From cb3004c935b8f89485f2056de08ac6fbdf408cce Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Sun, 25 Jan 2026 11:13:46 +0100 Subject: [PATCH 01/17] tcn code needs structuring --- compiam/rhythm/beat_tracking/__init__.py | 17 ++ .../beat_tracking/tcn_tracker/__init__.py | 92 ++++++++ .../beat_tracking/tcn_tracker/data_loader.py | 178 +++++++++++++++ .../rhythm/beat_tracking/tcn_tracker/tcn.py | 216 ++++++++++++++++++ 4 files changed, 503 insertions(+) create mode 100644 compiam/rhythm/beat_tracking/__init__.py create mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/__init__.py create mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py create mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/tcn.py diff --git a/compiam/rhythm/beat_tracking/__init__.py b/compiam/rhythm/beat_tracking/__init__.py new file mode 100644 index 00000000..3db5d9ba --- /dev/null +++ b/compiam/rhythm/beat_tracking/__init__.py @@ -0,0 +1,17 @@ +import sys + +from compiam.utils import get_tool_list +from compiam.data import models_dict + +from compiam.rhythm.beat_tracking import TCNTracker + + +# Show user the available tools +def list_tools(): + pre_trained_models = [ + x["class_name"] for x in list(models_dict.values()) + ] # Get list of pre-trained_models + return [ + tool + "*" if tool in pre_trained_models else tool + for tool in get_tool_list(modules=sys.modules[__name__]) + ] diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py b/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py new file mode 100644 index 00000000..9c6f075a --- /dev/null +++ b/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py @@ -0,0 +1,92 @@ +import os +import sys +import torch +import numpy as np +import madmom +from typing import Dict + +from compiam.rhythm.beat_tracking.tcn_tracker.tcn import MultiTracker +from compiam.rhythm.beat_tracking.tcn_tracker.data_loader import PreProcessor + +class TCNTracker: + def __init__(self, checkpoint_path: str, model_params: Dict = None, post_processor: str = "JOINT"): + # Device setup + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Default model parameters (should match training) + if model_params is None: + model_params = { + "N_FILTERS": 20, + "KERNEL_SIZE": 5, + "DROPOUT": 0.15, + "N_DILATIONS": 11, + } + + params = { + "LEARNING_RATE": 0.001, + "LOSS": "BCE", + "POST_PROCESSOR": post_processor, + "SCHEDULER_FACTOR": 0.2, + "SCHEDULER_PATIENCE": 5 + } + + # Create the model architecture (same as in training) + tcn = MultiTracker( + n_filters=model_params["N_FILTERS"], + n_dilations=model_params["N_DILATIONS"], + kernel_size=model_params["KERNEL_SIZE"], + dropout_rate=model_params["DROPOUT"] + ) + + self.model.eval() + + # Initialize preprocessor (same as your data loader) + self.pre_processor = PreProcessor(fps=100) + self.pad_frames = 2 + + def preprocess_audio(self, audio_path: str) -> np.ndarray: + """Extract features using your existing pipeline - EXACTLY like data_loader.py""" + # Load audio using madmom (same as your data loader) + audio, sr = madmom.io.audio.load_audio_file(audio_path) + + # Handle stereo to mono (same logic as your data loader) + if audio.shape[0] == 2: + audio = audio.mean(axis=0) + + # Create signal and extract features (same as your data loader) + s = madmom.audio.Signal(audio, sr, num_channels=1) + x = self.pre_processor(s) + + # Apply padding (same as your data loader) + pad_start = np.repeat(x[:1], self.pad_frames, axis=0) + pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) + x_padded = np.concatenate((pad_start, x, pad_stop)) + + # IMPORTANT: Apply the same dimension expansion as in data_loader.py + # The model expects (batch, channels, height, width) for 2D convolutions + # x_padded has shape (time, frequency_bins) where frequency_bins=12 + # We need to reshape it to (batch=1, channels=1, height=time, width=frequency_bins) + x_final = np.expand_dims(np.expand_dims(x_padded, axis=0), axis=0) + + return x_final + + @torch.no_grad() + def predict(self, audio_path: str) -> Dict: + """Predict beats from audio file""" + # Extract features using your pipeline (now with correct dimensions) + features = self.preprocess_audio(audio_path) + + # Convert to tensor - features now have shape (1, 1, time, frequency_bins) + x = torch.from_numpy(features).to(self.device) + + # Forward pass + output = self.model(x) + + # Extract activations + beats_act = output["beats"].squeeze().detach().cpu().numpy() + downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() + + # Apply post-processing using the model's post_tracker + pred = self.model.post_tracker(beats_act, downbeats_act) + + return pred diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py b/compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py new file mode 100644 index 00000000..5479c5d7 --- /dev/null +++ b/compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py @@ -0,0 +1,178 @@ +import madmom + +from madmom.processors import SequentialProcessor +from madmom.audio.signal import SignalProcessor, FramedSignalProcessor +from madmom.audio.stft import ShortTimeFourierTransformProcessor +from madmom.audio.spectrogram import FilteredSpectrogramProcessor, LogarithmicSpectrogramProcessor +from scipy.ndimage import maximum_filter1d + +from torch.utils.data import Dataset + +import numpy as np + +# Our approach operates on a spectrogram representation of the audio signal. +# We define the `BeatData` and `PreProcessor` classes which will pre-process our data and transforms the raw audio into a spectrogram with 100 frames per second and 81 frequency bins. + +FPS = 100 +NUM_BANDS = 12 +FFT_SIZE = 2048 +MASK_VALUE = -1 + +# BeatData is a dataset class that handles the loading and processing of beat data from a given dataset. +# It extracts audio, beats, and downbeats, and applies pre-processing to the audio signal +class BeatData(Dataset): + def __init__(self, dataset, split_keys, fps=100, widen=False): + self.fps = fps + self.keys = split_keys + self.tracks = self._get_tracks(dataset) + self.pre_processor = PreProcessor(fps=self.fps) + self.pad_frames = 2 + self.widen = widen + + def _get_tracks(self, dataset): + tracks = {} + for k in self.keys: + tracks[k] = dataset[k] + + return tracks + + + def __getitem__(self, idx): + data = {} + tid = self.keys[idx] + track = self.tracks[tid] + audio, sr = track.audio + if audio.shape[0] == 2: + audio = audio.mean(axis=0) + + s = madmom.audio.Signal(audio, sr, num_channels=1) + x = self.pre_processor(s) + + # pad features + pad_start = np.repeat(x[:1], self.pad_frames, axis=0) + pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) + + x_padded = np.concatenate((pad_start, x, pad_stop)) + + if track.beats is None: + print(f"Warning: Track {self.keys[idx]} has no beat information. Skipping.") + return self.__getitem__((idx + 1) % len(self)) # Try the next track + + + # Extract beat info + beat_times = track.beats.times.astype(np.float32) + beat_pos = track.beats.positions.astype(int) + downbeat_times = beat_times[beat_pos == 1] + + # Quantize beats and downbeats + beats = madmom.utils.quantize_events(beat_times, fps=self.fps, length=len(x)).astype(np.float32) + downbeats = madmom.utils.quantize_events(downbeat_times, fps=self.fps, length=len(x)).astype(np.float32) + + if self.widen: + # we skip masked values (assumed to be -1) + if not np.allclose(beats, -1): + np.maximum(beats, maximum_filter1d(beats, size=3) * 0.5, out=beats) + np.maximum(downbeats, maximum_filter1d(downbeats, size=3) * 0.5, out=downbeats) + + + # Adding this because torch is bothered by our batchsize=1 + data["x"] = np.expand_dims(x_padded, axis=0) + data["key"] = tid + data["audio"] = audio + data["sr"] = sr + data["beats"] = beats + data["downbeats"] = downbeats + data["beats_ann"] = beat_times + data["downbeats_ann"] = downbeat_times + + return data + + + def __len__(self): + return len(self.keys) + + +# MultiBeatData is a dataset class that handles multiple datasets and their tracks. +# It allows us to work with a list of (dataset_name, key) tuples, making it easier to manage tracks from different datasets. +class MultiBeatData(Dataset): + def __init__(self, datasets_tracks, split_keys, fps=100, widen=False): + """ + datasets_tracks: dict of {dataset_name: tracks_dict} + split_keys: list of (dataset_name, key) tuples + """ + self.fps = fps + self.keys = split_keys # List of (dataset_name, key) tuples + self.datasets_tracks = datasets_tracks + self.pre_processor = PreProcessor(fps=self.fps) + self.pad_frames = 2 + self.widen = widen + + def __getitem__(self, idx): + data = {} + dataset_name, key = self.keys[idx] # Unpack the tuple + track = self.datasets_tracks[dataset_name][key] # Get track from correct dataset + + audio, sr = track.audio + if audio.shape[0] == 2: + audio = audio.mean(axis=0) + + s = madmom.audio.Signal(audio, sr, num_channels=1) + x = self.pre_processor(s) + + # pad features + pad_start = np.repeat(x[:1], self.pad_frames, axis=0) + pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) + + x_padded = np.concatenate((pad_start, x, pad_stop)) + + if track.beats is None: + print(f"Warning: Track {dataset_name}:{key} has no beat information. Skipping.") + return self.__getitem__((idx + 1) % len(self)) # Try the next track + + # Extract beat info + beat_times = track.beats.times.astype(np.float32) + beat_pos = track.beats.positions.astype(int) + downbeat_times = beat_times[beat_pos == 1] + + # Quantize beats and downbeats + beats = madmom.utils.quantize_events(beat_times, fps=self.fps, length=len(x)).astype(np.float32) + downbeats = madmom.utils.quantize_events(downbeat_times, fps=self.fps, length=len(x)).astype(np.float32) + + if self.widen: + # we skip masked values (assumed to be -1) + if not np.allclose(beats, -1): + np.maximum(beats, maximum_filter1d(beats, size=3) * 0.5, out=beats) + np.maximum(downbeats, maximum_filter1d(downbeats, size=3) * 0.5, out=downbeats) + + # Adding this because torch is bothered by our batchsize=1 + data["x"] = np.expand_dims(x_padded, axis=0) + data["key"] = f"{dataset_name}:{key}" # Include dataset name for debugging + data["audio"] = audio + data["sr"] = sr + data["beats"] = beats + data["downbeats"] = downbeats + data["beats_ann"] = beat_times + data["downbeats_ann"] = downbeat_times + + return data + + def __len__(self): + return len(self.keys) + + +class PreProcessor(SequentialProcessor): + def __init__(self, frame_size=FFT_SIZE, num_bands=NUM_BANDS, log=np.log, add=1e-6, fps=FPS): + # resample to a fixed sample rate in order to get always the same number of filter bins + sig = SignalProcessor(num_channels=1, sample_rate=44100) + # split audio signal in overlapping frames + frames = FramedSignalProcessor(frame_size=frame_size, fps=fps) + # compute STFT + stft = ShortTimeFourierTransformProcessor() + # filter the magnitudes + filt = FilteredSpectrogramProcessor(num_bands=num_bands) + # scale them logarithmically + spec = LogarithmicSpectrogramProcessor(log=log, add=add) + # instantiate a SequentialProcessor + super(PreProcessor, self).__init__((sig, frames, stft, filt, spec, np.array)) + # safe fps as attribute (needed for quantization of events) + self.fps = fps diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/tcn.py b/compiam/rhythm/beat_tracking/tcn_tracker/tcn.py new file mode 100644 index 00000000..a0c3bb82 --- /dev/null +++ b/compiam/rhythm/beat_tracking/tcn_tracker/tcn.py @@ -0,0 +1,216 @@ +import torch +import torch.nn as nn + +class ResBlock(nn.Module): + def __init__(self, dilation_rate, n_filters, kernel_size, padding, dropout_rate): + """ + parameters: + dilation_rate : int + dilation_rate + n_filters : int + number of filters + kernel_size : int or tuple + conv kernel size + padding : str + padding (["valid", "same"]) + dropout_rate : float + dropout rate + """ + super().__init__() + in_channels = 20 + + self.res = nn.Conv1d( + in_channels=in_channels, + out_channels=n_filters, + kernel_size=1, + padding=padding, + ) + self.conv_1 = nn.Conv1d( + in_channels=in_channels, + out_channels=n_filters, + kernel_size=kernel_size, + dilation=dilation_rate, + padding=padding, + ) + self.conv_2 = nn.Conv1d( + in_channels=in_channels, + out_channels=n_filters, + kernel_size=kernel_size, + dilation=dilation_rate*2, + padding=padding, + ) + self.elu = nn.ELU() + self.dropout = nn.Dropout(dropout_rate) + self.conv_3 = nn.Conv1d( + in_channels=2*n_filters, + out_channels=n_filters, + kernel_size=1, + padding=padding, + ) + return + + + def forward(self, x): + res_x = self.res(x) + conv_1 = self.conv_1(x) + conv_2 = self.conv_2(x) + concat = torch.cat([conv_1, conv_2], dim=1) + out = self.elu(concat) + out = self.dropout(out) + out = self.conv_3(out) + + return res_x + out, out + + +class TCN(nn.Module): + def __init__( + self, + n_filters=20, + kernel_size=5, + dilations=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024], + padding="same", + dropout_rate=0.15, + ): + super().__init__() + + self.tcn_layers = nn.ModuleDict({}) + for idx, d in enumerate(dilations): + self.tcn_layers[f"tcn_{idx}"] = ResBlock(d, n_filters, kernel_size, padding, dropout_rate) + + self.activation = nn.ELU() + return + + + def forward(self, x): + skip_connections = [] + for tcn_i in self.tcn_layers: + # feed the output of the previous layer into the next layer + # increase dilation rate for each consecutive layer + x, skip_out = self.tcn_layers[tcn_i](x) + skip_connections.append(skip_out) + + x = self.activation(x) + + skip = torch.stack(skip_connections, dim=-1).sum(dim=-1) + + return x, skip + + +class MultiTracker(nn.Module): + def __init__(self, n_filters, n_dilations, kernel_size, dropout_rate, + verbose=False): + super().__init__() + padding = "same" + self.verbose = verbose + self.dropout_rate = dropout_rate + + self.conv_1 = nn.Conv2d( + in_channels=1, + out_channels=n_filters, + kernel_size=(3, 3), + stride=1, + padding="valid", + ) + self.elu_1 = nn.ELU() + self.mp_1 = nn.MaxPool2d((1, 3)) + self.dropout_1 = nn.Dropout(dropout_rate) + + self.conv_2 = nn.Conv2d( + in_channels=n_filters, + out_channels=n_filters, + kernel_size=(1, 10), + padding="valid", + ) + self.elu_2 = nn.ELU() + self.mp_2 = nn.MaxPool2d((1, 3)) + self.dropout_2 = nn.Dropout(dropout_rate) + + self.conv_3 = nn.Conv2d( + in_channels=n_filters, + out_channels=n_filters, + kernel_size=(3, 3), + padding="valid", + ) + self.elu_3 = nn.ELU() + self.mp_3 = nn.MaxPool2d((1, 3)) + self.dropout_3 = nn.Dropout(dropout_rate) + + dilations = [2**i for i in range(n_dilations)] + + self.tcn = TCN(n_filters, kernel_size, dilations, padding, dropout_rate) + + # beat head + self.beats_dropout = nn.Dropout(dropout_rate) + self.beats_dense = nn.Linear(n_filters, 1) + self.beats_act = nn.Sigmoid() + + # downbeat head + self.downbeats_dropout = nn.Dropout(dropout_rate) + self.downbeats_dense = nn.Linear(n_filters, 1) + self.downbeats_act = nn.Sigmoid() + + # tempo head + self.tempo_dropout = nn.Dropout(dropout_rate) + self.tempo_dense = nn.Linear(n_filters, 300) + self.tempo_act = nn.Softmax(dim=1) + + + def forward(self, x): + if self.verbose: + print("input shape:", x.shape) + x = self.conv_1(x) + x = self.elu_1(x) + x = self.mp_1(x) + x = self.dropout_1(x) + if self.verbose: + print("block1 out", x.shape) + + x = self.conv_2(x) + x = self.elu_2(x) + x = self.mp_2(x) + x = self.dropout_2(x) + if self.verbose: + print("block2 out", x.shape) + + x = self.conv_3(x) + x = self.elu_3(x) + x = self.mp_3(x) + x = self.dropout_3(x) + if self.verbose: + print("block3 out", x.shape) + + x = torch.squeeze(x, -1) + if self.verbose: + print("reshape", x.shape) + + x, skip = self.tcn(x) + if self.verbose: + print("tcn x out", x.shape) + print("skip out", skip.shape) + + # reshape x so it fits our linear layer + x = x.transpose(-2,-1) + if self.verbose: + print("tcn tranposed", x.shape) + + # beats head + beats = self.beats_dropout(x) + beats = self.beats_dense(beats) + beats = self.beats_act(beats) + + # Downbeats head + downbeats = self.downbeats_dropout(x) + downbeats = self.downbeats_dense(downbeats) + downbeats = self.downbeats_act(downbeats) + + + if self.verbose: + print("beats", beats.shape) + print("downbeats", downbeats.shape) + + + activations = {} + activations["beats"] = beats + activations["downbeats"] = downbeats + + return activations From f77f8c64f32aa661f91a3e8982375b8562be7c53 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Sun, 1 Feb 2026 13:59:07 +0100 Subject: [PATCH 02/17] models in zenodo --- compiam/rhythm/__init__.py | 1 + compiam/rhythm/beat_tracking/__init__.py | 3 +- .../beat_tracking/tcn_tracker/__init__.py | 125 ++++++------ .../beat_tracking/tcn_tracker/data_loader.py | 178 ------------------ .../tcn_tracker/{tcn.py => model.py} | 131 +++---------- .../rhythm/beat_tracking/tcn_tracker/post.py | 62 ++++++ .../rhythm/beat_tracking/tcn_tracker/pre.py | 17 ++ main.py | 9 + pyproject.toml | 4 +- 9 files changed, 184 insertions(+), 346 deletions(-) delete mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py rename compiam/rhythm/beat_tracking/tcn_tracker/{tcn.py => model.py} (51%) create mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/post.py create mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/pre.py create mode 100644 main.py diff --git a/compiam/rhythm/__init__.py b/compiam/rhythm/__init__.py index f3cdc292..02ebd342 100644 --- a/compiam/rhythm/__init__.py +++ b/compiam/rhythm/__init__.py @@ -13,6 +13,7 @@ ### IMPORT HERE THE CONSIDERED TASKS from compiam.rhythm import meter from compiam.rhythm import transcription +from compiam.rhythm import beat_tracking # Show user the available tasks diff --git a/compiam/rhythm/beat_tracking/__init__.py b/compiam/rhythm/beat_tracking/__init__.py index 3db5d9ba..e6da699a 100644 --- a/compiam/rhythm/beat_tracking/__init__.py +++ b/compiam/rhythm/beat_tracking/__init__.py @@ -3,8 +3,7 @@ from compiam.utils import get_tool_list from compiam.data import models_dict -from compiam.rhythm.beat_tracking import TCNTracker - +from compiam.rhythm.beat_tracking.tcn_tracker import TCNTracker # Show user the available tools def list_tools(): diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py b/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py index 9c6f075a..b894c443 100644 --- a/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py +++ b/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py @@ -5,88 +5,93 @@ import madmom from typing import Dict -from compiam.rhythm.beat_tracking.tcn_tracker.tcn import MultiTracker -from compiam.rhythm.beat_tracking.tcn_tracker.data_loader import PreProcessor +from compiam.rhythm.beat_tracking.tcn_tracker.model import MultiTracker +from compiam.rhythm.beat_tracking.tcn_tracker.pre import PreProcessor +from compiam.rhythm.beat_tracking.tcn_tracker.post import beat_tracker, joint_tracker, sequential_tracker +from compiam.utils.download import download_remote_model +from compiam.utils import get_logger, WORKDIR + +logger = get_logger(__name__) class TCNTracker: - def __init__(self, checkpoint_path: str, model_params: Dict = None, post_processor: str = "JOINT"): - # Device setup + def __init__(self, post_processor: str = "joint", model_version: int = 42, model_path: str = None): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - # Default model parameters (should match training) - if model_params is None: - model_params = { - "N_FILTERS": 20, - "KERNEL_SIZE": 5, - "DROPOUT": 0.15, - "N_DILATIONS": 11, - } - - params = { - "LEARNING_RATE": 0.001, - "LOSS": "BCE", - "POST_PROCESSOR": post_processor, - "SCHEDULER_FACTOR": 0.2, - "SCHEDULER_PATIENCE": 5 - } - - # Create the model architecture (same as in training) - tcn = MultiTracker( - n_filters=model_params["N_FILTERS"], - n_dilations=model_params["N_DILATIONS"], - kernel_size=model_params["KERNEL_SIZE"], - dropout_rate=model_params["DROPOUT"] - ) - + if post_processor not in ["beat", "joint", "sequential"]: + raise ValueError(f"Invalid post_processor: {post_processor}. Choose from 'beat', 'joint', or 'sequential'.") + if model_version not in [42, 52, 62]: + raise ValueError(f"Invalid model_version: {model_version}. Choose from 42, 52, or 62.") + + n_filters = 20 + kernel_size = 5 + n_dilations = 11 + self.model_path = model_path + self.model_version = f'multitracker_{model_version}.pth' + self.download_link = 'https://zenodo.org/records/18449067/files/compIAM-TCNCarnatic.zip?download=1' + self.download_checksum = '995369933f2a344af0ffa57ea5c15e62' + + self.model = MultiTracker(n_filters=n_filters, n_dilations=n_dilations, kernel_size=kernel_size).to(self.device) + self.load_model(self.model_path) self.model.eval() - - # Initialize preprocessor (same as your data loader) self.pre_processor = PreProcessor(fps=100) self.pad_frames = 2 + self.post_processor = beat_tracker if post_processor == "beat" else \ + joint_tracker if post_processor == "joint" else \ + sequential_tracker + + @torch.no_grad() + def __call__(self, audio_path: str) -> Dict: + features = self.preprocess_audio(audio_path) + x = torch.from_numpy(features).to(self.device) + output = self.model(x) + beats_act = output["beats"].squeeze().detach().cpu().numpy() + downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() + + if self.post_processor == beat_tracker: + pred = self.post_processor(beats_act) + else: + pred = self.post_processor(beats_act, downbeats_act) + return pred + def preprocess_audio(self, audio_path: str) -> np.ndarray: - """Extract features using your existing pipeline - EXACTLY like data_loader.py""" - # Load audio using madmom (same as your data loader) audio, sr = madmom.io.audio.load_audio_file(audio_path) - # Handle stereo to mono (same logic as your data loader) if audio.shape[0] == 2: audio = audio.mean(axis=0) - # Create signal and extract features (same as your data loader) s = madmom.audio.Signal(audio, sr, num_channels=1) x = self.pre_processor(s) - # Apply padding (same as your data loader) pad_start = np.repeat(x[:1], self.pad_frames, axis=0) pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) x_padded = np.concatenate((pad_start, x, pad_stop)) - # IMPORTANT: Apply the same dimension expansion as in data_loader.py - # The model expects (batch, channels, height, width) for 2D convolutions - # x_padded has shape (time, frequency_bins) where frequency_bins=12 - # We need to reshape it to (batch=1, channels=1, height=time, width=frequency_bins) x_final = np.expand_dims(np.expand_dims(x_padded, axis=0), axis=0) return x_final - @torch.no_grad() - def predict(self, audio_path: str) -> Dict: - """Predict beats from audio file""" - # Extract features using your pipeline (now with correct dimensions) - features = self.preprocess_audio(audio_path) - - # Convert to tensor - features now have shape (1, 1, time, frequency_bins) - x = torch.from_numpy(features).to(self.device) - - # Forward pass - output = self.model(x) - - # Extract activations - beats_act = output["beats"].squeeze().detach().cpu().numpy() - downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() - - # Apply post-processing using the model's post_tracker - pred = self.model.post_tracker(beats_act, downbeats_act) - - return pred + def load_model(self, model_path): + path_to_check = os.path.join(model_path, self.model_version) + if not os.path.exists(path_to_check): + print('yes') + self.download_model(model_path) # Downloading model weights + self.model.load_weights(os.path.join(model_path, self.model_version), self.device) + + def download_model(self, model_path=None, force_overwrite=True): + """Download pre-trained model.""" + download_path = ( + #os.sep + os.path.join(*model_path.split(os.sep)[:-2]) + model_path + if model_path is not None + else os.path.join(WORKDIR, "models", "rhythm", "tcn-carnatic") + ) + # Creating model folder to store the weights + if not os.path.exists(download_path): + os.makedirs(download_path) + download_remote_model( + self.download_link, + self.download_checksum, + download_path, + force_overwrite=force_overwrite, + ) diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py b/compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py deleted file mode 100644 index 5479c5d7..00000000 --- a/compiam/rhythm/beat_tracking/tcn_tracker/data_loader.py +++ /dev/null @@ -1,178 +0,0 @@ -import madmom - -from madmom.processors import SequentialProcessor -from madmom.audio.signal import SignalProcessor, FramedSignalProcessor -from madmom.audio.stft import ShortTimeFourierTransformProcessor -from madmom.audio.spectrogram import FilteredSpectrogramProcessor, LogarithmicSpectrogramProcessor -from scipy.ndimage import maximum_filter1d - -from torch.utils.data import Dataset - -import numpy as np - -# Our approach operates on a spectrogram representation of the audio signal. -# We define the `BeatData` and `PreProcessor` classes which will pre-process our data and transforms the raw audio into a spectrogram with 100 frames per second and 81 frequency bins. - -FPS = 100 -NUM_BANDS = 12 -FFT_SIZE = 2048 -MASK_VALUE = -1 - -# BeatData is a dataset class that handles the loading and processing of beat data from a given dataset. -# It extracts audio, beats, and downbeats, and applies pre-processing to the audio signal -class BeatData(Dataset): - def __init__(self, dataset, split_keys, fps=100, widen=False): - self.fps = fps - self.keys = split_keys - self.tracks = self._get_tracks(dataset) - self.pre_processor = PreProcessor(fps=self.fps) - self.pad_frames = 2 - self.widen = widen - - def _get_tracks(self, dataset): - tracks = {} - for k in self.keys: - tracks[k] = dataset[k] - - return tracks - - - def __getitem__(self, idx): - data = {} - tid = self.keys[idx] - track = self.tracks[tid] - audio, sr = track.audio - if audio.shape[0] == 2: - audio = audio.mean(axis=0) - - s = madmom.audio.Signal(audio, sr, num_channels=1) - x = self.pre_processor(s) - - # pad features - pad_start = np.repeat(x[:1], self.pad_frames, axis=0) - pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) - - x_padded = np.concatenate((pad_start, x, pad_stop)) - - if track.beats is None: - print(f"Warning: Track {self.keys[idx]} has no beat information. Skipping.") - return self.__getitem__((idx + 1) % len(self)) # Try the next track - - - # Extract beat info - beat_times = track.beats.times.astype(np.float32) - beat_pos = track.beats.positions.astype(int) - downbeat_times = beat_times[beat_pos == 1] - - # Quantize beats and downbeats - beats = madmom.utils.quantize_events(beat_times, fps=self.fps, length=len(x)).astype(np.float32) - downbeats = madmom.utils.quantize_events(downbeat_times, fps=self.fps, length=len(x)).astype(np.float32) - - if self.widen: - # we skip masked values (assumed to be -1) - if not np.allclose(beats, -1): - np.maximum(beats, maximum_filter1d(beats, size=3) * 0.5, out=beats) - np.maximum(downbeats, maximum_filter1d(downbeats, size=3) * 0.5, out=downbeats) - - - # Adding this because torch is bothered by our batchsize=1 - data["x"] = np.expand_dims(x_padded, axis=0) - data["key"] = tid - data["audio"] = audio - data["sr"] = sr - data["beats"] = beats - data["downbeats"] = downbeats - data["beats_ann"] = beat_times - data["downbeats_ann"] = downbeat_times - - return data - - - def __len__(self): - return len(self.keys) - - -# MultiBeatData is a dataset class that handles multiple datasets and their tracks. -# It allows us to work with a list of (dataset_name, key) tuples, making it easier to manage tracks from different datasets. -class MultiBeatData(Dataset): - def __init__(self, datasets_tracks, split_keys, fps=100, widen=False): - """ - datasets_tracks: dict of {dataset_name: tracks_dict} - split_keys: list of (dataset_name, key) tuples - """ - self.fps = fps - self.keys = split_keys # List of (dataset_name, key) tuples - self.datasets_tracks = datasets_tracks - self.pre_processor = PreProcessor(fps=self.fps) - self.pad_frames = 2 - self.widen = widen - - def __getitem__(self, idx): - data = {} - dataset_name, key = self.keys[idx] # Unpack the tuple - track = self.datasets_tracks[dataset_name][key] # Get track from correct dataset - - audio, sr = track.audio - if audio.shape[0] == 2: - audio = audio.mean(axis=0) - - s = madmom.audio.Signal(audio, sr, num_channels=1) - x = self.pre_processor(s) - - # pad features - pad_start = np.repeat(x[:1], self.pad_frames, axis=0) - pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) - - x_padded = np.concatenate((pad_start, x, pad_stop)) - - if track.beats is None: - print(f"Warning: Track {dataset_name}:{key} has no beat information. Skipping.") - return self.__getitem__((idx + 1) % len(self)) # Try the next track - - # Extract beat info - beat_times = track.beats.times.astype(np.float32) - beat_pos = track.beats.positions.astype(int) - downbeat_times = beat_times[beat_pos == 1] - - # Quantize beats and downbeats - beats = madmom.utils.quantize_events(beat_times, fps=self.fps, length=len(x)).astype(np.float32) - downbeats = madmom.utils.quantize_events(downbeat_times, fps=self.fps, length=len(x)).astype(np.float32) - - if self.widen: - # we skip masked values (assumed to be -1) - if not np.allclose(beats, -1): - np.maximum(beats, maximum_filter1d(beats, size=3) * 0.5, out=beats) - np.maximum(downbeats, maximum_filter1d(downbeats, size=3) * 0.5, out=downbeats) - - # Adding this because torch is bothered by our batchsize=1 - data["x"] = np.expand_dims(x_padded, axis=0) - data["key"] = f"{dataset_name}:{key}" # Include dataset name for debugging - data["audio"] = audio - data["sr"] = sr - data["beats"] = beats - data["downbeats"] = downbeats - data["beats_ann"] = beat_times - data["downbeats_ann"] = downbeat_times - - return data - - def __len__(self): - return len(self.keys) - - -class PreProcessor(SequentialProcessor): - def __init__(self, frame_size=FFT_SIZE, num_bands=NUM_BANDS, log=np.log, add=1e-6, fps=FPS): - # resample to a fixed sample rate in order to get always the same number of filter bins - sig = SignalProcessor(num_channels=1, sample_rate=44100) - # split audio signal in overlapping frames - frames = FramedSignalProcessor(frame_size=frame_size, fps=fps) - # compute STFT - stft = ShortTimeFourierTransformProcessor() - # filter the magnitudes - filt = FilteredSpectrogramProcessor(num_bands=num_bands) - # scale them logarithmically - spec = LogarithmicSpectrogramProcessor(log=log, add=add) - # instantiate a SequentialProcessor - super(PreProcessor, self).__init__((sig, frames, stft, filt, spec, np.array)) - # safe fps as attribute (needed for quantization of events) - self.fps = fps diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/tcn.py b/compiam/rhythm/beat_tracking/tcn_tracker/model.py similarity index 51% rename from compiam/rhythm/beat_tracking/tcn_tracker/tcn.py rename to compiam/rhythm/beat_tracking/tcn_tracker/model.py index a0c3bb82..4070b456 100644 --- a/compiam/rhythm/beat_tracking/tcn_tracker/tcn.py +++ b/compiam/rhythm/beat_tracking/tcn_tracker/model.py @@ -3,52 +3,17 @@ class ResBlock(nn.Module): def __init__(self, dilation_rate, n_filters, kernel_size, padding, dropout_rate): - """ - parameters: - dilation_rate : int - dilation_rate - n_filters : int - number of filters - kernel_size : int or tuple - conv kernel size - padding : str - padding (["valid", "same"]) - dropout_rate : float - dropout rate - """ super().__init__() in_channels = 20 - self.res = nn.Conv1d( - in_channels=in_channels, - out_channels=n_filters, - kernel_size=1, - padding=padding, - ) - self.conv_1 = nn.Conv1d( - in_channels=in_channels, - out_channels=n_filters, - kernel_size=kernel_size, - dilation=dilation_rate, - padding=padding, - ) - self.conv_2 = nn.Conv1d( - in_channels=in_channels, - out_channels=n_filters, - kernel_size=kernel_size, - dilation=dilation_rate*2, - padding=padding, - ) + self.res = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=1, padding=padding) + self.conv_1 = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, dilation=dilation_rate, padding=padding,) + self.conv_2 = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, dilation=dilation_rate*2, padding=padding,) self.elu = nn.ELU() self.dropout = nn.Dropout(dropout_rate) - self.conv_3 = nn.Conv1d( - in_channels=2*n_filters, - out_channels=n_filters, - kernel_size=1, - padding=padding, - ) - return + self.conv_3 = nn.Conv1d(in_channels=2*n_filters, out_channels=n_filters, kernel_size=1, padding=padding) + return def forward(self, x): res_x = self.res(x) @@ -61,31 +26,23 @@ def forward(self, x): return res_x + out, out - class TCN(nn.Module): - def __init__( - self, - n_filters=20, - kernel_size=5, - dilations=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024], - padding="same", - dropout_rate=0.15, - ): + def __init__(self, n_filters, kernel_size, n_dilations, dropout_rate, padding="same"): super().__init__() + dilations = [2**i for i in range(n_dilations)] self.tcn_layers = nn.ModuleDict({}) + for idx, d in enumerate(dilations): self.tcn_layers[f"tcn_{idx}"] = ResBlock(d, n_filters, kernel_size, padding, dropout_rate) self.activation = nn.ELU() - return + return def forward(self, x): skip_connections = [] for tcn_i in self.tcn_layers: - # feed the output of the previous layer into the next layer - # increase dilation rate for each consecutive layer x, skip_out = self.tcn_layers[tcn_i](x) skip_connections.append(skip_out) @@ -97,120 +54,84 @@ def forward(self, x): class MultiTracker(nn.Module): - def __init__(self, n_filters, n_dilations, kernel_size, dropout_rate, - verbose=False): + def __init__(self, n_filters, n_dilations, kernel_size, dropout_rate=0.15): super().