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# Copyright (c) 2024 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import librosa
import os
import pickle
import math
import json
import accelerate
import safetensors
from utils.util import load_config
from tqdm import tqdm
from models.codec.kmeans.repcodec_model import RepCodec
from models.tts.maskgct.maskgct_s2a import MaskGCT_S2A
from models.tts.maskgct.maskgct_t2s import MaskGCT_T2S
from models.codec.amphion_codec.codec import CodecEncoder, CodecDecoder
from transformers import Wav2Vec2BertModel
from models.tts.maskgct.g2p.g2p_generation import g2p, chn_eng_g2p
from transformers import SeamlessM4TFeatureExtractor
def g2p_(text, language):
if language in ["zh", "en"]:
return chn_eng_g2p(text)
else:
return g2p(text, sentence=None, language=language)
def build_t2s_model(cfg, device):
t2s_model = MaskGCT_T2S(cfg=cfg)
t2s_model.eval()
t2s_model.to(device)
return t2s_model
def build_s2a_model(cfg, device):
soundstorm_model = MaskGCT_S2A(cfg=cfg)
soundstorm_model.eval()
soundstorm_model.to(device)
return soundstorm_model
def build_semantic_model(device):
semantic_model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
semantic_model.eval()
semantic_model.to(device)
stat_mean_var = torch.load("./models/tts/maskgct/ckpt/wav2vec2bert_stats.pt")
semantic_mean = stat_mean_var["mean"]
semantic_std = torch.sqrt(stat_mean_var["var"])
semantic_mean = semantic_mean.to(device)
semantic_std = semantic_std.to(device)
return semantic_model, semantic_mean, semantic_std
def build_semantic_codec(cfg, device):
semantic_codec = RepCodec(cfg=cfg)
semantic_codec.eval()
semantic_codec.to(device)
return semantic_codec
def build_acoustic_codec(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
class MaskGCT_Inference_Pipeline:
def __init__(
self,
semantic_model,
semantic_codec,
codec_encoder,
codec_decoder,
t2s_model,
s2a_model_1layer,
s2a_model_full,
semantic_mean,
semantic_std,
device,
):
self.processor = SeamlessM4TFeatureExtractor.from_pretrained(
"facebook/w2v-bert-2.0"
)
self.semantic_model = semantic_model
self.semantic_codec = semantic_codec
self.codec_encoder = codec_encoder
self.codec_decoder = codec_decoder
self.t2s_model = t2s_model
self.s2a_model_1layer = s2a_model_1layer
self.s2a_model_full = s2a_model_full
self.semantic_mean = semantic_mean
self.semantic_std = semantic_std
self.device = device
@torch.no_grad()
def extract_features(self, speech):
inputs = self.processor(speech, sampling_rate=16000, return_tensors="pt")
input_features = inputs["input_features"][0]
attention_mask = inputs["attention_mask"][0]
return input_features, attention_mask
@torch.no_grad()
def extract_semantic_code(self, input_features, attention_mask):
vq_emb = self.semantic_model(
input_features=input_features,
attention_mask=attention_mask,
output_hidden_states=True,
)
feat = vq_emb.hidden_states[17] # (B, T, C)
feat = (feat - self.semantic_mean.to(feat)) / self.semantic_std.to(feat)
semantic_code, rec_feat = self.semantic_codec.quantize(feat) # (B, T)
return semantic_code, rec_feat
@torch.no_grad()
def extract_acoustic_code(self, speech):
vq_emb = self.codec_encoder(speech.unsqueeze(1))
_, vq, _, _, _ = self.codec_decoder.quantizer(vq_emb)
acoustic_code = vq.permute(1, 2, 0)
return acoustic_code
@torch.no_grad()
def text2semantic(
self,
prompt_speech,
prompt_text,
prompt_language,
target_text,
target_language,
