dreamvoice/train_utils/src/speaker_encoder/compute_embed.py

from speaker_encoder import inference as encoder
from multiprocessing.pool import Pool
from functools import partial
from pathlib import Path
# from utils import logmmse
# from tqdm import tqdm
# import numpy as np
# import librosa


def embed_utterance(fpaths, encoder_model_fpath):
    if not encoder.is_loaded():
        encoder.load_model(encoder_model_fpath)

    # Compute the speaker embedding of the utterance
    wav_fpath, embed_fpath = fpaths
    wav = np.load(wav_fpath)
    wav = encoder.preprocess_wav(wav)
    embed = encoder.embed_utterance(wav)
    np.save(embed_fpath, embed, allow_pickle=False)
    
 
def create_embeddings(outdir_root: Path, wav_dir: Path, encoder_model_fpath: Path, n_processes: int):

    wav_dir = outdir_root.joinpath("audio")
    metadata_fpath = synthesizer_root.joinpath("train.txt")
    assert wav_dir.exists() and metadata_fpath.exists()
    embed_dir = synthesizer_root.joinpath("embeds")
    embed_dir.mkdir(exist_ok=True)
    
    # Gather the input wave filepath and the target output embed filepath
    with metadata_fpath.open("r") as metadata_file:
        metadata = [line.split("|") for line in metadata_file]
        fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
        
    # TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
    # Embed the utterances in separate threads
    func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
    job = Pool(n_processes).imap(func, fpaths)
    list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))