__init__() padding = "same" - self.verbose = verbose self.dropout_rate = dropout_rate - self.conv_1 = nn.Conv2d( - in_channels=1, - out_channels=n_filters, - kernel_size=(3, 3), - stride=1, - padding="valid", - ) + self.conv_1 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(3, 3), stride=1, padding="valid") self.elu_1 = nn.ELU() self.mp_1 = nn.MaxPool2d((1, 3)) self.dropout_1 = nn.Dropout(dropout_rate) - self.conv_2 = nn.Conv2d( - in_channels=n_filters, - out_channels=n_filters, - kernel_size=(1, 10), - padding="valid", - ) + self.conv_2 = nn.Conv2d(in_channels=n_filters, out_channels=n_filters, kernel_size=(1, 10), padding="valid") self.elu_2 = nn.ELU() self.mp_2 = nn.MaxPool2d((1, 3)) self.dropout_2 = nn.Dropout(dropout_rate) - self.conv_3 = nn.Conv2d( - in_channels=n_filters, - out_channels=n_filters, - kernel_size=(3, 3), - padding="valid", - ) + self.conv_3 = nn.Conv2d(in_channels=n_filters, out_channels=n_filters, kernel_size=(3, 3), padding="valid") self.elu_3 = nn.ELU() self.mp_3 = nn.MaxPool2d((1, 3)) self.dropout_3 = nn.Dropout(dropout_rate) - dilations = [2**i for i in range(n_dilations)] - - self.tcn = TCN(n_filters, kernel_size, dilations, padding, dropout_rate) + self.tcn = TCN(n_filters, kernel_size, n_dilations, dropout_rate) - # beat head self.beats_dropout = nn.Dropout(dropout_rate) self.beats_dense = nn.Linear(n_filters, 1) self.beats_act = nn.Sigmoid() - # downbeat head self.downbeats_dropout = nn.Dropout(dropout_rate) self.downbeats_dense = nn.Linear(n_filters, 1) self.downbeats_act = nn.Sigmoid() - # tempo head self.tempo_dropout = nn.Dropout(dropout_rate) self.tempo_dense = nn.Linear(n_filters, 300) self.tempo_act = nn.Softmax(dim=1) - def forward(self, x): - if self.verbose: - print("input shape:", x.shape) x = self.conv_1(x) x = self.elu_1(x) x = self.mp_1(x) x = self.dropout_1(x) - if self.verbose: - print("block1 out", x.shape) x = self.conv_2(x) x = self.elu_2(x) x = self.mp_2(x) x = self.dropout_2(x) - if self.verbose: - print("block2 out", x.shape) x = self.conv_3(x) x = self.elu_3(x) x = self.mp_3(x) x = self.dropout_3(x) - if self.verbose: - print("block3 out", x.shape) x = torch.squeeze(x, -1) - if self.verbose: - print("reshape", x.shape) x, skip = self.tcn(x) - if self.verbose: - print("tcn x out", x.shape) - print("skip out", skip.shape) - # reshape x so it fits our linear layer x = x.transpose(-2,-1) - if self.verbose: - print("tcn tranposed", x.shape) - # beats head beats = self.beats_dropout(x) beats = self.beats_dense(beats) beats = self.beats_act(beats) - # Downbeats head downbeats = self.downbeats_dropout(x) downbeats = self.downbeats_dense(downbeats) downbeats = self.downbeats_act(downbeats) - - if self.verbose: - print("beats", beats.shape) - print("downbeats", downbeats.shape) - - activations = {} activations["beats"] = beats activations["downbeats"] = downbeats return activations + + def load_weights(self, model_path, device): + ckpt = torch.load(model_path, map_location=device) + state_dict = ckpt.get("state_dict", ckpt) + new_state_dict = {} + for k, v in state_dict.items(): + if k.startswith("model."): + k = k[len("model."):] + new_state_dict[k] = v + + self.load_state_dict(new_state_dict) + return diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/post.py b/compiam/rhythm/beat_tracking/tcn_tracker/post.py new file mode 100644 index 00000000..e31c7c35 --- /dev/null +++ b/compiam/rhythm/beat_tracking/tcn_tracker/post.py @@ -0,0 +1,62 @@ +#import madmom +from madmom.features.beats import DBNBeatTrackingProcessor +from madmom.features.downbeats import DBNDownBeatTrackingProcessor, DBNBarTrackingProcessor +from scipy.ndimage import maximum_filter1d +import numpy as np + +fps= 100 # frames per second for the DBN processors +min_bpm= 55.0 +max_bpm= 230.0 + +epsilon = 1e-5 + +def clip_probabilities(probs): + """Clip probabilities to avoid exact 0 and 1 values that cause DBN issues.""" + probs = np.maximum(probs, 0) + probs = np.minimum(probs, 1) + return probs * (1 - epsilon) + epsilon / 2 + +def beat_tracker(beats_act): + beats_act = clip_probabilities(beats_act) + beat_dbn = DBNBeatTrackingProcessor( + min_bpm=min_bpm, max_bpm=max_bpm, fps=fps, transition_lambda=100, online=False) + + if beats_act.size > 1: + beats_pred = beat_dbn(beats_act) + return beats_pred + else: + # If no beats are detected, return an empty array + return np.array([]) + +def joint_tracker(beats_act, downbeats_act): + beats_act = clip_probabilities(beats_act) + downbeats_act = clip_probabilities(downbeats_act) + + downbeat_tracker = DBNDownBeatTrackingProcessor( + beats_per_bar=[3, 5, 7, 8], min_bpm=min_bpm, max_bpm=max_bpm, fps=fps) + + combined_act = np.vstack((np.maximum(beats_act - downbeats_act, 0), downbeats_act)).T + pred = downbeat_tracker(combined_act) + + return pred + +def sequential_tracker(beats_act, downbeats_act): + beats_act = clip_probabilities(beats_act) + downbeats_act = clip_probabilities(downbeats_act) + + beats = beat_tracker(beats_act) + + # bars (i.e. track beats and then downbeats) + beat_idx = (beats * fps).astype(int) + bar_act = maximum_filter1d(downbeats_act, size=3) + bar_act = bar_act[beat_idx] + bar_act = np.vstack((beats, bar_act)).T + + bar_tracker = DBNBarTrackingProcessor(beats_per_bar=(3, 5, 7, 8), meter_change_prob=1e-3, observation_weight=4) + + try: + pred = bar_tracker(bar_act) + except IndexError: + pred = np.empty((0, 2)) + + return pred diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/pre.py b/compiam/rhythm/beat_tracking/tcn_tracker/pre.py new file mode 100644 index 00000000..8ba2826d --- /dev/null +++ b/compiam/rhythm/beat_tracking/tcn_tracker/pre.py @@ -0,0 +1,17 @@ +import numpy as np +from madmom.processors import SequentialProcessor +from madmom.audio.signal import SignalProcessor, FramedSignalProcessor +from madmom.audio.stft import ShortTimeFourierTransformProcessor +from madmom.audio.spectrogram import FilteredSpectrogramProcessor, LogarithmicSpectrogramProcessor +from torch.utils.data import Dataset + + +class PreProcessor(SequentialProcessor): + def __init__(self, frame_size=2048, num_bands=12, log=np.log, add=1e-6, fps=100): + sig = SignalProcessor(num_channels=1, sample_rate=44100) + frames = FramedSignalProcessor(frame_size=frame_size, fps=fps) + stft = ShortTimeFourierTransformProcessor() + filt = FilteredSpectrogramProcessor(num_bands=num_bands) + spec = LogarithmicSpectrogramProcessor(log=log, add=add) + super(PreProcessor, self).__init__((sig, frames, stft, filt, spec, np.array)) + self.fps = fps diff --git a/main.py b/main.py new file mode 100644 index 00000000..1026088e --- /dev/null +++ b/main.py @@ -0,0 +1,9 @@ +from compiam.rhythm.beat_tracking import TCNTracker +import os + +audio_path = "../Devi Pavane.wav" + +tracker = TCNTracker(model_path=os.path.join("..", "models", "tcn_tracker", "multitracker_42.pth"), post_processor="joint") +pred = tracker(audio_path) + +print(pred) diff --git a/pyproject.toml b/pyproject.toml index f5961a9c..2eb2b398 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,6 +41,8 @@ dependencies = [ "fastdtw~=0.3.4", "compmusic==0.4", "mirdata==0.3.9", + "madmom", + "torch" ] [tool.pytest.ini_options] @@ -79,4 +81,4 @@ essentia = [ Homepage = "https://github.com/MTG/compIAM" Documentation = "https://mtg.github.io/compIAM/" Issues = "https://github.com/MTG/compIAM/issues/" -Releases = "https://github.com/MTG/compIAM/releases/" \ No newline at end of file +Releases = "https://github.com/MTG/compIAM/releases/" From 400dac7bd3538e2efacdd4e7ac05d5cc95443378 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Mon, 2 Feb 2026 15:02:39 +0100 Subject: [PATCH 03/17] pending decision on madmom --- compiam/data.py | 16 ++ compiam/rhythm/__init__.py | 1 - compiam/rhythm/beat_tracking/__init__.py | 16 -- .../beat_tracking/tcn_tracker/__init__.py | 97 -------- compiam/rhythm/meter/__init__.py | 2 +- compiam/rhythm/meter/tcn_carnatic/__init__.py | 145 ++++++++++++ .../tcn_carnatic}/model.py | 20 +- .../tcn_carnatic}/post.py | 10 +- .../tcn_tracker => meter/tcn_carnatic}/pre.py | 0 main.py | 214 +++++++++++++++++- 10 files changed, 388 insertions(+), 133 deletions(-) delete mode 100644 compiam/rhythm/beat_tracking/__init__.py delete mode 100644 compiam/rhythm/beat_tracking/tcn_tracker/__init__.py create mode 100644 compiam/rhythm/meter/tcn_carnatic/__init__.py rename compiam/rhythm/{beat_tracking/tcn_tracker => meter/tcn_carnatic}/model.py (90%) rename compiam/rhythm/{beat_tracking/tcn_tracker => meter/tcn_carnatic}/post.py (91%) rename compiam/rhythm/{beat_tracking/tcn_tracker => meter/tcn_carnatic}/pre.py (100%) diff --git a/compiam/data.py b/compiam/data.py index fc1c2a4b..ae4d166d 100644 --- a/compiam/data.py +++ b/compiam/data.py @@ -125,6 +125,22 @@ }, }, }, + "rhythm:tcn-carnatic": { + "module_name": "compiam.rhythm.meter.tcn_carnatic", + "class_name": "TCNTracker", + "default_version": "v1", + "kwargs": { + "v1": { + "model_path": os.path.join( + "models", + "rhythm", + "tcn-carnatic" + ), + "download_link": "https://zenodo.org/records/18449067/files/compIAM-TCNCarnatic.zip?download=1", + "download_checksum": "995369933f2a344af0ffa57ea5c15e62", + }, + }, + }, "structure:dhrupad-bandish-segmentation": { "module_name": "compiam.structure.segmentation.dhrupad_bandish_segmentation", "class_name": "DhrupadBandishSegmentation", diff --git a/compiam/rhythm/__init__.py b/compiam/rhythm/__init__.py index 02ebd342..f3cdc292 100644 --- a/compiam/rhythm/__init__.py +++ b/compiam/rhythm/__init__.py @@ -13,7 +13,6 @@ ### IMPORT HERE THE CONSIDERED TASKS from compiam.rhythm import meter from compiam.rhythm import transcription -from compiam.rhythm import beat_tracking # Show user the available tasks diff --git a/compiam/rhythm/beat_tracking/__init__.py b/compiam/rhythm/beat_tracking/__init__.py deleted file mode 100644 index e6da699a..00000000 --- a/compiam/rhythm/beat_tracking/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -import sys - -from compiam.utils import get_tool_list -from compiam.data import models_dict - -from compiam.rhythm.beat_tracking.tcn_tracker import TCNTracker - -# Show user the available tools -def list_tools(): - pre_trained_models = [ - x["class_name"] for x in list(models_dict.values()) - ] # Get list of pre-trained_models - return [ - tool + "*" if tool in pre_trained_models else tool - for tool in get_tool_list(modules=sys.modules[__name__]) - ] diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py b/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py deleted file mode 100644 index b894c443..00000000 --- a/compiam/rhythm/beat_tracking/tcn_tracker/__init__.py +++ /dev/null @@ -1,97 +0,0 @@ -import os -import sys -import torch -import numpy as np -import madmom -from typing import Dict - -from compiam.rhythm.beat_tracking.tcn_tracker.model import MultiTracker -from compiam.rhythm.beat_tracking.tcn_tracker.pre import PreProcessor -from compiam.rhythm.beat_tracking.tcn_tracker.post import beat_tracker, joint_tracker, sequential_tracker -from compiam.utils.download import download_remote_model -from compiam.utils import get_logger, WORKDIR - -logger = get_logger(__name__) - -class TCNTracker: - def __init__(self, post_processor: str = "joint", model_version: int = 42, model_path: str = None): - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - if post_processor not in ["beat", "joint", "sequential"]: - raise ValueError(f"Invalid post_processor: {post_processor}. Choose from 'beat', 'joint', or 'sequential'.") - if model_version not in [42, 52, 62]: - raise ValueError(f"Invalid model_version: {model_version}. Choose from 42, 52, or 62.") - - n_filters = 20 - kernel_size = 5 - n_dilations = 11 - self.model_path = model_path - self.model_version = f'multitracker_{model_version}.pth' - self.download_link = 'https://zenodo.org/records/18449067/files/compIAM-TCNCarnatic.zip?download=1' - self.download_checksum = '995369933f2a344af0ffa57ea5c15e62' - - self.model = MultiTracker(n_filters=n_filters, n_dilations=n_dilations, kernel_size=kernel_size).to(self.device) - self.load_model(self.model_path) - self.model.eval() - self.pre_processor = PreProcessor(fps=100) - self.pad_frames = 2 - - self.post_processor = beat_tracker if post_processor == "beat" else \ - joint_tracker if post_processor == "joint" else \ - sequential_tracker - - @torch.no_grad() - def __call__(self, audio_path: str) -> Dict: - features = self.preprocess_audio(audio_path) - x = torch.from_numpy(features).to(self.device) - output = self.model(x) - beats_act = output["beats"].squeeze().detach().cpu().numpy() - downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() - - if self.post_processor == beat_tracker: - pred = self.post_processor(beats_act) - else: - pred = self.post_processor(beats_act, downbeats_act) - return pred - - def preprocess_audio(self, audio_path: str) -> np.ndarray: - audio, sr = madmom.io.audio.load_audio_file(audio_path) - - if audio.shape[0] == 2: - audio = audio.mean(axis=0) - - s = madmom.audio.Signal(audio, sr, num_channels=1) - x = self.pre_processor(s) - - pad_start = np.repeat(x[:1], self.pad_frames, axis=0) - pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) - x_padded = np.concatenate((pad_start, x, pad_stop)) - - x_final = np.expand_dims(np.expand_dims(x_padded, axis=0), axis=0) - - return x_final - - def load_model(self, model_path): - path_to_check = os.path.join(model_path, self.model_version) - if not os.path.exists(path_to_check): - print('yes') - self.download_model(model_path) # Downloading model weights - self.model.load_weights(os.path.join(model_path, self.model_version), self.device) - - def download_model(self, model_path=None, force_overwrite=True): - """Download pre-trained model.""" - download_path = ( - #os.sep + os.path.join(*model_path.split(os.sep)[:-2]) - model_path - if model_path is not None - else os.path.join(WORKDIR, "models", "rhythm", "tcn-carnatic") - ) - # Creating model folder to store the weights - if not os.path.exists(download_path): - os.makedirs(download_path) - download_remote_model( - self.download_link, - self.download_checksum, - download_path, - force_overwrite=force_overwrite, - ) diff --git a/compiam/rhythm/meter/__init__.py b/compiam/rhythm/meter/__init__.py index fbc5feb3..834b748d 100644 --- a/compiam/rhythm/meter/__init__.py +++ b/compiam/rhythm/meter/__init__.py @@ -4,7 +4,7 @@ from compiam.data import models_dict from compiam.rhythm.meter.akshara_pulse_tracker import AksharaPulseTracker - +from compiam.rhythm.meter.tcn_carnatic import TCNTracker # Show user the available tools def list_tools(): diff --git a/compiam/rhythm/meter/tcn_carnatic/__init__.py b/compiam/rhythm/meter/tcn_carnatic/__init__.py new file mode 100644 index 00000000..177ed1b8 --- /dev/null +++ b/compiam/rhythm/meter/tcn_carnatic/__init__.py @@ -0,0 +1,145 @@ +import os +import sys +import torch +import numpy as np +import madmom +from typing import Dict +from tqdm import tqdm +from compiam.exceptions import ModelNotTrainedError + +from compiam.rhythm.meter.tcn_carnatic.model import MultiTracker +from compiam.rhythm.meter.tcn_carnatic.pre import PreProcessor +from compiam.rhythm.meter.tcn_carnatic.post import beat_tracker, joint_tracker, sequential_tracker +from compiam.utils.download import download_remote_model +from compiam.utils import get_logger, WORKDIR + +logger = get_logger(__name__) + +class TCNTracker: + def __init__(self, post_processor="joint", model_version=42, model_path=None, download_link=None, download_checksum=None, gpu=-1): + ### IMPORTING OPTIONAL DEPENDENCIES + try: + global torch + import torch + + global MultiTracker + from compiam.rhythm.meter.tcn_carnatic.model import MultiTracker + + except: + raise ImportError( + "In order to use this tool you need to have torch installed. " + "Install compIAM with torch support: pip install 'compiam[torch]'" + ) + ### + if post_processor not in ["beat", "joint", "sequential"]: + raise ValueError(f"Invalid post_processor: {post_processor}. Choose from 'joint', or 'sequential'.") + if model_version not in [42, 52, 62]: + raise ValueError(f"Invalid model_version: {model_version}. Choose from 42, 52, or 62.") + + self.gpu = gpu + self.device = None + self.select_gpu(gpu) + + self.model_path = model_path + self.model_version = f'multitracker_{model_version}.pth' + self.download_link = download_link + self.download_checksum = download_checksum + + self.trained = False + self.model = self._build_model() + if self.model_path is not None: + self.load_model(self.model_path) + self.pre_processor = PreProcessor(fps=100) + self.pad_frames = 2 + + self.post_processor = joint_tracker if post_processor == "joint" else \ + sequential_tracker + + @torch.no_grad() + def predict(self, audio_path: str) -> Dict: + + if self.trained is False: + raise ModelNotTrainedError( + """Model is not trained. Please load model before running inference! + You can load the pre-trained instance with the load_model wrapper.""" + ) + + features = self.preprocess_audio(audio_path) + x = torch.from_numpy(features).to(self.device) + output = self.model(x) + beats_act = output["beats"].squeeze().detach().cpu().numpy() + downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() + + if self.post_processor == beat_tracker: + beats, beat_positions = self.post_processor(beats_act) + else: + pred = self.post_processor(beats_act, downbeats_act) + return pred + + def preprocess_audio(self, audio_path: str) -> np.ndarray: + audio, sr = madmom.io.audio.load_audio_file(audio_path) + + if audio.shape[0] == 2: + audio = audio.mean(axis=0) + + s = madmom.audio.Signal(audio, sr, num_channels=1) + x = self.pre_processor(s) + + pad_start = np.repeat(x[:1], self.pad_frames, axis=0) + pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) + x_padded = np.concatenate((pad_start, x, pad_stop)) + + x_final = np.expand_dims(np.expand_dims(x_padded, axis=0), axis=0) + + return x_final + + def _build_model(self): + model = MultiTracker().to(self.device) + model.eval() + return model + + def load_model(self, model_path): + """Load pre-trained model weights.""" + if not os.path.exists(os.path.join(model_path, self.model_version)): + self.download_model(model_path) # Downloading model weights + + self.model.load_weights(os.path.join(model_path, self.model_version), self.device) + + self.model_path = model_path + self.trained = True + + def download_model(self, model_path=None, force_overwrite=True): + """Download pre-trained model.""" + download_path = ( + #os.sep + os.path.join(*model_path.split(os.sep)[:-2]) + model_path + if model_path is not None + else os.path.join(WORKDIR, "models", "rhythm", "tcn-carnatic") + ) + # Creating model folder to store the weights + if not os.path.exists(download_path): + os.makedirs(download_path) + download_remote_model( + self.download_link, + self.download_checksum, + download_path, + force_overwrite=force_overwrite, + ) + + def select_gpu(self, gpu="-1"): + """Select the GPU to use for inference. + + :param gpu: Id of the available GPU to use (-1 by default, to run on CPU), use string: '0', '1', etc. + :returns: None + """ + if int(gpu) == -1: + self.device = torch.device("cpu") + else: + if torch.cuda.is_available(): + self.device = torch.device("cuda:" + str(gpu)) + elif torch.backends.mps.is_available(): + self.device = torch.device("mps:" + str(gpu)) + else: + self.device = torch.device("cpu") + logger.warning("No GPU available. Running on CPU.") + self.gpu = gpu diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/model.py b/compiam/rhythm/meter/tcn_carnatic/model.py similarity index 90% rename from compiam/rhythm/beat_tracking/tcn_tracker/model.py rename to compiam/rhythm/meter/tcn_carnatic/model.py index 4070b456..3b4ff51e 100644 --- a/compiam/rhythm/beat_tracking/tcn_tracker/model.py +++ b/compiam/rhythm/meter/tcn_carnatic/model.py @@ -2,16 +2,15 @@ import torch.nn as nn class ResBlock(nn.Module): - def __init__(self, dilation_rate, n_filters, kernel_size, padding, dropout_rate): + def __init__(self, dilation_rate, n_filters, kernel_size, dropout_rate=0.15): super().