GibbsTTS / amphion_utils /__init__.py
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import torch
from safetensors.torch import load_model
import json
from importlib.resources import files
from types import SimpleNamespace
from .amphion_codec.codec import CodecEncoder, CodecDecoder
class amphion_codec:
def __init__(self, configs, device=None):
self.device = device
cfg_path = files(__package__) / "maskgct.json"
with open(cfg_path, "r", encoding="utf-8") as f:
cfg = json.load(f, object_hook=lambda d: SimpleNamespace(**d))
self.codec_encoder, self.codec_decoder = self.build_acoustic_codec(cfg.model.acoustic_codec, device)
load_model(self.codec_encoder, f"{configs.infer_ckpt_dir}/MaskGCT_codec_encoder.safetensors")
load_model(self.codec_decoder, f"{configs.infer_ckpt_dir}/MaskGCT_codec_decoder.safetensors")
def build_acoustic_codec(self, cfg, device):
codec_encoder = CodecEncoder(cfg=cfg.encoder)
codec_decoder = CodecDecoder(cfg=cfg.decoder)
codec_encoder.eval()
codec_decoder.eval()
codec_encoder.to(device)
codec_decoder.to(device)
return codec_encoder, codec_decoder
@torch.no_grad()
def encode(self, speech_24k):
vq_emb = self.codec_encoder(speech_24k)
_, vq, _, _, _ = self.codec_decoder.quantizer(vq_emb)
acoustic_code = vq.permute(1, 2, 0)
return acoustic_code # [b, l, c]
@torch.no_grad()
def decode(self, tokens, n_quantizers=12):
vq_emb = self.codec_decoder.vq2emb(tokens.permute(2, 0, 1), n_quantizers)
recovered_audio = self.codec_decoder(vq_emb)
recovered_audio = recovered_audio[0][0].cpu().numpy()
return recovered_audio