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72c09ec0b7* indextts2 * update lfs for audio files --------- Co-authored-by: wangyining02 <wangyining02@bilibili.com>
259 lines
8 KiB
Python
259 lines
8 KiB
Python
import torch
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import librosa
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import json5
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from huggingface_hub import hf_hub_download
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from transformers import SeamlessM4TFeatureExtractor, Wav2Vec2BertModel
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import safetensors
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import numpy as np
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from indextts.utils.maskgct.models.codec.kmeans.repcodec_model import RepCodec
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from indextts.utils.maskgct.models.tts.maskgct.maskgct_s2a import MaskGCT_S2A
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from indextts.utils.maskgct.models.codec.amphion_codec.codec import CodecEncoder, CodecDecoder
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import time
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def _load_config(config_fn, lowercase=False):
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"""Load configurations into a dictionary
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Args:
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config_fn (str): path to configuration file
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lowercase (bool, optional): whether changing keys to lower case. Defaults to False.
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Returns:
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dict: dictionary that stores configurations
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"""
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with open(config_fn, "r") as f:
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data = f.read()
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config_ = json5.loads(data)
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if "base_config" in config_:
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# load configurations from new path
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p_config_path = os.path.join(os.getenv("WORK_DIR"), config_["base_config"])
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p_config_ = _load_config(p_config_path)
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config_ = override_config(p_config_, config_)
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if lowercase:
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# change keys in config_ to lower case
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config_ = get_lowercase_keys_config(config_)
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return config_
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def load_config(config_fn, lowercase=False):
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"""Load configurations into a dictionary
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Args:
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config_fn (str): path to configuration file
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lowercase (bool, optional): _description_. Defaults to False.
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Returns:
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JsonHParams: an object that stores configurations
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"""
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config_ = _load_config(config_fn, lowercase=lowercase)
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# create an JsonHParams object with configuration dict
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cfg = JsonHParams(**config_)
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return cfg
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class JsonHParams:
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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if type(v) == dict:
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v = JsonHParams(**v)
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self[k] = v
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def keys(self):
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return self.__dict__.keys()
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def items(self):
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return self.__dict__.items()
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def values(self):
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return self.__dict__.values()
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def __len__(self):
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return len(self.__dict__)
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def __getitem__(self, key):
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return getattr(self, key)
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def __setitem__(self, key, value):
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return setattr(self, key, value)
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def __contains__(self, key):
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return key in self.__dict__
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def __repr__(self):
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return self.__dict__.__repr__()
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def build_semantic_model(path_='./models/tts/maskgct/ckpt/wav2vec2bert_stats.pt'):
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semantic_model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
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semantic_model.eval()
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stat_mean_var = torch.load(path_)
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semantic_mean = stat_mean_var["mean"]
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semantic_std = torch.sqrt(stat_mean_var["var"])
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return semantic_model, semantic_mean, semantic_std
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def build_semantic_codec(cfg):
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semantic_codec = RepCodec(cfg=cfg)
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semantic_codec.eval()
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return semantic_codec
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def build_s2a_model(cfg, device):
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soundstorm_model = MaskGCT_S2A(cfg=cfg)
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soundstorm_model.eval()
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soundstorm_model.to(device)
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return soundstorm_model
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def build_acoustic_codec(cfg, device):
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codec_encoder = CodecEncoder(cfg=cfg.encoder)
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codec_decoder = CodecDecoder(cfg=cfg.decoder)
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codec_encoder.eval()
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codec_decoder.eval()
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codec_encoder.to(device)
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codec_decoder.to(device)
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return codec_encoder, codec_decoder
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class Inference_Pipeline():
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def __init__(
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self,
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semantic_model,
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semantic_codec,
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semantic_mean,
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semantic_std,
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codec_encoder,
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codec_decoder,
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s2a_model_1layer,
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s2a_model_full,
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):
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self.semantic_model = semantic_model
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self.semantic_codec = semantic_codec
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self.semantic_mean = semantic_mean
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self.semantic_std = semantic_std
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self.codec_encoder = codec_encoder
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self.codec_decoder = codec_decoder
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self.s2a_model_1layer = s2a_model_1layer
