|
|
from pathlib import Path
|
|
|
|
|
|
import librosa
|
|
|
import torch
|
|
|
import perth
|
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
|
|
from .models.s3tokenizer import S3_SR
|
|
|
from .models.s3gen import S3GEN_SR, S3Gen
|
|
|
|
|
|
|
|
|
REPO_ID = "ResembleAI/chatterbox"
|
|
|
|
|
|
|
|
|
class ChatterboxVC:
|
|
|
ENC_COND_LEN = 6 * S3_SR
|
|
|
DEC_COND_LEN = 10 * S3GEN_SR
|
|
|
|
|
|
def __init__(
|
|
|
self,
|
|
|
s3gen: S3Gen,
|
|
|
device: str,
|
|
|
ref_dict: dict=None,
|
|
|
):
|
|
|
self.sr = S3GEN_SR
|
|
|
self.s3gen = s3gen
|
|
|
self.device = device
|
|
|
self.watermarker = perth.PerthImplicitWatermarker()
|
|
|
if ref_dict is None:
|
|
|
self.ref_dict = None
|
|
|
else:
|
|
|
self.ref_dict = {
|
|
|
k: v.to(device) if torch.is_tensor(v) else v
|
|
|
for k, v in ref_dict.items()
|
|
|
}
|
|
|
|
|
|
@classmethod
|
|
|
def from_local(cls, ckpt_dir, device) -> 'ChatterboxVC':
|
|
|
ckpt_dir = Path(ckpt_dir)
|
|
|
ref_dict = None
|
|
|
if (builtin_voice := ckpt_dir / "conds.pt").exists():
|
|
|
states = torch.load(builtin_voice)
|
|
|
ref_dict = states['gen']
|
|
|
|
|
|
s3gen = S3Gen()
|
|
|
s3gen.load_state_dict(
|
|
|
torch.load(ckpt_dir / "s3gen.pt")
|
|
|
)
|
|
|
s3gen.to(device).eval()
|
|
|
|
|
|
return cls(s3gen, device, ref_dict=ref_dict)
|
|
|
|
|
|
@classmethod
|
|
|
def from_pretrained(cls, device) -> 'ChatterboxVC':
|
|
|
for fpath in ["s3gen.pt", "conds.pt"]:
|
|
|
local_path = hf_hub_download(repo_id=REPO_ID, filename=fpath)
|
|
|
|
|
|
return cls.from_local(Path(local_path).parent, device)
|
|
|
|
|
|
def set_target_voice(self, wav_fpath):
|
|
|
|
|
|
s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR)
|
|
|
|
|
|
s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN]
|
|
|
self.ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device)
|
|
|
|
|
|
def generate(
|
|
|
self,
|
|
|
audio,
|
|
|
target_voice_path=None,
|
|
|
):
|
|
|
if target_voice_path:
|
|
|
self.set_target_voice(target_voice_path)
|
|
|
else:
|
|
|
assert self.ref_dict is not None, "Please `prepare_conditionals` first or specify `target_voice_path`"
|
|
|
|
|
|
with torch.inference_mode():
|
|
|
audio_16, _ = librosa.load(audio, sr=S3_SR)
|
|
|
audio_16 = torch.from_numpy(audio_16).float().to(self.device)[None, ]
|
|
|
|
|
|
s3_tokens, _ = self.s3gen.tokenizer(audio_16)
|
|
|
wav, _ = self.s3gen.inference(
|
|
|
speech_tokens=s3_tokens,
|
|
|
ref_dict=self.ref_dict,
|
|
|
)
|
|
|
wav = wav.squeeze(0).detach().cpu().numpy()
|
|
|
watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr)
|
|
|
return torch.from_numpy(watermarked_wav).unsqueeze(0)
|
|
|
|
