anime_speaker_embedding 0.1.1

Speaker embedding for anime speech domain based on ECAPA_TDNN

Anime Speaker Embedding

Overview

• ECAPA-TDNN model (from SpeechBrain) trained on OOPPEENN/VisualNovel_Dataset (a.k.a. Galgame_Dataset)
• This model is designed for speaker embedding tasks in anime and visual novel contexts.

Features

• Well-suited for Japanese anime-like voices, including non-verbal vocalizations or acted voices
• Also this model works well for NSFW erotic utterances and vocalizations such as aegi (喘ぎ) and chupa-sound (チュパ音) which are important culture in Japanese Visual Novel games, while other usual speaker embedding models cannot distinguish such voices of different speakers at all!

Installation

pip install torch --index-url https://download.pytorch.org/whl/cu128  # if you want to use GPU
pip install anime_speaker_embedding

Usage

from anime_speaker_embedding.model import AnimeSpeakerEmbedding
import torch


device = "cuda" if torch.cuda.is_available() else "cpu"
model = AnimeSpeakerEmbedding(device=device)
audio_path = "path/to/audio.wav"  # Path to the audio file
embedding = model.get_embedding(audio_path)
print(embedding.shape)  # np.array with shape (192,)

See example.ipynb for some usage and visualization examples.

Comparison with other models

The t-SNE plot of embeddings from some Galgames (not included in the training set!) is shown below.

Game	THIS MODEL	speechbrain/spkrec-ecapa-voxceleb	pyannote/wespeaker-voxceleb-resnet34-LM	Resemblyzer
Game1
Game2
Game3
Game4

• Game1 and Game2 contains NSFW voices, while Game3 and Game4 does not.
• In Game4, Brown and yellow speakers are actually the same character

Model Details

Model Architecture

The actual model is SpeechBrain's ECAPA-TDNN with all BatchNorm layers replaced with GroupNorm. This is because I encountered a problem with the original BathNorm layers when evaluating the model (maybe some statistics drifted).

Dataset

From all the audio files in the OOPPEENN/VisualNovel_Dataset dataset, we filtered out some broken audio files, and exluded the speakers with less than 100 audio files. The final dataset contains:

• train: 6,260,482 audio files, valid: 699,488 audio files, total: 6,959,970 audio files
• 7,357 speakers

Training process

• I used speechbrain/spkrec-ecapa-voxceleb as the base model
  • But after some fine-tuning, I replaced all BN with GN, so I don't know how actually the base model effects the final model
  • Also the scaling before fbank is added (x = x * 32768.0) (by misadvice of ChatGPT), so the original knowledge may not be fully transferred
• First I trained the model on the small subset (the top 100 or 1000 speakers w.r.t. the number of audio files)
• Then I trained the model on the full dataset
• Finally I trained the model on the full dataset with many online augmentations (including reverb, background noise, various filters, etc.)
• At some point, since some characters appear in several games (like FD or same series), I computed the confusion matrix of the model on the validation set, and merged some speakers with high confusion if they are from the same game maker and same character name

The training code will be released in maybe another repo.