__init__() in_channels = 20 - - self.res = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=1, padding=padding) - self.conv_1 = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, dilation=dilation_rate, padding=padding,) - self.conv_2 = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, dilation=dilation_rate*2, padding=padding,) + self.res = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=1, padding='same') + self.conv_1 = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, dilation=dilation_rate, padding='same') + self.conv_2 = nn.Conv1d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, dilation=dilation_rate*2, padding='same') self.elu = nn.ELU() - self.dropout = nn.Dropout(dropout_rate) - self.conv_3 = nn.Conv1d(in_channels=2*n_filters, out_channels=n_filters, kernel_size=1, padding=padding) + self.dropout = nn.Dropout(0.15) + self.conv_3 = nn.Conv1d(in_channels=2*n_filters, out_channels=n_filters, kernel_size=1, padding='same') return @@ -27,14 +26,14 @@ def forward(self, x): return res_x + out, out class TCN(nn.Module): - def __init__(self, n_filters, kernel_size, n_dilations, dropout_rate, padding="same"): + def __init__(self, n_filters, kernel_size, n_dilations, dropout_rate): super().__init__() dilations = [2**i for i in range(n_dilations)] self.tcn_layers = nn.ModuleDict({}) for idx, d in enumerate(dilations): - self.tcn_layers[f"tcn_{idx}"] = ResBlock(d, n_filters, kernel_size, padding, dropout_rate) + self.tcn_layers[f"tcn_{idx}"] = ResBlock(d, n_filters, kernel_size, dropout_rate) self.activation = nn.ELU() @@ -54,9 +53,8 @@ def forward(self, x): class MultiTracker(nn.Module): - def __init__(self, n_filters, n_dilations, kernel_size, dropout_rate=0.15): + def __init__(self, n_filters=20, n_dilations=11, kernel_size=5, dropout_rate=0.15): super().__init__() - padding = "same" self.dropout_rate = dropout_rate self.conv_1 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(3, 3), stride=1, padding="valid") diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/post.py b/compiam/rhythm/meter/tcn_carnatic/post.py similarity index 91% rename from compiam/rhythm/beat_tracking/tcn_tracker/post.py rename to compiam/rhythm/meter/tcn_carnatic/post.py index e31c7c35..b1f770db 100644 --- a/compiam/rhythm/beat_tracking/tcn_tracker/post.py +++ b/compiam/rhythm/meter/tcn_carnatic/post.py @@ -38,7 +38,10 @@ def joint_tracker(beats_act, downbeats_act): combined_act = np.vstack((np.maximum(beats_act - downbeats_act, 0), downbeats_act)).T pred = downbeat_tracker(combined_act) - return pred + beats = pred[:, 0] + beat_positions = pred[:, 1] + + return beats, beat_positions def sequential_tracker(beats_act, downbeats_act): beats_act = clip_probabilities(beats_act) @@ -59,4 +62,7 @@ def sequential_tracker(beats_act, downbeats_act): except IndexError: pred = np.empty((0, 2)) - return pred + beats = pred[:, 0] + beat_positions = pred[:, 1] + + return beats, beat_positions diff --git a/compiam/rhythm/beat_tracking/tcn_tracker/pre.py b/compiam/rhythm/meter/tcn_carnatic/pre.py similarity index 100% rename from compiam/rhythm/beat_tracking/tcn_tracker/pre.py rename to compiam/rhythm/meter/tcn_carnatic/pre.py diff --git a/main.py b/main.py index 1026088e..7422a0c7 100644 --- a/main.py +++ b/main.py @@ -1,9 +1,213 @@ -from compiam.rhythm.beat_tracking import TCNTracker +import compiam import os -audio_path = "../Devi Pavane.wav" -tracker = TCNTracker(model_path=os.path.join("..", "models", "tcn_tracker", "multitracker_42.pth"), post_processor="joint") -pred = tracker(audio_path) -print(pred) +import numpy as np +import matplotlib.pyplot as plt +import librosa +import IPython.display as ipd + +#@title Plotting function +def plot_sonify(y, sr=22050, beats=None, labels=None, start_time=0, duration=None): + """ + Plots the waveform with optional beats and downbeats. If beats are None, only the waveform is plotted. + + Parameters: + - y: Audio signal waveform data. + - sr: Sampling rate of the audio signal. + - beats: Array of beat times (in seconds). If None, only the waveform is plotted. + - labels: Optional array of labels corresponding to beats. Downbeats should be labeled as 1. + - start_time: Start time in seconds for the plot window (default: 0). + - duration: Duration in seconds for the plot window. If None, plots entire signal. + """ + time = (np.arange(len(y))/sr) + + plt.figure(figsize=(100, 5)) + + # Apply time windowing if specified + plot_y = y + plot_time = time + + if duration is not None: + end_time = start_time + duration + # Find indices for the time window + start_idx = np.searchsorted(time, start_time) + end_idx = np.searchsorted(time, end_time) + plot_time = time[start_idx:end_idx] + plot_y = y[start_idx:end_idx] + + # Plot waveform + plt.plot(plot_time, plot_y) + plt.ylabel('Amplitude') + plt.xlabel('Time (sec)') + plt.title("Waveform") + + # Set x-axis limits if duration is specified + if duration is not None: + plt.xlim(start_time, start_time + duration) + + # Check if beats are provided + if beats is not None: + # Filter beats to the time window if duration is specified + if duration is not None: + end_time = start_time + duration + mask = (beats >= start_time) & (beats <= end_time) + windowed_beats = beats[mask] + windowed_labels = labels[mask] if labels is not None else None + else: + windowed_beats = beats + windowed_labels = labels + + # Separate beats into downbeats and other beats + if windowed_labels is not None: + windowed_labels = np.array(windowed_labels) # Ensure labels is a NumPy array + downbeat_times = windowed_beats[windowed_labels == 1] # Downbeats (label == 1) + beat_times = windowed_beats[windowed_labels != 1] # Other beats (label != 1) + plt.title("Waveform with Beats and Downbeats") + else: + downbeat_times = np.array([]) # Empty array for downbeats + beat_times = windowed_beats # All beats + plt.title("Waveform with Beats") + + # Plot vertical lines for downbeats and beats + ylim = np.max(np.abs(plot_y)) + plt.vlines(downbeat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='DownBeats', color='black', linewidths=1, linestyle='--') + plt.vlines(beat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='Beats', color='red', linewidths=1, linestyle=':') + + plt.legend(frameon=True, framealpha=1.0, edgecolor='black', loc='lower left', bbox_to_anchor=(0, 0.05), fontsize='small') + + # Generate click sounds for downbeats and beats + # Use the windowed audio for click generation + audio_length = len(plot_y) + + # Adjust beat times relative to the start of the windowed audio + if duration is not None: + adjusted_downbeat_times = downbeat_times - start_time + adjusted_beat_times = beat_times - start_time + + # Filter out negative times and times beyond the audio length + time_limit = len(plot_y) / sr + adjusted_downbeat_times = adjusted_downbeat_times[(adjusted_downbeat_times >= 0) & (adjusted_downbeat_times <= time_limit)] + adjusted_beat_times = adjusted_beat_times[(adjusted_beat_times >= 0) & (adjusted_beat_times <= time_limit)] + else: + adjusted_downbeat_times = downbeat_times + adjusted_beat_times = beat_times + + downbeat_click = librosa.clicks(times=adjusted_downbeat_times, sr=sr, click_freq=1000, length=audio_length, click_duration=0.1) + beat_click = librosa.clicks(times=adjusted_beat_times, sr=sr, click_freq=500, length=audio_length, click_duration=0.1) + + # Combine original audio with clicks + combined_audio = plot_y + beat_click + downbeat_click + + # Normalize combined audio to prevent clipping + combined_audio = combined_audio / np.max(np.abs(combined_audio)) + + else: + combined_audio = plot_y + + # Play the audio with clicks + audio_widget = ipd.Audio(combined_audio, rate=sr) + + plt.savefig('plot.png') + plt.close() + + return audio_widget + + +def plot_spec(y, sr, gt_beats=None, gt_labels=None, pred_beats=None, pred_labels=None, start_time=0, duration=30): + """ + Plots spectrogram with optional ground truth and predicted beats/downbeats. + + Parameters: + - y: Audio signal + - sr: Sampling rate + - gt_beats: Ground truth beat times (optional) + - gt_labels: Ground truth beat labels (optional, 1 for downbeats, other values for beats) + - pred_beats: Predicted beat times (optional) + - pred_labels: Predicted beat labels (optional, 1 for downbeats, other values for beats) + - start_time: Start time in seconds for the plot window (default: 0) + - duration: Duration in seconds for the plot window (default: 20) + """ + hop_length = 512 + + # Calculate end time + end_time = start_time + duration + + # Extract audio segment + start_sample = int(start_time * sr) + end_sample = int(end_time * sr) + audio_segment = y[start_sample:end_sample] + + # Generate spectrogram + spec = librosa.amplitude_to_db(np.abs(librosa.stft(audio_segment, hop_length=hop_length)), ref=np.max) + librosa.display.specshow(spec, y_axis='log', sr=sr, hop_length=hop_length, x_axis='time') + plt.title(f"Log-frequency power spectrogram of track ({start_time}s - {end_time}s)") + plt.colorbar(format="%+2.f dB") + + # Process ground truth beats if provided + if gt_beats is not None: + # Filter beat times and labels to the specified window + gt_mask = (gt_beats >= start_time) & (gt_beats <= end_time) + gt_beats_windowed = gt_beats[gt_mask] + gt_labels_windowed = gt_labels[gt_mask] if gt_labels is not None else None + + # Adjust times relative to the start of the window + gt_beats_adjusted = gt_beats_windowed - start_time + + # Separate beats and downbeats for ground truth + if gt_labels_windowed is not None: + gt_labels_windowed = np.array(gt_labels_windowed) + gt_downbeats = gt_beats_adjusted[gt_labels_windowed == 1] + gt_beats_only = gt_beats_adjusted[gt_labels_windowed != 1] + else: + gt_downbeats = np.array([]) + gt_beats_only = gt_beats_adjusted + + # Plot ground truth annotations in the upper part + if len(gt_beats_only) > 0: + plt.vlines(gt_beats_only, hop_length * 2, sr / 2, linestyles='dotted', color='w', alpha=0.8) + if len(gt_downbeats) > 0: + plt.vlines(gt_downbeats, hop_length * 2, sr / 2, color='w', alpha=0.8) + plt.text(duration * 0.35, hop_length * 1.65, 'GT Annotations (above)', color='w', fontsize=10, fontweight='bold') + + # Process predicted beats if provided + if pred_beats is not None: + # Filter beat times and labels to the specified window + pred_mask = (pred_beats >= start_time) & (pred_beats <= end_time) + pred_beats_windowed = pred_beats[pred_mask] + pred_labels_windowed = pred_labels[pred_mask] if pred_labels is not None else None + + # Adjust times relative to the start of the window + pred_beats_adjusted = pred_beats_windowed - start_time + + # Separate beats and downbeats for predictions + if pred_labels_windowed is not None: + pred_labels_windowed = np.array(pred_labels_windowed) + pred_downbeats = pred_beats_adjusted[pred_labels_windowed == 1] + pred_beats_only = pred_beats_adjusted[pred_labels_windowed != 1] + else: + pred_downbeats = np.array([]) + pred_beats_only = pred_beats_adjusted + + # Plot predictions in the lower part + if len(pred_beats_only) > 0: + plt.vlines(pred_beats_only, 0, hop_length, linestyles='dotted', color='w', alpha=0.8) + if len(pred_downbeats) > 0: + plt.vlines(pred_downbeats, 0, hop_length, color='w', alpha=0.8) + plt.text(duration * 0.35, hop_length * 1.1, 'Predictions (below)', color='w', fontsize=10, fontweight='bold') + + plt.show() + +audio_path = "../raksha_bettare.wav" + +print(compiam.list_models()) + +tracker = compiam.load_model('rhythm:tcn-carnatic') +beats, beat_positions = tracker.predict(audio_path) + +print(beats) + +y, sr = librosa.load(audio_path) + +plot_sonify(y=y, sr=sr, beats=beats) From b829261c4e364d24c863c0f4b15f046af2d55ba1 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Mon, 2 Feb 2026 16:45:58 +0100 Subject: [PATCH 04/17] pending decision on madmom --- compiam/rhythm/meter/tcn_carnatic/__init__.py | 128 ++++++++++++------ compiam/rhythm/meter/tcn_carnatic/post.py | 10 +- madmom_patch.py | 54 ++++++++ main.py | 12 +- plot.png | Bin 0 -> 462507 bytes pyproject.toml | 5 +- tests/resources/rhythm/beat_test.wav | Bin 0 -> 353018 bytes tests/rhythm/test_tcn_carnatic.py | 55 ++++++++ 8 files changed, 210 insertions(+), 54 deletions(-) create mode 100644 madmom_patch.py create mode 100644 plot.png create mode 100644 tests/resources/rhythm/beat_test.wav create mode 100644 tests/rhythm/test_tcn_carnatic.py diff --git a/compiam/rhythm/meter/tcn_carnatic/__init__.py b/compiam/rhythm/meter/tcn_carnatic/__init__.py index 177ed1b8..7718f3e7 100644 --- a/compiam/rhythm/meter/tcn_carnatic/__init__.py +++ b/compiam/rhythm/meter/tcn_carnatic/__init__.py @@ -12,23 +12,35 @@ from compiam.rhythm.meter.tcn_carnatic.post import beat_tracker, joint_tracker, sequential_tracker from compiam.utils.download import download_remote_model from compiam.utils import get_logger, WORKDIR +from compiam.io import write_csv logger = get_logger(__name__) -class TCNTracker: +class TCNTracker(object): + """TCN beat tracker tuned to Carnatic Music.""" def __init__(self, post_processor="joint", model_version=42, model_path=None, download_link=None, download_checksum=None, gpu=-1): + """TCN beat tracker init method. + + :param post_processor: Post-processing method to use. Choose from 'joint', or 'sequential'. + :param model_version: Version of the pre-trained model to use. Choose from 42, 52, or 62. + :param model_path: path to file to the model weights. + :param download_link: link to the remote pre-trained model. + :param download_checksum: checksum of the model file. + """ ### IMPORTING OPTIONAL DEPENDENCIES try: global torch import torch + global madmom + import madmom global MultiTracker from compiam.rhythm.meter.tcn_carnatic.model import MultiTracker except: raise ImportError( - "In order to use this tool you need to have torch installed. " - "Install compIAM with torch support: pip install 'compiam[torch]'" + "In order to use this tool you need to have torch and madmom installed. " + "Install compIAM with torch and madmom support: pip install 'compiam[torch,madmom]'" ) ### if post_processor not in ["beat", "joint", "sequential"]: @@ -55,49 +67,14 @@ def __init__(self, post_processor="joint", model_version=42, model_path=None, do self.post_processor = joint_tracker if post_processor == "joint" else \ sequential_tracker - @torch.no_grad() - def predict(self, audio_path: str) -> Dict: - - if self.trained is False: - raise ModelNotTrainedError( - """Model is not trained. Please load model before running inference! - You can load the pre-trained instance with the load_model wrapper.""" - ) - - features = self.preprocess_audio(audio_path) - x = torch.from_numpy(features).to(self.device) - output = self.model(x) - beats_act = output["beats"].squeeze().detach().cpu().numpy() - downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() - - if self.post_processor == beat_tracker: - beats, beat_positions = self.post_processor(beats_act) - else: - pred = self.post_processor(beats_act, downbeats_act) - return pred - - def preprocess_audio(self, audio_path: str) -> np.ndarray: - audio, sr = madmom.io.audio.load_audio_file(audio_path) - - if audio.shape[0] == 2: - audio = audio.mean(axis=0) - - s = madmom.audio.Signal(audio, sr, num_channels=1) - x = self.pre_processor(s) - - pad_start = np.repeat(x[:1], self.pad_frames, axis=0) - pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) - x_padded = np.concatenate((pad_start, x, pad_stop)) - - x_final = np.expand_dims(np.expand_dims(x_padded, axis=0), axis=0) - - return x_final def _build_model(self): + """Build the TCN model.""" model = MultiTracker().to(self.device) model.eval() return model + def load_model(self, model_path): """Load pre-trained model weights.""" if not os.path.exists(os.path.join(model_path, self.model_version)): @@ -108,6 +85,7 @@ def load_model(self, model_path): self.model_path = model_path self.trained = True + def download_model(self, model_path=None, force_overwrite=True): """Download pre-trained model.""" download_path = ( @@ -126,6 +104,76 @@ def download_model(self, model_path=None, force_overwrite=True): force_overwrite=force_overwrite, ) + + @torch.no_grad() + def predict(self, input_data: str) -> Dict: + """Run inference on input audio file. + + :param input_data: path to audio file or numpy array like audio signal. + + :returns: a 2-D list with beats and beat positions. + """ + + if self.trained is False: + raise ModelNotTrainedError( + """Model is not trained. Please load model before running inference! + You can load the pre-trained instance with the load_model wrapper.""" + ) + + features = self.preprocess_audio(input_data) + x = torch.from_numpy(features).to(self.device) + output = self.model(x) + beats_act = output["beats"].squeeze().detach().cpu().numpy() + downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() + + pred = self.post_processor(beats_act, downbeats_act) + + return pred + + def preprocess_audio(self, input_data: str, input_sr: int = 44100) -> np.ndarray: + """Preprocess input audio file to extract features for inference. + :param audio_path: Path to the input audio file. + :param input_sr: Sampling rate of the input audio file. + + :returns: Preprocessed features as a numpy array. + """ + if isinstance(input_data, str): + if not os.path.exists(input_data): + raise FileNotFoundError("Target audio not found.") + audio, sr = madmom.io.audio.load_audio_file(input_data) + if audio.shape[0] == 2: + audio = audio.mean(axis=0) + s = madmom.audio.Signal(audio, sr, num_channels=1) + elif isinstance(input_data, np.ndarray): + audio = input_data + if audio.shape[0] == 2: + audio = audio.mean(axis=0) + s = madmom.audio.Signal(audio, input_sr, num_channels=1) + else: + raise ValueError("Input must be path to audio signal or an audio array") + + x = self.pre_processor(s) + + pad_start = np.repeat(x[:1], self.pad_frames, axis=0) + pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) + x_padded = np.concatenate((pad_start, x, pad_stop)) + + x_final = np.expand_dims(np.expand_dims(x_padded, axis=0), axis=0) + + return x_final + + @staticmethod + def save_pitch(data, output_path): + """Calling the write_csv function in compiam.io to write the output beat track in a file + + :param data: the data to write + :param output_path: the path where the data is going to be stored + + :returns: None + """ + return write_csv(data, output_path) + + def select_gpu(self, gpu="-1"): """Select the GPU to use for inference. diff --git a/compiam/rhythm/meter/tcn_carnatic/post.py b/compiam/rhythm/meter/tcn_carnatic/post.py index b1f770db..e31c7c35 100644 --- a/compiam/rhythm/meter/tcn_carnatic/post.py +++ b/compiam/rhythm/meter/tcn_carnatic/post.py @@ -38,10 +38,7 @@ def joint_tracker(beats_act, downbeats_act): combined_act = np.vstack((np.maximum(beats_act - downbeats_act, 0), downbeats_act)).T pred = downbeat_tracker(combined_act) - beats = pred[:, 0] - beat_positions = pred[:, 1] - - return beats, beat_positions + return pred def sequential_tracker(beats_act, downbeats_act): beats_act = clip_probabilities(beats_act) @@ -62,7 +59,4 @@ def sequential_tracker(beats_act, downbeats_act): except IndexError: pred = np.empty((0, 2)) - beats = pred[:, 0] - beat_positions = pred[:, 1] - - return beats, beat_positions + return pred diff --git a/madmom_patch.py b/madmom_patch.py new file mode 100644 index 00000000..b3c4dd5e --- /dev/null +++ b/madmom_patch.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +""" +madmom_patch.py +---------------- +Fixes Python 3.10+ compatibility issue in madmom by replacing: + from collections import MutableSequence +with: + from collections.abc import MutableSequence + +Run this script **once after installing dependencies**: + python madmom_patch.py +""" + +import sys +import os +import importlib.util + +print("🔍 Locating madmom installation...") + +# Find the madmom module path without importing it +spec = importlib.util.find_spec("madmom") +if spec is None or not spec.origin: + print("⚠ī¸ madmom is not installed. Please run:") + print(" pip install -r requirements.txt") + sys.exit(1) + +madmom_dir = os.path.dirname(spec.origin) +processors_file = os.path.join(madmom_dir, "processors.py") + +if not os.path.isfile(processors_file): + print(f"❌ processors.py not found at: {processors_file}") + sys.exit(1) + +try: + with open(processors_file, "r", encoding="utf-8") as f: + content = f.read() + + if "from collections import MutableSequence" not in content: + print("ℹī¸ Patch not needed. madmom is already compatible.") + sys.exit(0) + + content = content.replace( + "from collections import MutableSequence", + "from collections.abc import MutableSequence" + ) + + with open(processors_file, "w", encoding="utf-8") as f: + f.write(content) + + print(f"✅ Successfully patched madmom at:\n {processors_file}") + +except Exception as e: + print(f"❌ Failed to patch madmom: {e}") + sys.exit(1) diff --git a/main.py b/main.py index 7422a0c7..f7771bde 100644 --- a/main.py +++ b/main.py @@ -199,14 +199,18 @@ def plot_spec(y, sr, gt_beats=None, gt_labels=None, pred_beats=None, pred_labels plt.show() -audio_path = "../raksha_bettare.wav" +audio_path = "../Devi Pavane.wav" print(compiam.list_models()) +from compiam.rhythm.meter.tcn_carnatic import TCNTracker +tracker = TCNTracker(post_processor="sequential") +tracker.trained = True +pred = tracker.predict(audio_path) -tracker = compiam.load_model('rhythm:tcn-carnatic') -beats, beat_positions = tracker.predict(audio_path) +beats = pred[:, 0] -print(beats) +print(pred) +print(pred.shape) y, sr = librosa.load(audio_path) diff --git a/plot.png b/plot.png new file mode 100644 index 0000000000000000000000000000000000000000..9433ad985abd288da82a84510955c772a6d08ecf GIT binary patch literal 462507 zcmeFa2UHVj+Xfs(MMY#06#)SkDGCTk?^uvt6p-GO-bCpg6cr0adM8K~k=|Q^B3%eY zYJjNp8hRih@IS+@yS}Tw-+Ol7bH4xo&o{@-Fq6qVx+tRDL?Rc+Wjfwi@K)y&ro};aXgJM$~6*_xP^D1bCe= z@}oUV;0b{&9rt+KLS8z1v-I@zCx?z?#17Oej+`md$nTGTKGZ&dk!mmFyV#<+Pe*Zm 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+ + tracker = TCNTracker() + with pytest.raises(ModelNotTrainedError): + tracker.predict(os.path.join(TESTDIR, "resources", "rhythm", "hola.wav")) + tracker.trained = True + with pytest.raises(FileNotFoundError): + tracker.predict(os.path.join(TESTDIR, "resources", "rhythm", "hola.wav")) + beats = tracker.predict( + os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") + ) + + print(beats.shape) + + audio_in, sr = librosa.load( + os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") + ) + beats_2 = tracker.predict(audio_in) + + assert isinstance(beats, np.ndarray) + assert isinstance(beats_2, np.ndarray) + +def test_predict_sequential(): + from compiam.rhythm.meter.tcn_carnatic import TCNTracker + + tracker = TCNTracker(post_processor="sequential") + with pytest.raises(ModelNotTrainedError): + tracker.predict(os.path.join(TESTDIR, "resources", "rhythm", "hola.wav")) + tracker.trained = True + with pytest.raises(FileNotFoundError): + tracker.predict(os.path.join(TESTDIR, "resources", "rhythm", "hola.wav")) + beats = tracker.predict( + os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") + ) + + print(beats.shape) + + audio_in, sr = librosa.load( + os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") + ) + beats_2 = tracker.predict(audio_in) + + assert isinstance(beats, np.ndarray) + assert isinstance(beats_2, np.ndarray) From 64c75320ef1a237455628660c69fe4bb9dc9e3a3 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Mon, 2 Feb 2026 16:46:15 +0100 Subject: [PATCH 05/17] pending decision on madmom --- main.py | 217 ------------------------------------------------------- plot.png | Bin 462507 -> 0 bytes 2 files changed, 217 deletions(-) delete mode 100644 main.py delete mode 100644 plot.png diff --git a/main.py b/main.py deleted file mode 100644 index f7771bde..00000000 --- a/main.py +++ /dev/null @@ -1,217 +0,0 @@ -import compiam -import os - - - -import numpy as np -import matplotlib.pyplot as plt -import librosa -import IPython.display as ipd - -#@title Plotting function -def plot_sonify(y, sr=22050, beats=None, labels=None, start_time=0, duration=None): - """ - Plots the waveform with optional beats and downbeats. If beats are None, only the waveform is plotted. - - Parameters: - - y: Audio signal waveform data. - - sr: Sampling rate of the audio signal. - - beats: Array of beat times (in seconds). If None, only the waveform is plotted. - - labels: Optional array of labels corresponding to beats. Downbeats should be labeled as 1. - - start_time: Start time in seconds for the plot window (default: 0). - - duration: Duration in seconds for the plot window. If None, plots entire signal. - """ - time = (np.arange(len(y))/sr) - - plt.figure(figsize=(100, 5)) - - # Apply time windowing if specified - plot_y = y - plot_time = time - - if duration is not None: - end_time = start_time + duration - # Find indices for the time window - start_idx = np.searchsorted(time, start_time) - end_idx = np.searchsorted(time, end_time) - plot_time = time[start_idx:end_idx] - plot_y = y[start_idx:end_idx] - - # Plot waveform - plt.plot(plot_time, plot_y) - plt.ylabel('Amplitude') - plt.xlabel('Time (sec)') - plt.title("Waveform") - - # Set x-axis limits if duration is specified - if duration is not None: - plt.xlim(start_time, start_time + duration) - - # Check if beats are provided - if beats is not None: - # Filter beats to the time window if duration is specified - if duration is not None: - end_time = start_time + duration - mask = (beats >= start_time) & (beats <= end_time) - windowed_beats = beats[mask] - windowed_labels = labels[mask] if labels is not None else None - else: - windowed_beats = beats - windowed_labels = labels - - # Separate beats into downbeats and other beats - if windowed_labels is not None: - windowed_labels = np.array(windowed_labels) # Ensure labels is a NumPy array - downbeat_times = windowed_beats[windowed_labels == 1] # Downbeats (label == 1) - beat_times = windowed_beats[windowed_labels != 1] # Other beats (label != 1) - plt.title("Waveform with Beats and Downbeats") - else: - downbeat_times = np.array([]) # Empty array for downbeats - beat_times = windowed_beats # All beats - plt.title("Waveform with Beats") - - # Plot vertical lines for downbeats and beats - ylim = np.max(np.abs(plot_y)) - plt.vlines(downbeat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='DownBeats', color='black', linewidths=1, linestyle='--') - plt.vlines(beat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='Beats', color='red', linewidths=1, linestyle=':') - - plt.legend(frameon=True, framealpha=1.0, edgecolor='black', loc='lower left', bbox_to_anchor=(0, 0.05), fontsize='small') - - # Generate click sounds for downbeats and beats - # Use the windowed audio for click generation - audio_length = len(plot_y) - - # Adjust beat times relative to the start of the windowed audio - if duration is not None: - adjusted_downbeat_times = downbeat_times - start_time - adjusted_beat_times = beat_times - start_time - - # Filter out negative times and times beyond the audio length - time_limit = len(plot_y) / sr - adjusted_downbeat_times = adjusted_downbeat_times[(adjusted_downbeat_times >= 0) & (adjusted_downbeat_times <= time_limit)] - adjusted_beat_times = adjusted_beat_times[(adjusted_beat_times >= 0) & (adjusted_beat_times <= time_limit)] - else: - adjusted_downbeat_times = downbeat_times - adjusted_beat_times = beat_times - - downbeat_click = librosa.clicks(times=adjusted_downbeat_times, sr=sr, click_freq=1000, length=audio_length, click_duration=0.1) - beat_click = librosa.clicks(times=adjusted_beat_times, sr=sr, click_freq=500, length=audio_length, click_duration=0.1) - - # Combine original audio with clicks - combined_audio = plot_y + beat_click + downbeat_click - - # Normalize combined audio to prevent clipping - combined_audio = combined_audio / np.max(np.abs(combined_audio)) - - else: - combined_audio = plot_y - - # Play the audio with clicks - audio_widget = ipd.Audio(combined_audio, rate=sr) - - plt.savefig('plot.png') - plt.close() - - return audio_widget - - -def plot_spec(y, sr, gt_beats=None, gt_labels=None, pred_beats=None, pred_labels=None, start_time=0, duration=30): - """ - Plots spectrogram with optional ground truth and predicted beats/downbeats. - - Parameters: - - y: Audio signal - - sr: Sampling rate - - gt_beats: Ground truth beat times (optional) - - gt_labels: Ground truth beat labels (optional, 1 for downbeats, other values for beats) - - pred_beats: Predicted beat times (optional) - - pred_labels: Predicted beat labels (optional, 1 for downbeats, other values for beats) - - start_time: Start time in seconds for the plot window (default: 0) - - duration: Duration in seconds for the plot window (default: 20) - """ - hop_length = 512 - - # Calculate end time - end_time = start_time + duration - - # Extract audio segment - start_sample = int(start_time * sr) - end_sample = int(end_time * sr) - audio_segment = y[start_sample:end_sample] - - # Generate spectrogram - spec = librosa.amplitude_to_db(np.abs(librosa.stft(audio_segment, hop_length=hop_length)), ref=np.max) - librosa.display.specshow(spec, y_axis='log', sr=sr, hop_length=hop_length, x_axis='time') - plt.title(f"Log-frequency power spectrogram of track ({start_time}s - {end_time}s)") - plt.colorbar(format="%+2.f dB") - - # Process ground truth beats if provided - if gt_beats is not None: - # Filter beat times and labels to the specified window - gt_mask = (gt_beats >= start_time) & (gt_beats <= end_time) - gt_beats_windowed = gt_beats[gt_mask] - gt_labels_windowed = gt_labels[gt_mask] if gt_labels is not None else None - - # Adjust times relative to the start of the window - gt_beats_adjusted = gt_beats_windowed - start_time - - # Separate beats and downbeats for ground truth - if gt_labels_windowed is not None: - gt_labels_windowed = np.array(gt_labels_windowed) - gt_downbeats = gt_beats_adjusted[gt_labels_windowed == 1] - gt_beats_only = gt_beats_adjusted[gt_labels_windowed != 1] - else: - gt_downbeats = np.array([]) - gt_beats_only = gt_beats_adjusted - - # Plot ground truth annotations in the upper part - if len(gt_beats_only) > 0: - plt.vlines(gt_beats_only, hop_length * 2, sr / 2, linestyles='dotted', color='w', alpha=0.8) - if len(gt_downbeats) > 0: - plt.vlines(gt_downbeats, hop_length * 2, sr / 2, color='w', alpha=0.8) - plt.text(duration * 0.35, hop_length * 1.65, 'GT Annotations (above)', color='w', fontsize=10, fontweight='bold') - - # Process predicted beats if provided - if pred_beats is not None: - # Filter beat times and labels to the specified window - pred_mask = (pred_beats >= start_time) & (pred_beats <= end_time) - pred_beats_windowed = pred_beats[pred_mask] - pred_labels_windowed = pred_labels[pred_mask] if pred_labels is not None else None - - # Adjust times relative to the start of the window - pred_beats_adjusted = pred_beats_windowed - start_time - - # Separate beats and downbeats for predictions - if pred_labels_windowed is not None: - pred_labels_windowed = np.array(pred_labels_windowed) - pred_downbeats = pred_beats_adjusted[pred_labels_windowed == 1] - pred_beats_only = pred_beats_adjusted[pred_labels_windowed != 1] - else: - pred_downbeats = np.array([]) - pred_beats_only = pred_beats_adjusted - - # Plot predictions in the lower part - if len(pred_beats_only) > 0: - plt.vlines(pred_beats_only, 0, hop_length, linestyles='dotted', color='w', alpha=0.8) - if len(pred_downbeats) > 0: - plt.vlines(pred_downbeats, 0, hop_length, color='w', alpha=0.8) - plt.text(duration * 0.35, hop_length * 1.1, 'Predictions (below)', color='w', fontsize=10, fontweight='bold') - - plt.show() - -audio_path = "../Devi Pavane.wav" - -print(compiam.list_models()) -from compiam.rhythm.meter.tcn_carnatic import TCNTracker -tracker = TCNTracker(post_processor="sequential") -tracker.trained = True -pred = tracker.predict(audio_path) - -beats = pred[:, 0] - -print(pred) -print(pred.shape) - -y, sr = librosa.load(audio_path) - -plot_sonify(y=y, sr=sr, beats=beats) diff --git a/plot.png b/plot.png deleted file mode 100644 index 9433ad985abd288da82a84510955c772a6d08ecf..