8).numpy()\n    _, axis = plt.subplots(1, 1)\n    axis.imshow(spectrogram, cmap=\"viridis\", vmin=-60, vmax=0, origin=\"lower\", aspect=\"auto\")\n    axis.set_title(title)\n    plt.tight_layout()\n\ndef extract_animal_segments(src, top_db=30):\n    _,index = trim(src, top_db=top_db)\n    return src[...,: index[0]], index[0]\n    \n\nN_FFT = 4096\nN_HOP = 4\nstft = torchaudio.transforms.Spectrogram(\n    n_fft=N_FFT,\n    hop_length=N_HOP,\n    power=None,\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T09:42:21.778310Z","iopub.execute_input":"2025-03-19T09:42:21.778629Z","iopub.status.idle":"2025-03-19T09:42:21.784466Z","shell.execute_reply.started":"2025-03-19T09:42:21.778602Z","shell.execute_reply":"2025-03-19T09:42:21.783466Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Define Segmentor Class","metadata":{}},{"cell_type":"code","source":"class HDemucsSegmentor:\n    def __init__(self, device, segment, overlap):\n        bundle = HDEMUCS_HIGH_MUSDB_PLUS\n        self.model = bundle.get_model()\n        self.model.to(device)\n        self.sample_rate = bundle.sample_rate\n        self.segment = segment\n        self.overlap = overlap\n        self.source = 3\n        \n    def forward(self, x, sample_rate):\n        assert len(x.shape) == 2\n\n        ref = x.mean(0)\n        x = (x - ref.mean()) / ref.std()  # normalization\n        \n        if x.shape[0] == 1:\n            x = x.repeat(2,1)\n            \n        x = x.unsqueeze(0)  # Add batch dimension\n        batch, channels, length = x.shape\n\n        chunk_len = int(self.sample_rate * self.segment * (1 + self.overlap))\n        start = 0\n        overlap_frames = int(self.overlap * self.sample_rate)\n        fade = Fade(fade_in_len=0, fade_out_len=overlap_frames, fade_shape=\"linear\")\n        final = torch.zeros(batch, channels, length, device=x.device)  # Use x.device\n\n        while start < length:\n            end = min(start + chunk_len, length)  # Ensure end does not exceed length\n            chunk = x[:, :, start:end]\n\n            with torch.no_grad():\n                out = self.model.forward(chunk)\n\n            out = fade(out)  # Apply fade\n            out = out[:, self.source, :, :]  # Select the desired source\n\n            out_len = out.shape[-1]  # Get output length\n            final_len = final[:, :, start:end].shape[-1]  # Get final segment length\n\n            if out_len != final_len:\n                out = out[:, :, :final_len]  # Trim to match\n\n            final[:, :, start:end] += out  # Accumulate\n\n            if start == 0:\n                fade.fade_in_len = overlap_frames  # Adjust fade-in after first chunk\n\n            start += chunk_len - overlap_frames  # Move by segment minus overlap\n\n        return final[0]\nsegmentor = HDemucsSegmentor(device, 10., .1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T10:17:54.638595Z","iopub.execute_input":"2025-03-19T10:17:54.638976Z","iopub.status.idle":"2025-03-19T10:17:55.477537Z","shell.execute_reply.started":"2025-03-19T10:17:54.638947Z","shell.execute_reply":"2025-03-19T10:17:55.476881Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Apply on Some examples\n## Read a Raw data","metadata":{}},{"cell_type":"code","source":"start_segment = 0\nend_segment = 500000\nwaveform, sample_rate = torchaudio.load(audio_root.joinpath(train_df.iloc[1].filename))\nplot_spectrogram(stft(waveform[...,start_segment:end_segment])[0], \"Raw Audio Spectrogram\")\nAudio(waveform[...,start_segment:end_segment].cpu(), rate=sample_rate)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T09:53:58.666720Z","iopub.execute_input":"2025-03-19T09:53:58.667082Z","iopub.status.idle":"2025-03-19T09:54:19.834756Z","shell.execute_reply.started":"2025-03-19T09:53:58.667056Z","shell.execute_reply":"2025-03-19T09:54:19.833914Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Without Applying Vocal Segmentor","metadata":{}},{"cell_type":"code","source":"roi_wav,_ = extract_animal_segments(waveform[...,start_segment:end_segment])\nplot_spectrogram(stft(roi_wav)[0], \"Raw Audio Spectrogram\")\nAudio(roi_wav, rate=sample_rate)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T09:54:51.380128Z","iopub.execute_input":"2025-03-19T09:54:51.380441Z","iopub.status.idle":"2025-03-19T09:54:59.356748Z","shell.execute_reply.started":"2025-03-19T09:54:51.380418Z","shell.execute_reply":"2025-03-19T09:54:59.355740Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Extract Vocal Source","metadata":{}},{"cell_type":"code","source":"vocal_source = segmentor.forward(waveform.to(device), sample_rate)\nplot_spectrogram(stft(vocal_source[:,start_segment:end_segment].cpu())[0], f\"Spectrogram - Vocal\")\nAudio(vocal_source[0,start_segment:end_segment].cpu(), rate=sample_rate)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T10:18:00.692308Z","iopub.execute_input":"2025-03-19T10:18:00.692614Z","iopub.status.idle":"2025-03-19T10:18:25.557242Z","shell.execute_reply.started":"2025-03-19T10:18:00.692589Z","shell.execute_reply":"2025-03-19T10:18:25.556313Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_,start_at = extract_animal_segments(vocal_source[0,start_segment:end_segment].cpu().unsqueeze(0), 50)\nroi_wav = waveform[...,start_segment:end_segment]\nplot_spectrogram(stft(roi_wav[:,:start_at])[0], \"Proccessed Audio Spectrogram\")\nAudio(roi_wav[:,:start_at], rate=sample_rate)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T10:19:13.971280Z","iopub.execute_input":"2025-03-19T10:19:13.971629Z","iopub.status.idle":"2025-03-19T10:19:28.704782Z","shell.execute_reply.started":"2025-03-19T10:19:13.971599Z","shell.execute_reply":"2025-03-19T10:19:28.703902Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Conclusion","metadata":{}},{"cell_type":"markdown","source":"As we can see, our region of interest has been successfully extracted. We can now incorporate this preprocessing step into our training and testing pipeline to ensure cleaner data.\n\nNext, I will explore some traditional techniques to achieve this and work on removing noise from the data.","metadata":{"execution":{"iopub.status.busy":"2025-03-19T10:21:55.904019Z","iopub.execute_input":"2025-03-19T10:21:55.904353Z","iopub.status.idle":"2025-03-19T10:21:55.909393Z","shell.execute_reply.started":"2025-03-19T10:21:55.904327Z","shell.execute_reply":"2025-03-19T10:21:55.908330Z"}}}]}