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self.s2a_model_full = s2a_model_full
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@torch.no_grad()
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def get_emb(self, input_features, attention_mask):
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vq_emb = self.semantic_model(
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input_features=input_features,
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attention_mask=attention_mask,
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output_hidden_states=True,
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)
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feat = vq_emb.hidden_states[17] # (B, T, C)
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feat = (feat - self.semantic_mean.to(feat)) / self.semantic_std.to(feat)
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return feat
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@torch.no_grad()
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def extract_acoustic_code(self, speech):
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vq_emb = self.codec_encoder(speech.unsqueeze(1))
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_, vq, _, _, _ = self.codec_decoder.quantizer(vq_emb)
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acoustic_code = vq.permute(1, 2, 0)
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return acoustic_code
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@torch.no_grad()
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def get_scode(self, inputs):
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semantic_code, feat = self.semantic_codec.quantize(inputs)
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# vq = self.semantic_codec.quantizer.vq2emb(semantic_code.unsqueeze(1))
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# vq = vq.transpose(1,2)
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return semantic_code
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@torch.no_grad()
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def semantic2acoustic(
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self,
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combine_semantic_code,
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acoustic_code,
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n_timesteps=[25, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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cfg=2.5,
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rescale_cfg=0.75,
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):
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semantic_code = combine_semantic_code
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cond = self.s2a_model_1layer.cond_emb(semantic_code)
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prompt = acoustic_code[:, :, :]
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predict_1layer = self.s2a_model_1layer.reverse_diffusion(
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cond=cond,
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prompt=prompt,
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temp=1.5,
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filter_thres=0.98,
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n_timesteps=n_timesteps[:1],
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cfg=cfg,
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rescale_cfg=rescale_cfg,
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)
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cond = self.s2a_model_full.cond_emb(semantic_code)
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prompt = acoustic_code[:, :, :]
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predict_full = self.s2a_model_full.reverse_diffusion(
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cond=cond,
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prompt=prompt,
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temp=1.5,
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filter_thres=0.98,
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n_timesteps=n_timesteps,
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cfg=cfg,
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rescale_cfg=rescale_cfg,
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gt_code=predict_1layer,
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)
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vq_emb = self.codec_decoder.vq2emb(
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predict_full.permute(2, 0, 1), n_quantizers=12
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)
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recovered_audio = self.codec_decoder(vq_emb)
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prompt_vq_emb = self.codec_decoder.vq2emb(
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prompt.permute(2, 0, 1), n_quantizers=12
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)
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recovered_prompt_audio = self.codec_decoder(prompt_vq_emb)
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recovered_prompt_audio = recovered_prompt_audio[0][0].cpu().numpy()
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recovered_audio = recovered_audio[0][0].cpu().numpy()
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combine_audio = np.concatenate([recovered_prompt_audio, recovered_audio])
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return combine_audio, recovered_audio
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def s2a_inference(
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self,
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prompt_speech_path,
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combine_semantic_code,
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cfg=2.5,
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n_timesteps_s2a=[25, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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cfg_s2a=2.5,
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rescale_cfg_s2a=0.75,
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):
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speech = librosa.load(prompt_speech_path, sr=24000)[0]
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acoustic_code = self.extract_acoustic_code(
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torch.tensor(speech).unsqueeze(0).to(combine_semantic_code.device)
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)
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_, recovered_audio = self.semantic2acoustic(
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combine_semantic_code,
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acoustic_code,
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n_timesteps=n_timesteps_s2a,
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cfg=cfg_s2a,
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rescale_cfg=rescale_cfg_s2a,
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)
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return recovered_audio
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@torch.no_grad()
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def gt_inference(
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self,
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prompt_speech_path,
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combine_semantic_code,
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):
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speech = librosa.load(prompt_speech_path, sr=24000)[0]
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'''
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acoustic_code = self.extract_acoustic_code(
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torch.tensor(speech).unsqueeze(0).to(combine_semantic_code.device)
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)
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prompt = acoustic_code[:, :, :]
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prompt_vq_emb = self.codec_decoder.vq2emb(
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prompt.permute(2, 0, 1), n_quantizers=12
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)
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'''
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prompt_vq_emb = self.codec_encoder(torch.tensor(speech).unsqueeze(0).unsqueeze(1).to(combine_semantic_code.device))
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recovered_prompt_audio = self.codec_decoder(prompt_vq_emb)
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recovered_prompt_audio = recovered_prompt_audio[0][0].cpu().numpy()
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return recovered_prompt_audio
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