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 462507 zcmeFa2UHVj+Xfs(MMY#06#)SkDGCTk?^uvt6p-GO-bCpg6cr0adM8K~k=|Q^B3%eY zYJjNp8hRih@IS+@yS}Tw-+Ol7bH4xo&o{@-Fq6qVx+tRDL?Rc+Wjfwi@K)y&ro};aXgJM$~6*_xP^D1bCe= z@}oUV;0b{&9rt+KLS8z1v-I@zCx?z?#17Oej+`md$nTGTKGZ&dk!mmFyV#<+Pe*Zm zc1?V8bFQd&vZ&W{-Z6>P9qtcT$nLS;54!aqY;s{5&yW5G*Fl$`9f3F%;pXNetEzgyr?+n-$weh+w zeo7gw=9CFM6*?L$wr~w4vGK{@XynrOjz9nDtCRHKpN9E4RN`l{)w47lhMoH)CWn$d 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zJV0@LeC(X}8FM{IA?^UoN*7wUD94ky9-D;93kVPub38vj4fy{J=RRuG3AN_x@MLOi$vJ@Y6<)?4|&pNlCa_7P+mB*IOLFA%3%hzZ{FWvgb{{Zr6&UpX; From 4d1b6066909b87f6317b253ea9110376135fec73 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Thu, 5 Feb 2026 13:29:14 +0100 Subject: [PATCH 06/17] docs --- docs/source/rhythm.rst | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/rhythm.rst b/docs/source/rhythm.rst index 2ca5ef18..96238d23 100644 --- a/docs/source/rhythm.rst +++ b/docs/source/rhythm.rst @@ -24,4 +24,10 @@ Akshara Pulse Tracker --------------------- .. autoclass:: compiam.rhythm.meter.akshara_pulse_tracker.AksharaPulseTracker - :members: +:members: + +TCN Carnatic +--------------------- + +.. autoclass:: compiam.rhythm.meter.tcn_carnatic.TCNTracker +:members: From c67b9a9dd227b833aa4a5c64c77ca2d8b008e0b3 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Thu, 5 Feb 2026 13:44:21 +0100 Subject: [PATCH 07/17] docs --- docs/source/rhythm.rst | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/source/rhythm.rst b/docs/source/rhythm.rst index 96238d23..1e94a0b7 100644 --- a/docs/source/rhythm.rst +++ b/docs/source/rhythm.rst @@ -24,10 +24,13 @@ Akshara Pulse Tracker --------------------- .. autoclass:: compiam.rhythm.meter.akshara_pulse_tracker.AksharaPulseTracker -:members: + :members: TCN Carnatic --------------------- +.. note:: + REQUIRES: torch + .. autoclass:: compiam.rhythm.meter.tcn_carnatic.TCNTracker -:members: + :members: From 9bd87f9ebbe1278c5c7e7561ed2139d49c9e45e1 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 10 Feb 2026 13:08:46 +0100 Subject: [PATCH 08/17] fork of madmom release version --- main.py | 217 +++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 218 insertions(+), 1 deletion(-) create mode 100644 main.py diff --git a/main.py b/main.py new file mode 100644 index 00000000..f7771bde --- /dev/null +++ b/main.py @@ -0,0 +1,217 @@ +import compiam +import os + + + +import numpy as np +import matplotlib.pyplot as plt +import librosa +import IPython.display as ipd + +#@title Plotting function +def plot_sonify(y, sr=22050, beats=None, labels=None, start_time=0, duration=None): + """ + Plots the waveform with optional beats and downbeats. If beats are None, only the waveform is plotted. + + Parameters: + - y: Audio signal waveform data. + - sr: Sampling rate of the audio signal. + - beats: Array of beat times (in seconds). If None, only the waveform is plotted. + - labels: Optional array of labels corresponding to beats. Downbeats should be labeled as 1. + - start_time: Start time in seconds for the plot window (default: 0). + - duration: Duration in seconds for the plot window. If None, plots entire signal. + """ + time = (np.arange(len(y))/sr) + + plt.figure(figsize=(100, 5)) + + # Apply time windowing if specified + plot_y = y + plot_time = time + + if duration is not None: + end_time = start_time + duration + # Find indices for the time window + start_idx = np.searchsorted(time, start_time) + end_idx = np.searchsorted(time, end_time) + plot_time = time[start_idx:end_idx] + plot_y = y[start_idx:end_idx] + + # Plot waveform + plt.plot(plot_time, plot_y) + plt.ylabel('Amplitude') + plt.xlabel('Time (sec)') + plt.title("Waveform") + + # Set x-axis limits if duration is specified + if duration is not None: + plt.xlim(start_time, start_time + duration) + + # Check if beats are provided + if beats is not None: + # Filter beats to the time window if duration is specified + if duration is not None: + end_time = start_time + duration + mask = (beats >= start_time) & (beats <= end_time) + windowed_beats = beats[mask] + windowed_labels = labels[mask] if labels is not None else None + else: + windowed_beats = beats + windowed_labels = labels + + # Separate beats into downbeats and other beats + if windowed_labels is not None: + windowed_labels = np.array(windowed_labels) # Ensure labels is a NumPy array + downbeat_times = windowed_beats[windowed_labels == 1] # Downbeats (label == 1) + beat_times = windowed_beats[windowed_labels != 1] # Other beats (label != 1) + plt.title("Waveform with Beats and Downbeats") + else: + downbeat_times = np.array([]) # Empty array for downbeats + beat_times = windowed_beats # All beats + plt.title("Waveform with Beats") + + # Plot vertical lines for downbeats and beats + ylim = np.max(np.abs(plot_y)) + plt.vlines(downbeat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='DownBeats', color='black', linewidths=1, linestyle='--') + plt.vlines(beat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='Beats', color='red', linewidths=1, linestyle=':') + + plt.legend(frameon=True, framealpha=1.0, edgecolor='black', loc='lower left', bbox_to_anchor=(0, 0.05), fontsize='small') + + # Generate click sounds for downbeats and beats + # Use the windowed audio for click generation + audio_length = len(plot_y) + + # Adjust beat times relative to the start of the windowed audio + if duration is not None: + adjusted_downbeat_times = downbeat_times - start_time + adjusted_beat_times = beat_times - start_time + + # Filter out negative times and times beyond the audio length + time_limit = len(plot_y) / sr + adjusted_downbeat_times = adjusted_downbeat_times[(adjusted_downbeat_times >= 0) & (adjusted_downbeat_times <= time_limit)] + adjusted_beat_times = adjusted_beat_times[(adjusted_beat_times >= 0) & (adjusted_beat_times <= time_limit)] + else: + adjusted_downbeat_times = downbeat_times + adjusted_beat_times = beat_times + + downbeat_click = librosa.clicks(times=adjusted_downbeat_times, sr=sr, click_freq=1000, length=audio_length, click_duration=0.1) + beat_click = librosa.clicks(times=adjusted_beat_times, sr=sr, click_freq=500, length=audio_length, click_duration=0.1) + + # Combine original audio with clicks + combined_audio = plot_y + beat_click + downbeat_click + + # Normalize combined audio to prevent clipping + combined_audio = combined_audio / np.max(np.abs(combined_audio)) + + else: + combined_audio = plot_y + + # Play the audio with clicks + audio_widget = ipd.Audio(combined_audio, rate=sr) + + plt.savefig('plot.png') + plt.close() + + return audio_widget + + +def plot_spec(y, sr, gt_beats=None, gt_labels=None, pred_beats=None, pred_labels=None, start_time=0, duration=30): + """ + Plots spectrogram with optional ground truth and predicted beats/downbeats. + + Parameters: + - y: Audio signal + - sr: Sampling rate + - gt_beats: Ground truth beat times (optional) + - gt_labels: Ground truth beat labels (optional, 1 for downbeats, other values for beats) + - pred_beats: Predicted beat times (optional) + - pred_labels: Predicted beat labels (optional, 1 for downbeats, other values for beats) + - start_time: Start time in seconds for the plot window (default: 0) + - duration: Duration in seconds for the plot window (default: 20) + """ + hop_length = 512 + + # Calculate end time + end_time = start_time + duration + + # Extract audio segment + start_sample = int(start_time * sr) + end_sample = int(end_time * sr) + audio_segment = y[start_sample:end_sample] + + # Generate spectrogram + spec = librosa.amplitude_to_db(np.abs(librosa.stft(audio_segment, hop_length=hop_length)), ref=np.max) + librosa.display.specshow(spec, y_axis='log', sr=sr, hop_length=hop_length, x_axis='time') + plt.title(f"Log-frequency power spectrogram of track ({start_time}s - {end_time}s)") + plt.colorbar(format="%+2.f dB") + + # Process ground truth beats if provided + if gt_beats is not None: + # Filter beat times and labels to the specified window + gt_mask = (gt_beats >= start_time) & (gt_beats <= end_time) + gt_beats_windowed = gt_beats[gt_mask] + gt_labels_windowed = gt_labels[gt_mask] if gt_labels is not None else None + + # Adjust times relative to the start of the window + gt_beats_adjusted = gt_beats_windowed - start_time + + # Separate beats and downbeats for ground truth + if gt_labels_windowed is not None: + gt_labels_windowed = np.array(gt_labels_windowed) + gt_downbeats = gt_beats_adjusted[gt_labels_windowed == 1] + gt_beats_only = gt_beats_adjusted[gt_labels_windowed != 1] + else: + gt_downbeats = np.array([]) + gt_beats_only = gt_beats_adjusted + + # Plot ground truth annotations in the upper part + if len(gt_beats_only) > 0: + plt.vlines(gt_beats_only, hop_length * 2, sr / 2, linestyles='dotted', color='w', alpha=0.8) + if len(gt_downbeats) > 0: + plt.vlines(gt_downbeats, hop_length * 2, sr / 2, color='w', alpha=0.8) + plt.text(duration * 0.35, hop_length * 1.65, 'GT Annotations (above)', color='w', fontsize=10, fontweight='bold') + + # Process predicted beats if provided + if pred_beats is not None: + # Filter beat times and labels to the specified window + pred_mask = (pred_beats >= start_time) & (pred_beats <= end_time) + pred_beats_windowed = pred_beats[pred_mask] + pred_labels_windowed = pred_labels[pred_mask] if pred_labels is not None else None + + # Adjust times relative to the start of the window + pred_beats_adjusted = pred_beats_windowed - start_time + + # Separate beats and downbeats for predictions + if pred_labels_windowed is not None: + pred_labels_windowed = np.array(pred_labels_windowed) + pred_downbeats = pred_beats_adjusted[pred_labels_windowed == 1] + pred_beats_only = pred_beats_adjusted[pred_labels_windowed != 1] + else: + pred_downbeats = np.array([]) + pred_beats_only = pred_beats_adjusted + + # Plot predictions in the lower part + if len(pred_beats_only) > 0: + plt.vlines(pred_beats_only, 0, hop_length, linestyles='dotted', color='w', alpha=0.8) + if len(pred_downbeats) > 0: + plt.vlines(pred_downbeats, 0, hop_length, color='w', alpha=0.8) + plt.text(duration * 0.35, hop_length * 1.1, 'Predictions (below)', color='w', fontsize=10, fontweight='bold') + + plt.show() + +audio_path = "../Devi Pavane.wav" + +print(compiam.list_models()) +from compiam.rhythm.meter.tcn_carnatic import TCNTracker +tracker = TCNTracker(post_processor="sequential") +tracker.trained = True +pred = tracker.predict(audio_path) + +beats = pred[:, 0] + +print(pred) +print(pred.shape) + +y, sr = librosa.load(audio_path) + +plot_sonify(y=y, sr=sr, beats=beats) diff --git a/pyproject.toml b/pyproject.toml index 82549967..a12b4ea7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,7 +75,7 @@ essentia = [ "essentia", ] madmom = [ - "madmom", + "git+https://github.com/vivekvjyn/madmom.git", ] [project.urls] From 6e91350aefd32deefa0cffa51be51f8e90d0671f Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 10 Feb 2026 13:17:33 +0100 Subject: [PATCH 09/17] no madmom patch --- madmom_patch.py | 54 ------------------------------------------------- 1 file changed, 54 deletions(-) delete mode 100644 madmom_patch.py diff --git a/madmom_patch.py b/madmom_patch.py deleted file mode 100644 index b3c4dd5e..00000000 --- a/madmom_patch.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -""" -madmom_patch.py ----------------- -Fixes Python 3.10+ compatibility issue in madmom by replacing: - from collections import MutableSequence -with: - from collections.abc import MutableSequence - -Run this script **once after installing dependencies**: - python madmom_patch.py -""" - -import sys -import os -import importlib.util - -print("🔍 Locating madmom installation...") - -# Find the madmom module path without importing it -spec = importlib.util.find_spec("madmom") -if spec is None or not spec.origin: - print("⚠ī¸ madmom is not installed. Please run:") - print(" pip install -r requirements.txt") - sys.exit(1) - -madmom_dir = os.path.dirname(spec.origin) -processors_file = os.path.join(madmom_dir, "processors.py") - -if not os.path.isfile(processors_file): - print(f"❌ processors.py not found at: {processors_file}") - sys.exit(1) - -try: - with open(processors_file, "r", encoding="utf-8") as f: - content = f.read() - - if "from collections import MutableSequence" not in content: - print("ℹī¸ Patch not needed. madmom is already compatible.") - sys.exit(0) - - content = content.replace( - "from collections import MutableSequence", - "from collections.abc import MutableSequence" - ) - - with open(processors_file, "w", encoding="utf-8") as f: - f.write(content) - - print(f"✅ Successfully patched madmom at:\n {processors_file}") - -except Exception as e: - print(f"❌ Failed to patch madmom: {e}") - sys.exit(1) From aa63dad69280ed10c281aa8106f07f1c1cc0cb2f Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 10 Feb 2026 15:22:04 +0100 Subject: [PATCH 10/17] madmom fix --- madmom_patch.py | 54 +++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 55 insertions(+), 1 deletion(-) create mode 100644 madmom_patch.py diff --git a/madmom_patch.py b/madmom_patch.py new file mode 100644 index 00000000..b3c4dd5e --- /dev/null +++ b/madmom_patch.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +""" +madmom_patch.py +---------------- +Fixes Python 3.10+ compatibility issue in madmom by replacing: + from collections import MutableSequence +with: + from collections.abc import MutableSequence + +Run this script **once after installing dependencies**: + python madmom_patch.py +""" + +import sys +import os +import importlib.util + +print("🔍 Locating madmom installation...") + +# Find the madmom module path without importing it +spec = importlib.util.find_spec("madmom") +if spec is None or not spec.origin: + print("⚠ī¸ madmom is not installed. Please run:") + print(" pip install -r requirements.txt") + sys.exit(1) + +madmom_dir = os.path.dirname(spec.origin) +processors_file = os.path.join(madmom_dir, "processors.py") + +if not os.path.isfile(processors_file): + print(f"❌ processors.py not found at: {processors_file}") + sys.exit(1) + +try: + with open(processors_file, "r", encoding="utf-8") as f: + content = f.read() + + if "from collections import MutableSequence" not in content: + print("ℹī¸ Patch not needed. madmom is already compatible.") + sys.exit(0) + + content = content.replace( + "from collections import MutableSequence", + "from collections.abc import MutableSequence" + ) + + with open(processors_file, "w", encoding="utf-8") as f: + f.write(content) + + print(f"✅ Successfully patched madmom at:\n {processors_file}") + +except Exception as e: + print(f"❌ Failed to patch madmom: {e}") + sys.exit(1) diff --git a/pyproject.toml b/pyproject.toml index a12b4ea7..08b3d37e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,7 +75,7 @@ essentia = [ "essentia", ] madmom = [ - "git+https://github.com/vivekvjyn/madmom.git", + "madmom @ git+https://github.com/vivekvjyn/madmom.git", ] [project.urls] From 11a0313413813956742256d509ebdbadab2c8413 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 10 Feb 2026 15:31:29 +0100 Subject: [PATCH 11/17] readme update --- compiam/rhythm/README.md | 3 ++- madmom_patch.py | 54 ---------------------------------------- 2 files changed, 2 insertions(+), 55 deletions(-) delete mode 100644 madmom_patch.py diff --git a/compiam/rhythm/README.md b/compiam/rhythm/README.md index 6fddc88d..e4a17534 100644 --- a/compiam/rhythm/README.md +++ b/compiam/rhythm/README.md @@ -4,8 +4,9 @@ |--------------------------------|------------------------------------------------|-----------| | Akshara Pulse Tracker Detector | Detect onsets of aksharas in tabla recordings | [1] | | Mnemonic Stroke Transcription | Bol/Solkattu trasncription using HMM | [2] | +| TCN Carnatic | Carnatic meter tracking using TCN | [3] | [1] Originally implemented by Ajay Srinivasamurthy as part of PyCompMusic - https://github.com/MTG/pycompmusic -[2] Gupta, S., Srinivasamurthy, A., Kumar, M., Murthy, H., & Serra, X. (2015, October). Discovery of Syllabic Percussion Patterns in Tabla Solo Recordings. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015) (pp. 385–391). Malaga, Spain. \ No newline at end of file +[2] Gupta, S., Srinivasamurthy, A., Kumar, M., Murthy, H., & Serra, X. (2015, October). Discovery of Syllabic Percussion Patterns in Tabla Solo Recordings. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015) (pp. 385–391). Malaga, Spain. diff --git a/madmom_patch.py b/madmom_patch.py deleted file mode 100644 index b3c4dd5e..00000000 --- a/madmom_patch.