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from huggingface_hub import hf_hub_download
import torch
import safetensors
import soundfile as sf
if __name__ == "__main__":
# build model
device = torch.device("cuda:0")
cfg_path = "./models/tts/maskgct/config/maskgct.json"
cfg = load_config(cfg_path)
# 1. build semantic model (w2v-bert-2.0)
semantic_model, semantic_mean, semantic_std = build_semantic_model(device)
# 2. build semantic codec
semantic_codec = build_semantic_codec(cfg.model.semantic_codec, device)
# 3. build acoustic codec
codec_encoder, codec_decoder = build_acoustic_codec(cfg.model.acoustic_codec, device)
# 4. build t2s model
t2s_model = build_t2s_model(cfg.model.t2s_model, device)
# 5. build s2a model
s2a_model_1layer = build_s2a_model(cfg.model.s2a_model.s2a_1layer, device)
s2a_model_full = build_s2a_model(cfg.model.s2a_model.s2a_full, device)
# download checkpoint
# download semantic codec ckpt
semantic_code_ckpt = hf_hub_download("amphion/MaskGCT", filename="semantic_codec/model.safetensors")
# download acoustic codec ckpt
codec_encoder_ckpt = hf_hub_download("amphion/MaskGCT", filename="acoustic_codec/model.safetensors")
codec_decoder_ckpt = hf_hub_download("amphion/MaskGCT", filename="acoustic_codec/model_1.safetensors")
# download t2s model ckpt
t2s_model_ckpt = hf_hub_download("amphion/MaskGCT", filename="t2s_model/model.safetensors")
# download s2a model ckpt
s2a_1layer_ckpt = hf_hub_download("amphion/MaskGCT", filename="s2a_model/s2a_model_1layer/model.safetensors")
s2a_full_ckpt = hf_hub_download("amphion/MaskGCT", filename="s2a_model/s2a_model_full/model.safetensors")
# load semantic codec
safetensors.torch.load_model(semantic_codec, semantic_code_ckpt)
# load acoustic codec
safetensors.torch.load_model(codec_encoder, codec_encoder_ckpt)
safetensors.torch.load_model(codec_decoder, codec_decoder_ckpt)
# load t2s model
safetensors.torch.load_model(t2s_model, t2s_model_ckpt)
# load s2a model
safetensors.torch.load_model(s2a_model_1layer, s2a_1layer_ckpt)
safetensors.torch.load_model(s2a_model_full, s2a_full_ckpt)
# inference
prompt_wav_path = "/home/kade/maskgct/alan_watts_sample_2.wav"
save_path = "/home/kade/maskgct/save"
prompt_text = "About the basic ideas which as westerners living today in the United States influence our everyday common sense, our fundamental notions about what life is about and there are historical"
target_text = "I really like sucking on otter dicks. Subscribe to my fucking youtube channel!"
# Specify the target duration (in seconds). If target_len = None, we use a simple rule to predict the target duration.
target_len = 18
maskgct_inference_pipeline = MaskGCT_Inference_Pipeline(
semantic_model,
semantic_codec,
codec_encoder,
codec_decoder,
t2s_model,
s2a_model_1layer,
s2a_model_full,
semantic_mean,
semantic_std,
device,
)
recovered_audio = maskgct_inference_pipeline.maskgct_inference(
prompt_wav_path, prompt_text, target_text, "en", "en", target_len=target_len
)
sf.write(save_path, recovered_audio, 24000)
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