from encoder.params_data import * from encoder.model import SpeakerEncoder from encoder.audio import preprocess_wav # We want to expose this function from here from matplotlib import cm from encoder import audio from pathlib import Path import matplotlib.pyplot as plt import numpy as np import torch _model = None # type: SpeakerEncoder _device = None # type: torch.device def load_model(weights_fpath: Path, device=None): """ Loads the model in memory. If this function is not explicitely called, it will be run on the first call to embed_frames() with the default weights file. :param weights_fpath: the path to saved model weights. :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The model will be loaded and will run on this device. Outputs will however always be on the cpu. If None, will default to your GPU if it"s available, otherwise your CPU. """ # TODO: I think the slow loading of the encoder might have something to do with the device it # was saved on. Worth investigating. global _model, _device if device is None: _device = torch.device("cuda" if torch.cuda.is_available() else "cpu") elif isinstance(device, str): _device = torch.device(device) _model = SpeakerEncoder(_device, torch.device("cpu")) checkpoint = torch.load(weights_fpath, _device) _model.load_state_dict(checkpoint["model_state"]) _model.eval() print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"])) return _model def set_model(model, device=None): global _model, _device _model = model if device is None: _device = torch.device("cuda" if torch.cuda.is_available() else "cpu") _device = device _model.to(device) def is_loaded(): return _model is not None def embed_frames_batch(frames_batch): """ Computes embeddings for a batch of mel spectrogram. :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape (batch_size, n_frames, n_channels) :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size) """ if _model is None: raise Exception("Model was not loaded. Call load_model() before inference.") frames = torch.from_numpy(frames_batch).to(_device) embed = _model.forward(frames).detach().cpu().numpy() return embed def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames, min_pad_coverage=0.75, overlap=0.5, rate=None): """ Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain partial utterances of each. Both the waveform and the mel spectrogram slices are returned, so as to make each partial utterance waveform correspond to its spectrogram. This function assumes that the mel spectrogram parameters used are those defined in params_data.py. The returned ranges may be indexing further than the length of the waveform. It is recommended that you pad the waveform with zeros up to wave_slices[-1].stop. :param n_samples: the number of samples in the waveform :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial utterance :param min_pad_coverage: when reaching the last partial utterance, it may or may not have enough frames. If at least of are present, then the last partial utterance will be considered, as if we padded the audio. Otherwise, it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial utterance, this parameter is ignored so that the function always returns at least 1 slice. :param overlap: by how much the partial utterance should overlap. If set to 0, the partial utterances are entirely disjoint. :return: the waveform slices and mel spectrogram slices as lists of array slices. Index respectively the waveform and the mel spectrogram with these slices to obtain the partial utterances. """ assert 0 <= overlap < 1 assert 0 < min_pad_coverage <= 1 if rate != None: samples_per_frame = int((sampling_rate * mel_window_step / 1000)) n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) frame_step = int(np.round((sampling_rate / rate) / samples_per_frame)) else: samples_per_frame = int((sampling_rate * mel_window_step / 1000)) n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1) assert 0 < frame_step, "The rate is too high" assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % \ (sampling_rate / (samples_per_frame * partials_n_frames)) # Compute the slices wav_slices, mel_slices = [], [] steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1) for i in range(0, steps, frame_step): mel_range = np.array([i, i + partial_utterance_n_frames]) wav_range = mel_range * samples_per_frame mel_slices.append(slice(*mel_range)) wav_slices.append(slice(*wav_range)) # Evaluate whether extra padding is warranted or not last_wav_range = wav_slices[-1] coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start) if coverage < min_pad_coverage and len(mel_slices) > 1: mel_slices = mel_slices[:-1] wav_slices = wav_slices[:-1] return wav_slices, mel_slices def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs): """ Computes an embedding for a single utterance. # TODO: handle multiple wavs to benefit from batching on GPU :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32 :param using_partials: if True, then the utterance is split in partial utterances of frames and the utterance embedding is computed from their normalized average. If False, the utterance is instead computed from feeding the entire spectogram to the network. :param return_partials: if True, the partial embeddings will also be returned along with the wav slices that correspond to the partial embeddings. :param kwargs: additional arguments to compute_partial_splits() :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If is True, the partial utterances as a numpy array of float32 of shape (n_partials, model_embedding_size) and the wav partials as a list of slices will also be returned. If is simultaneously set to False, both these values will be None instead. """ # Process the entire utterance if not using partials if not using_partials: frames = audio.wav_to_mel_spectrogram(wav) embed = embed_frames_batch(frames[None, ...])[0] if return_partials: return embed, None, None return embed # Compute where to split the utterance into partials and pad if necessary wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs) max_wave_length = wave_slices[-1].stop if max_wave_length >= len(wav): wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant") # Split the utterance into partials frames = audio.wav_to_mel_spectrogram(wav) frames_batch = np.array([frames[s] for s in mel_slices]) partial_embeds = embed_frames_batch(frames_batch) # Compute the utterance embedding from the partial embeddings raw_embed = np.mean(partial_embeds, axis=0) embed = raw_embed / np.linalg.norm(raw_embed, 2) if return_partials: return embed, partial_embeds, wave_slices return embed def embed_speaker(wavs, **kwargs): raise NotImplemented() def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)): if ax is None: ax = plt.gca() if shape is None: height = int(np.sqrt(len(embed))) shape = (height, -1) embed = embed.reshape(shape) cmap = cm.get_cmap() mappable = ax.imshow(embed, cmap=cmap) cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04) sm = cm.ScalarMappable(cmap=cmap) sm.set_clim(*color_range) ax.set_xticks([]), ax.set_yticks([]) ax.set_title(title)