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -""" -madmom_patch.py ----------------- -Fixes Python 3.10+ compatibility issue in madmom by replacing: - from collections import MutableSequence -with: - from collections.abc import MutableSequence - -Run this script **once after installing dependencies**: - python madmom_patch.py -""" - -import sys -import os -import importlib.util - -print("🔍 Locating madmom installation...") - -# Find the madmom module path without importing it -spec = importlib.util.find_spec("madmom") -if spec is None or not spec.origin: - print("⚠ī¸ madmom is not installed. Please run:") - print(" pip install -r requirements.txt") - sys.exit(1) - -madmom_dir = os.path.dirname(spec.origin) -processors_file = os.path.join(madmom_dir, "processors.py") - -if not os.path.isfile(processors_file): - print(f"❌ processors.py not found at: {processors_file}") - sys.exit(1) - -try: - with open(processors_file, "r", encoding="utf-8") as f: - content = f.read() - - if "from collections import MutableSequence" not in content: - print("ℹī¸ Patch not needed. madmom is already compatible.") - sys.exit(0) - - content = content.replace( - "from collections import MutableSequence", - "from collections.abc import MutableSequence" - ) - - with open(processors_file, "w", encoding="utf-8") as f: - f.write(content) - - print(f"✅ Successfully patched madmom at:\n {processors_file}") - -except Exception as e: - print(f"❌ Failed to patch madmom: {e}") - sys.exit(1) From 40dabb33f020c519ce79d2d88b5f1bf357a3d488 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Wed, 11 Feb 2026 16:39:25 +0100 Subject: [PATCH 12/17] fix errors (downgrade numpy, madmom fix) --- .github/environment-ci.yml | 41 ++-- compiam/rhythm/meter/tcn_carnatic/__init__.py | 32 +-- main.py | 217 ------------------ pyproject.toml | 2 +- tests/conftest.py | 1 + tests/rhythm/test_tcn_carnatic.py | 4 +- 6 files changed, 42 insertions(+), 255 deletions(-) delete mode 100644 main.py diff --git a/.github/environment-ci.yml b/.github/environment-ci.yml index d3c24541..dceaf17a 100644 --- a/.github/environment-ci.yml +++ b/.github/environment-ci.yml @@ -4,25 +4,6 @@ channels: - defaults dependencies: - pip - - "attrs>=23.1.0" - - "matplotlib>=3.0.0" - - "numpy>=1.20.3,<=1.26.4" - - "joblib>=1.2.0" - - "pathlib~=1.0.1" - - "tqdm>=4.66.1" - - "IPython>=7.34.0" - - "ipywidgets>=7.0.0,<8" - - "Jinja2~=3.1.2" - - "configobj~=5.0.6" - - "seaborn" - - "librosa>=0.10.1" - - "scikit-learn==1.5.2" - - "scikit-image~=0.24.0" - - "hmmlearn==0.3.3" - - "fastdtw~=0.3.4" - ####### - - libvorbis - - pytest>=7.4.3 ####### - pip: - "keras<3.0.0" @@ -38,4 +19,24 @@ dependencies: - "black>=23.3.0" - "decorator>=5.1.1" - "future>=0.18.3" - - "testcontainers>=3.7.1" \ No newline at end of file + - "testcontainers>=3.7.1" + - "madmom @ git+https://github.com/vivekvjyn/madmom.git" + - "attrs>=23.1.0" + - "matplotlib>=3.0.0" + - "numpy>=1.20.3,<=1.26.4" + - "joblib>=1.2.0" + - "pathlib~=1.0.1" + - "tqdm>=4.66.1" + - "IPython>=7.34.0" + - "ipywidgets>=7.0.0,<8" + - "Jinja2~=3.1.2" + - "configobj~=5.0.6" + - "seaborn" + - "librosa>=0.10.1" + - "scikit-learn==1.5.2" + - "scikit-image~=0.24.0" + - "hmmlearn==0.3.3" + - "fastdtw~=0.3.4" + ####### + - libvorbis + - pytest>=7.4.3 diff --git a/compiam/rhythm/meter/tcn_carnatic/__init__.py b/compiam/rhythm/meter/tcn_carnatic/__init__.py index 7718f3e7..6903bfb8 100644 --- a/compiam/rhythm/meter/tcn_carnatic/__init__.py +++ b/compiam/rhythm/meter/tcn_carnatic/__init__.py @@ -1,15 +1,10 @@ import os import sys -import torch import numpy as np -import madmom from typing import Dict from tqdm import tqdm from compiam.exceptions import ModelNotTrainedError -from compiam.rhythm.meter.tcn_carnatic.model import MultiTracker -from compiam.rhythm.meter.tcn_carnatic.pre import PreProcessor -from compiam.rhythm.meter.tcn_carnatic.post import beat_tracker, joint_tracker, sequential_tracker from compiam.utils.download import download_remote_model from compiam.utils import get_logger, WORKDIR from compiam.io import write_csv @@ -31,18 +26,26 @@ def __init__(self, post_processor="joint", model_version=42, model_path=None, do try: global torch import torch + except ImportError: + raise ImportError( + "Torch is required to use TCNTracker. " + "Install compIAM with torch support: pip install 'compiam[torch]'" + ) + + try: global madmom import madmom - - global MultiTracker - from compiam.rhythm.meter.tcn_carnatic.model import MultiTracker - - except: + except ImportError: raise ImportError( - "In order to use this tool you need to have torch and madmom installed. " - "Install compIAM with torch and madmom support: pip install 'compiam[torch,madmom]'" + "Madmom is required to use TCNTracker. " + "Install compIAM with madmom support: pip install 'compiam[madmom]'" ) - ### +### + global MultiTracker, PreProcessor, joint_tracker, sequential_tracker + from compiam.rhythm.meter.tcn_carnatic.model import MultiTracker + from compiam.rhythm.meter.tcn_carnatic.pre import PreProcessor + from compiam.rhythm.meter.tcn_carnatic.post import joint_tracker, sequential_tracker + if post_processor not in ["beat", "joint", "sequential"]: raise ValueError(f"Invalid post_processor: {post_processor}. Choose from 'joint', or 'sequential'.") if model_version not in [42, 52, 62]: @@ -104,8 +107,6 @@ def download_model(self, model_path=None, force_overwrite=True): force_overwrite=force_overwrite, ) - - @torch.no_grad() def predict(self, input_data: str) -> Dict: """Run inference on input audio file. @@ -113,7 +114,6 @@ def predict(self, input_data: str) -> Dict: :returns: a 2-D list with beats and beat positions. """ - if self.trained is False: raise ModelNotTrainedError( """Model is not trained. Please load model before running inference! diff --git a/main.py b/main.py deleted file mode 100644 index f7771bde..00000000 --- a/main.py +++ /dev/null @@ -1,217 +0,0 @@ -import compiam -import os - - - -import numpy as np -import matplotlib.pyplot as plt -import librosa -import IPython.display as ipd - -#@title Plotting function -def plot_sonify(y, sr=22050, beats=None, labels=None, start_time=0, duration=None): - """ - Plots the waveform with optional beats and downbeats. If beats are None, only the waveform is plotted. - - Parameters: - - y: Audio signal waveform data. - - sr: Sampling rate of the audio signal. - - beats: Array of beat times (in seconds). If None, only the waveform is plotted. - - labels: Optional array of labels corresponding to beats. Downbeats should be labeled as 1. - - start_time: Start time in seconds for the plot window (default: 0). - - duration: Duration in seconds for the plot window. If None, plots entire signal. - """ - time = (np.arange(len(y))/sr) - - plt.figure(figsize=(100, 5)) - - # Apply time windowing if specified - plot_y = y - plot_time = time - - if duration is not None: - end_time = start_time + duration - # Find indices for the time window - start_idx = np.searchsorted(time, start_time) - end_idx = np.searchsorted(time, end_time) - plot_time = time[start_idx:end_idx] - plot_y = y[start_idx:end_idx] - - # Plot waveform - plt.plot(plot_time, plot_y) - plt.ylabel('Amplitude') - plt.xlabel('Time (sec)') - plt.title("Waveform") - - # Set x-axis limits if duration is specified - if duration is not None: - plt.xlim(start_time, start_time + duration) - - # Check if beats are provided - if beats is not None: - # Filter beats to the time window if duration is specified - if duration is not None: - end_time = start_time + duration - mask = (beats >= start_time) & (beats <= end_time) - windowed_beats = beats[mask] - windowed_labels = labels[mask] if labels is not None else None - else: - windowed_beats = beats - windowed_labels = labels - - # Separate beats into downbeats and other beats - if windowed_labels is not None: - windowed_labels = np.array(windowed_labels) # Ensure labels is a NumPy array - downbeat_times = windowed_beats[windowed_labels == 1] # Downbeats (label == 1) - beat_times = windowed_beats[windowed_labels != 1] # Other beats (label != 1) - plt.title("Waveform with Beats and Downbeats") - else: - downbeat_times = np.array([]) # Empty array for downbeats - beat_times = windowed_beats # All beats - plt.title("Waveform with Beats") - - # Plot vertical lines for downbeats and beats - ylim = np.max(np.abs(plot_y)) - plt.vlines(downbeat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='DownBeats', color='black', linewidths=1, linestyle='--') - plt.vlines(beat_times, ymin=-ylim-0.1, ymax=ylim+0.1, label='Beats', color='red', linewidths=1, linestyle=':') - - plt.legend(frameon=True, framealpha=1.0, edgecolor='black', loc='lower left', bbox_to_anchor=(0, 0.05), fontsize='small') - - # Generate click sounds for downbeats and beats - # Use the windowed audio for click generation - audio_length = len(plot_y) - - # Adjust beat times relative to the start of the windowed audio - if duration is not None: - adjusted_downbeat_times = downbeat_times - start_time - adjusted_beat_times = beat_times - start_time - - # Filter out negative times and times beyond the audio length - time_limit = len(plot_y) / sr - adjusted_downbeat_times = adjusted_downbeat_times[(adjusted_downbeat_times >= 0) & (adjusted_downbeat_times <= time_limit)] - adjusted_beat_times = adjusted_beat_times[(adjusted_beat_times >= 0) & (adjusted_beat_times <= time_limit)] - else: - adjusted_downbeat_times = downbeat_times - adjusted_beat_times = beat_times - - downbeat_click = librosa.clicks(times=adjusted_downbeat_times, sr=sr, click_freq=1000, length=audio_length, click_duration=0.1) - beat_click = librosa.clicks(times=adjusted_beat_times, sr=sr, click_freq=500, length=audio_length, click_duration=0.1) - - # Combine original audio with clicks - combined_audio = plot_y + beat_click + downbeat_click - - # Normalize combined audio to prevent clipping - combined_audio = combined_audio / np.max(np.abs(combined_audio)) - - else: - combined_audio = plot_y - - # Play the audio with clicks - audio_widget = ipd.Audio(combined_audio, rate=sr) - - plt.savefig('plot.png') - plt.close() - - return audio_widget - - -def plot_spec(y, sr, gt_beats=None, gt_labels=None, pred_beats=None, pred_labels=None, start_time=0, duration=30): - """ - Plots spectrogram with optional ground truth and predicted beats/downbeats. - - Parameters: - - y: Audio signal - - sr: Sampling rate - - gt_beats: Ground truth beat times (optional) - - gt_labels: Ground truth beat labels (optional, 1 for downbeats, other values for beats) - - pred_beats: Predicted beat times (optional) - - pred_labels: Predicted beat labels (optional, 1 for downbeats, other values for beats) - - start_time: Start time in seconds for the plot window (default: 0) - - duration: Duration in seconds for the plot window (default: 20) - """ - hop_length = 512 - - # Calculate end time - end_time = start_time + duration - - # Extract audio segment - start_sample = int(start_time * sr) - end_sample = int(end_time * sr) - audio_segment = y[start_sample:end_sample] - - # Generate spectrogram - spec = librosa.amplitude_to_db(np.abs(librosa.stft(audio_segment, hop_length=hop_length)), ref=np.max) - librosa.display.specshow(spec, y_axis='log', sr=sr, hop_length=hop_length, x_axis='time') - plt.title(f"Log-frequency power spectrogram of track ({start_time}s - {end_time}s)") - plt.colorbar(format="%+2.f dB") - - # Process ground truth beats if provided - if gt_beats is not None: - # Filter beat times and labels to the specified window - gt_mask = (gt_beats >= start_time) & (gt_beats <= end_time) - gt_beats_windowed = gt_beats[gt_mask] - gt_labels_windowed = gt_labels[gt_mask] if gt_labels is not None else None - - # Adjust times relative to the start of the window - gt_beats_adjusted = gt_beats_windowed - start_time - - # Separate beats and downbeats for ground truth - if gt_labels_windowed is not None: - gt_labels_windowed = np.array(gt_labels_windowed) - gt_downbeats = gt_beats_adjusted[gt_labels_windowed == 1] - gt_beats_only = gt_beats_adjusted[gt_labels_windowed != 1] - else: - gt_downbeats = np.array([]) - gt_beats_only = gt_beats_adjusted - - # Plot ground truth annotations in the upper part - if len(gt_beats_only) > 0: - plt.vlines(gt_beats_only, hop_length * 2, sr / 2, linestyles='dotted', color='w', alpha=0.8) - if len(gt_downbeats) > 0: - plt.vlines(gt_downbeats, hop_length * 2, sr / 2, color='w', alpha=0.8) - plt.text(duration * 0.35, hop_length * 1.65, 'GT Annotations (above)', color='w', fontsize=10, fontweight='bold') - - # Process predicted beats if provided - if pred_beats is not None: - # Filter beat times and labels to the specified window - pred_mask = (pred_beats >= start_time) & (pred_beats <= end_time) - pred_beats_windowed = pred_beats[pred_mask] - pred_labels_windowed = pred_labels[pred_mask] if pred_labels is not None else None - - # Adjust times relative to the start of the window - pred_beats_adjusted = pred_beats_windowed - start_time - - # Separate beats and downbeats for predictions - if pred_labels_windowed is not None: - pred_labels_windowed = np.array(pred_labels_windowed) - pred_downbeats = pred_beats_adjusted[pred_labels_windowed == 1] - pred_beats_only = pred_beats_adjusted[pred_labels_windowed != 1] - else: - pred_downbeats = np.array([]) - pred_beats_only = pred_beats_adjusted - - # Plot predictions in the lower part - if len(pred_beats_only) > 0: - plt.vlines(pred_beats_only, 0, hop_length, linestyles='dotted', color='w', alpha=0.8) - if len(pred_downbeats) > 0: - plt.vlines(pred_downbeats, 0, hop_length, color='w', alpha=0.8) - plt.text(duration * 0.35, hop_length * 1.1, 'Predictions (below)', color='w', fontsize=10, fontweight='bold') - - plt.show() - -audio_path = "../Devi Pavane.wav" - -print(compiam.list_models()) -from compiam.rhythm.meter.tcn_carnatic import TCNTracker -tracker = TCNTracker(post_processor="sequential") -tracker.trained = True -pred = tracker.predict(audio_path) - -beats = pred[:, 0] - -print(pred) -print(pred.shape) - -y, sr = librosa.load(audio_path) - -plot_sonify(y=y, sr=sr, beats=beats) diff --git a/pyproject.toml b/pyproject.toml index 08b3d37e..62c03ac0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ classifiers = [ dependencies = [ "attrs>=23.1.0", "matplotlib>=3.0.0", - "numpy>=1.20.3,<=1.26.4", + "numpy>=1.20.3,<=1.23.5", "joblib>=1.2.0", "pathlib~=1.0.1", "tqdm>=4.66.1", diff --git a/tests/conftest.py b/tests/conftest.py index 9fb61c7f..829bf7d5 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,6 +6,7 @@ "essentia", "full_ml", "all", + "madmom @ git+https://github.com/vivekvjyn/madmom.git" ] diff --git a/tests/rhythm/test_tcn_carnatic.py b/tests/rhythm/test_tcn_carnatic.py index 0bfc0a55..5fdfd475 100644 --- a/tests/rhythm/test_tcn_carnatic.py +++ b/tests/rhythm/test_tcn_carnatic.py @@ -1,7 +1,7 @@ import os import pytest import librosa - +import torch import numpy as np from compiam.data import TESTDIR @@ -17,6 +17,8 @@ def test_predict_joint(): tracker.trained = True with pytest.raises(FileNotFoundError): tracker.predict(os.path.join(TESTDIR, "resources", "rhythm", "hola.wav")) + + print(tracker.predict(os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav"))) beats = tracker.predict( os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") ) From 7fe021aa9ce058e57c55286386b9a80b07e5c62d Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 17 Feb 2026 12:45:19 +0100 Subject: [PATCH 13/17] conda dependency fix --- .github/environment-ci.yml | 70 +++++++++++++++++++------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/.github/environment-ci.yml b/.github/environment-ci.yml index dceaf17a..a4b122b7 100644 --- a/.github/environment-ci.yml +++ b/.github/environment-ci.yml @@ -1,42 +1,42 @@ name: compiam-dev + channels: - conda-forge - defaults + dependencies: + - python=3.9 - pip - ####### + - libvorbis + - pip: - - "keras<3.0.0" - - "tensorflow>=2.12.0,<2.16" - - "torch==2.0.0" - - "torchaudio==2.0.1" - - "essentia" - - "soundfile>=0.12.1" - - "opencv-python~=4.6.0" - - "mirdata==0.3.9" - - "compmusic==0.4" - - "attrs>=23.1.0" - - "black>=23.3.0" - - "decorator>=5.1.1" - - "future>=0.18.3" - - "testcontainers>=3.7.1" - - "madmom @ git+https://github.com/vivekvjyn/madmom.git" - - "attrs>=23.1.0" - - "matplotlib>=3.0.0" - - "numpy>=1.20.3,<=1.26.4" - - "joblib>=1.2.0" - - "pathlib~=1.0.1" - - "tqdm>=4.66.1" - - "IPython>=7.34.0" - - "ipywidgets>=7.0.0,<8" - - "Jinja2~=3.1.2" - - "configobj~=5.0.6" - - "seaborn" - - "librosa>=0.10.1" - - "scikit-learn==1.5.2" - - "scikit-image~=0.24.0" - - "hmmlearn==0.3.3" - - "fastdtw~=0.3.4" - ####### - - libvorbis - - pytest>=7.4.3 + - keras<3.0.0 + - tensorflow>=2.12.0,<2.16 + - torch==2.0.0 + - torchaudio==2.0.1 + - essentia + - soundfile>=0.12.1 + - opencv-python~=4.6.0 + - mirdata==0.3.9 + - compmusic==0.4 + - attrs>=23.1.0 + - black>=23.3.0 + - decorator>=5.1.1 + - future>=0.18.3 + - testcontainers>=3.7.1 + - madmom @ git+https://github.com/vivekvjyn/madmom.git + - matplotlib>=3.0.0 + - numpy>=1.20.3,<=1.26.4 + - joblib>=1.2.0 + - tqdm>=4.66.1 + - IPython>=7.34.0 + - ipywidgets>=7.0.0,<8 + - Jinja2~=3.1.2 + - configobj~=5.0.6 + - seaborn + - librosa>=0.10.1 + - scikit-learn==1.5.2 + - scikit-image~=0.24.0 + - hmmlearn==0.3.3 + - fastdtw~=0.3.4 + - pytest>=7.4.3 From fa2ba029a6f3ad61447843b38e0f626030aa02c6 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 17 Feb 2026 13:09:56 +0100 Subject: [PATCH 14/17] numpy lower version for git workflow --- .github/environment-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/environment-ci.yml