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from typing import Callable
import numpy as np
from modules.utils.g2p import (
kana_to_phonemes_openjtalk,
pinyin_to_phonemes_ace,
pinyin_to_phonemes_opencpop,
)
from .base import AbstractSVSModel
from .registry import register_svs_model
@register_svs_model("espnet/")
class ESPNetSVS(AbstractSVSModel):
def __init__(self, model_id: str, device="auto", cache_dir="cache", **kwargs):
from espnet2.bin.svs_inference import SingingGenerate
from espnet_model_zoo.downloader import ModelDownloader
if device == "auto":
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
downloaded = ModelDownloader(cache_dir).download_and_unpack(model_id)
self.model = SingingGenerate(
train_config=downloaded["train_config"],
model_file=downloaded["model_file"],
device=self.device,
)
self.model_id = model_id
self.output_sample_rate = self.model.fs
self.phoneme_mappers = self._build_phoneme_mappers()
def _build_phoneme_mappers(self) -> dict[str, Callable[[str], list[str]]]:
if self.model_id == "espnet/aceopencpop_svs_visinger2_40singer_pretrain":
phoneme_mappers = {
"mandarin": pinyin_to_phonemes_opencpop,
}
elif self.model_id == "espnet/mixdata_svs_visinger2_spkemb_lang_pretrained":
def mandarin_mapper(pinyin: str) -> list[str]:
phns = pinyin_to_phonemes_ace(pinyin)
return [phn + "@zh" for phn in phns]
def japanese_mapper(kana: str) -> list[str]:
phones = kana_to_phonemes_openjtalk(kana)
return [phn + "@jp" for phn in phones]
phoneme_mappers = {
"mandarin": mandarin_mapper,
"japanese": japanese_mapper,
}
else:
phoneme_mappers = {}
return phoneme_mappers
def _preprocess(self, score: list[tuple[float, float, str, int] | tuple[float, float, str, float]], language: str):
if language not in self.phoneme_mappers:
raise ValueError(f"Unsupported language: {language} for {self.model_id}")
phoneme_mapper = self.phoneme_mappers[language]
# text to phoneme
notes = []
phns = []
pre_phn = None
for st, ed, text, pitch in score:
assert text not in [
"<AP>",
"<SP>",
], f"Proccessed score segments should not contain <AP> or <SP>. {score}" # TODO: remove in PR, only for debug
if text == "AP" or text == "SP":
lyric_units = [text]
phn_units = [text]
elif text == "-" or text == "——":
lyric_units = [text]
if pre_phn is None:
raise ValueError(
f"Text `{text}` cannot be recognized by {self.model_id}. Lyrics cannot start with a lyric continuation symbol `-` or `——`"
)
phn_units = [pre_phn]
else:
try:
lyric_units = phoneme_mapper(text)
except ValueError as e:
raise ValueError(
f"Text `{text}` cannot be recognized by {self.model_id}"
) from e
phn_units = lyric_units
notes.append((st, ed, "".join(lyric_units), pitch, "_".join(phn_units)))
phns.extend(phn_units)
pre_phn = phn_units[-1]
batch = {
"score": (
120, # does not affect svs result, as note durations are in time unit
notes,
),
"text": " ".join(phns),
}
return batch
def synthesize(
self, score: list[tuple[float, float, str, float] | tuple[float, float, str, int]], language: str, speaker: str, **kwargs
):
batch = self._preprocess(score, language)
if self.model_id == "espnet/aceopencpop_svs_visinger2_40singer_pretrain":
sid = np.array([int(speaker)])
output_dict = self.model(batch, sids=sid)
elif self.model_id == "espnet/mixdata_svs_visinger2_spkemb_lang_pretrained":
langs = {
"mandarin": 2,
"japanese": 1,
}
if language not in langs:
raise ValueError(
f"Unsupported language: {language} for {self.model_id}"
)
lid = np.array([langs[language]])
spk_embed = np.load(speaker)
output_dict = self.model(batch, lids=lid, spembs=spk_embed)
else:
raise NotImplementedError(f"Model {self.model_id} not supported")
wav_info = output_dict["wav"].cpu().numpy()
return wav_info, self.output_sample_rate
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