target_len=None,
n_timesteps=50,
cfg=2.5,
rescale_cfg=0.75,
):
prompt_phone_id = g2p_(prompt_text, prompt_language)[1]
target_phone_id = g2p_(target_text, target_language)[1]
if target_len is None:
target_len = int(
(len(prompt_speech) * len(target_phone_id) / len(prompt_phone_id))
/ 16000
* 50
)
else:
target_len = int(target_len * 50)
prompt_phone_id = torch.tensor(prompt_phone_id, dtype=torch.long).to(
self.device
)
target_phone_id = torch.tensor(target_phone_id, dtype=torch.long).to(
self.device
)
phone_id = torch.cat([prompt_phone_id, target_phone_id])
input_features, attention_mask = self.extract_features(prompt_speech)
input_features = input_features.unsqueeze(0).to(self.device)
attention_mask = attention_mask.unsqueeze(0).to(self.device)
semantic_code, _ = self.extract_semantic_code(input_features, attention_mask)
predict_semantic = self.t2s_model.reverse_diffusion(
semantic_code[:, :],
target_len,
phone_id.unsqueeze(0),
n_timesteps=n_timesteps,
cfg=cfg,
rescale_cfg=rescale_cfg,
)
print("predict semantic shape", predict_semantic.shape)
combine_semantic_code = torch.cat(
[semantic_code[:, :], predict_semantic], dim=-1
)
prompt_semantic_code = semantic_code
return combine_semantic_code, prompt_semantic_code
@torch.no_grad()
def semantic2acoustic(
self,
combine_semantic_code,
acoustic_code,
n_timesteps=[25, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
cfg=2.5,
rescale_cfg=0.75,
):
semantic_code = combine_semantic_code
cond = self.s2a_model_1layer.cond_emb(semantic_code)
prompt = acoustic_code[:, :, :]
predict_1layer = self.s2a_model_1layer.reverse_diffusion(
cond=cond,
prompt=prompt,
temp=1.5,
filter_thres=0.98,
n_timesteps=n_timesteps[:1],
cfg=cfg,
rescale_cfg=rescale_cfg,
)
cond = self.s2a_model_full.cond_emb(semantic_code)
prompt = acoustic_code[:, :, :]
predict_full = self.s2a_model_full.reverse_diffusion(
cond=cond,
prompt=prompt,
temp=1.5,
filter_thres=0.98,
n_timesteps=n_timesteps,
cfg=cfg,
rescale_cfg=rescale_cfg,
gt_code=predict_1layer,
)
vq_emb = self.codec_decoder.vq2emb(
predict_full.permute(2, 0, 1), n_quantizers=12
)
recovered_audio = self.codec_decoder(vq_emb)
prompt_vq_emb = self.codec_decoder.vq2emb(
prompt.permute(2, 0, 1), n_quantizers=12
)
recovered_prompt_audio = self.codec_decoder(prompt_vq_emb)
recovered_prompt_audio = recovered_prompt_audio[0][0].cpu().numpy()
recovered_audio = recovered_audio[0][0].cpu().numpy()
combine_audio = np.concatenate([recovered_prompt_audio, recovered_audio])
return combine_audio, recovered_audio
def maskgct_inference(
self,
prompt_speech_path,
prompt_text,
target_text,
language="en",
target_language="en",
target_len=None,
n_timesteps=25,
cfg=2.5,
rescale_cfg=0.75,
n_timesteps_s2a=[25, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
cfg_s2a=2.5,
rescale_cfg_s2a=0.75,
):
speech_16k = librosa.load(prompt_speech_path, sr=16000)[0]
speech = librosa.load(prompt_speech_path, sr=24000)[0]
combine_semantic_code, _ = self.text2semantic(
speech_16k,
prompt_text,
language,
target_text,
target_language,
target_len,
n_timesteps,
cfg,
rescale_cfg,
)
acoustic_code = self.extract_acoustic_code(
torch.tensor(speech).unsqueeze(0).to(self.device)
)
_, recovered_audio = self.semantic2acoustic(
combine_semantic_code,
acoustic_code,
n_timesteps=n_timesteps_s2a,
cfg=cfg_s2a,
rescale_cfg=rescale_cfg_s2a,
)
return recovered_audio
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