b/.github/environment-ci.yml index a4b122b7..4e84827d 100644 --- a/.github/environment-ci.yml +++ b/.github/environment-ci.yml @@ -26,7 +26,7 @@ dependencies: - testcontainers>=3.7.1 - madmom @ git+https://github.com/vivekvjyn/madmom.git - matplotlib>=3.0.0 - - numpy>=1.20.3,<=1.26.4 + - numpy>=1.20.3,<=1.23.5 - joblib>=1.2.0 - tqdm>=4.66.1 - IPython>=7.34.0 From a4ed39a11f24da6ed3f3dfec453fa62743d8ec6b Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Tue, 17 Feb 2026 15:44:37 +0100 Subject: [PATCH 15/17] fixed hardcoded parameters issue --- compiam/rhythm/meter/tcn_carnatic/__init__.py | 22 ++++++++++------ compiam/rhythm/meter/tcn_carnatic/post.py | 12 +++------ compiam/rhythm/meter/tcn_carnatic/pre.py | 2 +- tests/resources/rhythm/48k.wav | Bin 0 -> 710952 bytes tests/rhythm/test_tcn_carnatic.py | 24 +++++++++++++----- 5 files changed, 37 insertions(+), 23 deletions(-) create mode 100644 tests/resources/rhythm/48k.wav diff --git a/compiam/rhythm/meter/tcn_carnatic/__init__.py b/compiam/rhythm/meter/tcn_carnatic/__init__.py index 6903bfb8..18efca9f 100644 --- a/compiam/rhythm/meter/tcn_carnatic/__init__.py +++ b/compiam/rhythm/meter/tcn_carnatic/__init__.py @@ -13,7 +13,13 @@ class TCNTracker(object): """TCN beat tracker tuned to Carnatic Music.""" - def __init__(self, post_processor="joint", model_version=42, model_path=None, download_link=None, download_checksum=None, gpu=-1): + def __init__(self, + post_processor="joint", + model_version=42, + model_path=None, + download_link=None, + download_checksum=None, + gpu=-1): """TCN beat tracker init method. :param post_processor: Post-processing method to use. Choose from 'joint', or 'sequential'. @@ -64,7 +70,6 @@ def __init__(self, post_processor="joint", model_version=42, model_path=None, do self.model = self._build_model() if self.model_path is not None: self.load_model(self.model_path) - self.pre_processor = PreProcessor(fps=100) self.pad_frames = 2 self.post_processor = joint_tracker if post_processor == "joint" else \ @@ -107,7 +112,7 @@ def download_model(self, model_path=None, force_overwrite=True): force_overwrite=force_overwrite, ) - def predict(self, input_data: str) -> Dict: + def predict(self, input_data: str, sr: int = 44100) -> Dict: """Run inference on input audio file. :param input_data: path to audio file or numpy array like audio signal. @@ -120,7 +125,7 @@ def predict(self, input_data: str) -> Dict: You can load the pre-trained instance with the load_model wrapper.""" ) - features = self.preprocess_audio(input_data) + features = self.preprocess_audio(input_data, sr) x = torch.from_numpy(features).to(self.device) output = self.model(x) beats_act = output["beats"].squeeze().detach().cpu().numpy() @@ -130,7 +135,7 @@ def predict(self, input_data: str) -> Dict: return pred - def preprocess_audio(self, input_data: str, input_sr: int = 44100) -> np.ndarray: + def preprocess_audio(self, input_data: str, input_sr: int) -> np.ndarray: """Preprocess input audio file to extract features for inference. :param audio_path: Path to the input audio file. :param input_sr: Sampling rate of the input audio file. @@ -143,16 +148,17 @@ def preprocess_audio(self, input_data: str, input_sr: int = 44100) -> np.ndarray audio, sr = madmom.io.audio.load_audio_file(input_data) if audio.shape[0] == 2: audio = audio.mean(axis=0) - s = madmom.audio.Signal(audio, sr, num_channels=1) + signal = madmom.audio.Signal(audio, sr, num_channels=1) elif isinstance(input_data, np.ndarray): audio = input_data if audio.shape[0] == 2: audio = audio.mean(axis=0) - s = madmom.audio.Signal(audio, input_sr, num_channels=1) + signal = madmom.audio.Signal(audio, input_sr, num_channels=1) + sr = input_sr else: raise ValueError("Input must be path to audio signal or an audio array") - x = self.pre_processor(s) + x = PreProcessor(sample_rate=sr)(signal) pad_start = np.repeat(x[:1], self.pad_frames, axis=0) pad_stop = np.repeat(x[-1:], self.pad_frames, axis=0) diff --git a/compiam/rhythm/meter/tcn_carnatic/post.py b/compiam/rhythm/meter/tcn_carnatic/post.py index e31c7c35..2bb9c127 100644 --- a/compiam/rhythm/meter/tcn_carnatic/post.py +++ b/compiam/rhythm/meter/tcn_carnatic/post.py @@ -4,14 +4,9 @@ from scipy.ndimage import maximum_filter1d import numpy as np -fps= 100 # frames per second for the DBN processors -min_bpm= 55.0 -max_bpm= 230.0 - -epsilon = 1e-5 - def clip_probabilities(probs): """Clip probabilities to avoid exact 0 and 1 values that cause DBN issues.""" + epsilon = 1e-5 probs = np.maximum(probs, 0) probs = np.minimum(probs, 1) return probs * (1 - epsilon) + epsilon / 2 @@ -19,7 +14,7 @@ def clip_probabilities(probs): def beat_tracker(beats_act): beats_act = clip_probabilities(beats_act) beat_dbn = DBNBeatTrackingProcessor( - min_bpm=min_bpm, max_bpm=max_bpm, fps=fps, transition_lambda=100, online=False) + min_bpm=55.0, max_bpm=230.0, fps=100, transition_lambda=100, online=False) if beats_act.size > 1: beats_pred = beat_dbn(beats_act) @@ -33,7 +28,7 @@ def joint_tracker(beats_act, downbeats_act): downbeats_act = clip_probabilities(downbeats_act) downbeat_tracker = DBNDownBeatTrackingProcessor( - beats_per_bar=[3, 5, 7, 8], min_bpm=min_bpm, max_bpm=max_bpm, fps=fps) + beats_per_bar=[3, 5, 7, 8], min_bpm=55, max_bpm=230.0, fps=100) combined_act = np.vstack((np.maximum(beats_act - downbeats_act, 0), downbeats_act)).T pred = downbeat_tracker(combined_act) @@ -41,6 +36,7 @@ def joint_tracker(beats_act, downbeats_act): return pred def sequential_tracker(beats_act, downbeats_act): + fps = 100 beats_act = clip_probabilities(beats_act) downbeats_act = clip_probabilities(downbeats_act) diff --git a/compiam/rhythm/meter/tcn_carnatic/pre.py b/compiam/rhythm/meter/tcn_carnatic/pre.py index 8ba2826d..06e712f2 100644 --- a/compiam/rhythm/meter/tcn_carnatic/pre.py +++ b/compiam/rhythm/meter/tcn_carnatic/pre.py @@ -7,7 +7,7 @@ class PreProcessor(SequentialProcessor): - def __init__(self, frame_size=2048, num_bands=12, log=np.log, add=1e-6, fps=100): + def __init__(self, sample_rate, frame_size=2048, num_bands=12, log=np.log, add=1e-6, fps=100): sig = SignalProcessor(num_channels=1, sample_rate=44100) frames = FramedSignalProcessor(frame_size=frame_size, fps=fps) stft = ShortTimeFourierTransformProcessor() diff --git a/tests/resources/rhythm/48k.wav b/tests/resources/rhythm/48k.wav new file mode 100644 index 0000000000000000000000000000000000000000..576942ee9463ef010d10740c600c1d1bffc7f97a GIT binary patch literal 710952 zcmeIyJxT*X6ae67G$4tf2S_oMwJa%ZEF4%35#4AY-9iuqg8|pZ%9Gf72wM+fz{9vA 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tracker.predict(audio_in) + beats_2 = tracker.predict(audio_in, sr) assert isinstance(beats, np.ndarray) assert isinstance(beats_2, np.ndarray) + assert beats.shape[1] == 2 + assert beats_2.shape[1] == 2 def test_predict_sequential(): from compiam.rhythm.meter.tcn_carnatic import TCNTracker @@ -46,8 +45,6 @@ def test_predict_sequential(): os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") ) - print(beats.shape) - audio_in, sr = librosa.load( os.path.join(TESTDIR, "resources", "rhythm", "beat_test.wav") ) @@ -55,3 +52,18 @@ def test_predict_sequential(): assert isinstance(beats, np.ndarray) assert isinstance(beats_2, np.ndarray) + assert beats.shape[1] == 2 + assert beats_2.shape[1] == 2 + +def test_48k(): + from compiam.rhythm.meter.tcn_carnatic import TCNTracker + + tracker = TCNTracker(post_processor="sequential") + tracker.trained = True + audio_in, sr = librosa.load( + os.path.join(TESTDIR, "resources", "rhythm", "48k.wav"), sr=48000 + ) + beats = tracker.predict(audio_in) + + assert isinstance(beats, np.ndarray) + assert beats.shape[1] == 2 From 2f69d3ab9dc36166c1e3b092b7c990c4f25ea353 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Wed, 18 Feb 2026 13:16:00 +0100 Subject: [PATCH 16/17] min and max bpm now can be passed as an argument --- compiam/rhythm/meter/tcn_carnatic/__init__.py | 7 +++++-- compiam/rhythm/meter/tcn_carnatic/post.py | 12 ++++++------ compiam/rhythm/meter/tcn_carnatic/pre.py | 2 +- 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/compiam/rhythm/meter/tcn_carnatic/__init__.py b/compiam/rhythm/meter/tcn_carnatic/__init__.py index 18efca9f..663ecc1e 100644 --- a/compiam/rhythm/meter/tcn_carnatic/__init__.py +++ b/compiam/rhythm/meter/tcn_carnatic/__init__.py @@ -112,10 +112,13 @@ def download_model(self, model_path=None, force_overwrite=True): force_overwrite=force_overwrite, ) - def predict(self, input_data: str, sr: int = 44100) -> Dict: + def predict(self, input_data: str, sr: int = 44100, min_bpm=55, max_bpm=230) -> Dict: """Run inference on input audio file. :param input_data: path to audio file or numpy array like audio signal. + :param sr: sampling rate of the input audio signal (default: 44100). + :param min_bpm: minimum BPM for beat tracking (default: 55). + :param max_bpm: maximum BPM for beat tracking (default: 230). :returns: a 2-D list with beats and beat positions. """ @@ -131,7 +134,7 @@ def predict(self, input_data: str, sr: int = 44100) -> Dict: beats_act = output["beats"].squeeze().detach().cpu().numpy() downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() - pred = self.post_processor(beats_act, downbeats_act) + pred = self.post_processor(beats_act, downbeats_act, min_bpm=min_bpm, max_bpm=max_bpm) return pred diff --git a/compiam/rhythm/meter/tcn_carnatic/post.py b/compiam/rhythm/meter/tcn_carnatic/post.py index 2bb9c127..2b6a1fe2 100644 --- a/compiam/rhythm/meter/tcn_carnatic/post.py +++ b/compiam/rhythm/meter/tcn_carnatic/post.py @@ -11,10 +11,10 @@ def clip_probabilities(probs): probs = np.minimum(probs, 1) return probs * (1 - epsilon) + epsilon / 2 -def beat_tracker(beats_act): +def beat_tracker(beats_act, min_bpm=55, max_bpm=230): beats_act = clip_probabilities(beats_act) beat_dbn = DBNBeatTrackingProcessor( - min_bpm=55.0, max_bpm=230.0, fps=100, transition_lambda=100, online=False) + min_bpm=min_bpm, max_bpm=max_bpm, fps=100, transition_lambda=100, online=False) if beats_act.size > 1: beats_pred = beat_dbn(beats_act) @@ -23,19 +23,19 @@ def beat_tracker(beats_act): # If no beats are detected, return an empty array return np.array([]) -def joint_tracker(beats_act, downbeats_act): +def joint_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230): beats_act = clip_probabilities(beats_act) downbeats_act = clip_probabilities(downbeats_act) downbeat_tracker = DBNDownBeatTrackingProcessor( - beats_per_bar=[3, 5, 7, 8], min_bpm=55, max_bpm=230.0, fps=100) + beats_per_bar=[3, 5, 7, 8], min_bpm=min_bpm, max_bpm=max_bpm, fps=100) combined_act = np.vstack((np.maximum(beats_act - downbeats_act, 0), downbeats_act)).T pred = downbeat_tracker(combined_act) return pred -def sequential_tracker(beats_act, downbeats_act): +def sequential_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230): fps = 100 beats_act = clip_probabilities(beats_act) downbeats_act = clip_probabilities(downbeats_act) @@ -51,7 +51,7 @@ def sequential_tracker(beats_act, downbeats_act): bar_tracker = DBNBarTrackingProcessor(beats_per_bar=(3, 5, 7, 8), meter_change_prob=1e-3, observation_weight=4) try: - pred = bar_tracker(bar_act) + pred = bar_tracker(bar_act, min_bpm=min_bpm, max_bpm=max_bpm) except IndexError: pred = np.empty((0, 2)) diff --git a/compiam/rhythm/meter/tcn_carnatic/pre.py b/compiam/rhythm/meter/tcn_carnatic/pre.py index 06e712f2..4aa24391 100644 --- a/compiam/rhythm/meter/tcn_carnatic/pre.py +++ b/compiam/rhythm/meter/tcn_carnatic/pre.py @@ -8,7 +8,7 @@ class PreProcessor(SequentialProcessor): def __init__(self, sample_rate, frame_size=2048, num_bands=12, log=np.log, add=1e-6, fps=100): - sig = SignalProcessor(num_channels=1, sample_rate=44100) + sig = SignalProcessor(num_channels=1, sample_rate=sample_rate) frames = FramedSignalProcessor(frame_size=frame_size, fps=fps) stft = ShortTimeFourierTransformProcessor() filt = FilteredSpectrogramProcessor(num_bands=num_bands) From d837be0f08652dc0adbdb3eb7f2fbc4963b99680 Mon Sep 17 00:00:00 2001 From: vivekvjyn Date: Wed, 18 Feb 2026 15:54:34 +0100 Subject: [PATCH 17/17] pass beats per bars as argument --- compiam/rhythm/meter/tcn_carnatic/__init__.py | 5 +++-- compiam/rhythm/meter/tcn_carnatic/post.py | 16 +++++++--------- compiam/rhythm/meter/tcn_carnatic/pre.py | 4 ++-- 3 files changed, 12 insertions(+), 13 deletions(-) diff --git a/compiam/rhythm/meter/tcn_carnatic/__init__.py b/compiam/rhythm/meter/tcn_carnatic/__init__.py index 663ecc1e..d8252224 100644 --- a/compiam/rhythm/meter/tcn_carnatic/__init__.py +++ b/compiam/rhythm/meter/tcn_carnatic/__init__.py @@ -112,13 +112,14 @@ def download_model(self, model_path=None, force_overwrite=True): force_overwrite=force_overwrite, ) - def predict(self, input_data: str, sr: int = 44100, min_bpm=55, max_bpm=230) -> Dict: + def predict(self, input_data: str, sr: int = 44100, min_bpm=55, max_bpm=230, beats_per_bar=[3, 5, 7, 8]) -> Dict: """Run inference on input audio file. :param input_data: path to audio file or numpy array like audio signal. :param sr: sampling rate of the input audio signal (default: 44100). :param min_bpm: minimum BPM for beat tracking (default: 55). :param max_bpm: maximum BPM for beat tracking (default: 230). + :param beats_per_bar: list of possible beats per bar for downbeat tracking (default: [3, 5, 7, 8]). :returns: a 2-D list with beats and beat positions. """ @@ -134,7 +135,7 @@ def predict(self, input_data: str, sr: int = 44100, min_bpm=55, max_bpm=230) -> beats_act = output["beats"].squeeze().detach().cpu().numpy() downbeats_act = output["downbeats"].squeeze().detach().cpu().numpy() - pred = self.post_processor(beats_act, downbeats_act, min_bpm=min_bpm, max_bpm=max_bpm) + pred = self.post_processor(beats_act, downbeats_act, min_bpm=min_bpm, max_bpm=max_bpm, beats_per_bar=beats_per_bar) return pred diff --git a/compiam/rhythm/meter/tcn_carnatic/post.py b/compiam/rhythm/meter/tcn_carnatic/post.py index 2b6a1fe2..5b7fb090 100644 --- a/compiam/rhythm/meter/tcn_carnatic/post.py +++ b/compiam/rhythm/meter/tcn_carnatic/post.py @@ -4,17 +4,16 @@ from scipy.ndimage import maximum_filter1d import numpy as np -def clip_probabilities(probs): +def clip_probabilities(probs, epsilon=1e-5): """Clip probabilities to avoid exact 0 and 1 values that cause DBN issues.""" - epsilon = 1e-5 probs = np.maximum(probs, 0) probs = np.minimum(probs, 1) return probs * (1 - epsilon) + epsilon / 2 -def beat_tracker(beats_act, min_bpm=55, max_bpm=230): +def beat_tracker(beats_act, min_bpm=55, max_bpm=230, fps=100, transition_lambda=100): beats_act = clip_probabilities(beats_act) beat_dbn = DBNBeatTrackingProcessor( - min_bpm=min_bpm, max_bpm=max_bpm, fps=100, transition_lambda=100, online=False) + min_bpm=min_bpm, max_bpm=max_bpm, fps=fps, transition_lambda=transition_lambda, online=False) if beats_act.size > 1: beats_pred = beat_dbn(beats_act) @@ -23,20 +22,19 @@ def beat_tracker(beats_act, min_bpm=55, max_bpm=230): # If no beats are detected, return an empty array return np.array([]) -def joint_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230): +def joint_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230, fps=100, beats_per_bar=[3, 5, 7, 8]): beats_act = clip_probabilities(beats_act) downbeats_act = clip_probabilities(downbeats_act) downbeat_tracker = DBNDownBeatTrackingProcessor( - beats_per_bar=[3, 5, 7, 8], min_bpm=min_bpm, max_bpm=max_bpm, fps=100) + beats_per_bar=beats_per_bar, min_bpm=min_bpm, max_bpm=max_bpm, fps=fps) combined_act = np.vstack((np.maximum(beats_act - downbeats_act, 0), downbeats_act)).T pred = downbeat_tracker(combined_act) return pred -def sequential_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230): - fps = 100 +def sequential_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230, fps=100, beats_per_bar=[3, 5, 7, 8]): beats_act = clip_probabilities(beats_act) downbeats_act = clip_probabilities(downbeats_act) @@ -48,7 +46,7 @@ def sequential_tracker(beats_act, downbeats_act, min_bpm=55, max_bpm=230): bar_act = bar_act[beat_idx] bar_act = np.vstack((beats, bar_act)).T - bar_tracker = DBNBarTrackingProcessor(beats_per_bar=(3, 5, 7, 8), meter_change_prob=1e-3, observation_weight=4) + bar_tracker = DBNBarTrackingProcessor(beats_per_bar=beats_per_bar, meter_change_prob=1e-3, observation_weight=4) try: pred = bar_tracker(bar_act, min_bpm=min_bpm, max_bpm=max_bpm) diff --git a/compiam/rhythm/meter/tcn_carnatic/pre.py b/compiam/rhythm/meter/tcn_carnatic/pre.py index 4aa24391..c8b0f9b6 100644 --- a/compiam/rhythm/meter/tcn_carnatic/pre.py +++ b/compiam/rhythm/meter/tcn_carnatic/pre.py @@ -7,8 +7,8 @@ class PreProcessor(SequentialProcessor): - def __init__(self, sample_rate, frame_size=2048, num_bands=12, log=np.log, add=1e-6, fps=100): - sig = SignalProcessor(num_channels=1, sample_rate=sample_rate) + def __init__(self, sample_rate, frame_size=2048, num_bands=12, log=np.log, add=1e-6, fps=100, num_channels=1): + sig = SignalProcessor(num_channels=num_channels, sample_rate=sample_rate) frames = FramedSignalProcessor(frame_size=frame_size, fps=fps) stft = ShortTimeFourierTransformProcessor() filt = FilteredSpectrogramProcessor(num_bands=num_bands)