Function embed_utterance

encoder/inference.py:111–155

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

(wav, using_partials=True, return_partials=False, **kwargs)

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 
    <partial_utterance_n_frames> 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 
    <return_partials> 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 <using_partials> 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):