mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
import os, sys, traceback
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# device=sys.argv[1]
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n_part = int(sys.argv[2])
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i_part = int(sys.argv[3])
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if len(sys.argv) == 5:
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exp_dir = sys.argv[4]
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else:
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i_gpu = sys.argv[4]
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exp_dir = sys.argv[5]
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os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
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import torch
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import torch.nn.functional as F
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import soundfile as sf
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import numpy as np
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from fairseq import checkpoint_utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():
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device = "cuda"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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def printt(strr):
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print(strr)
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f.write("%s\n" % strr)
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f.flush()
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printt(sys.argv)
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model_path = "hubert_base.pt"
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printt(exp_dir)
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wavPath = "%s/1_16k_wavs" % exp_dir
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outPath = "%s/3_feature256" % exp_dir
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os.makedirs(outPath, exist_ok=True)
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# wave must be 16k, hop_size=320
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def readwave(wav_path, normalize=False):
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wav, sr = sf.read(wav_path)
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assert sr == 16000
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feats = torch.from_numpy(wav).float()
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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if normalize:
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with torch.no_grad():
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feats = F.layer_norm(feats, feats.shape)
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feats = feats.view(1, -1)
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return feats
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# HuBERT model
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printt("load model(s) from {}".format(model_path))
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[model_path],
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suffix="",
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)
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model = models[0]
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model = model.to(device)
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printt("move model to %s" % device)
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if device not in ["mps", "cpu"]:
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model = model.half()
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model.eval()
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todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
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n = max(1, len(todo) // 10) # 最多打印十条
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if len(todo) == 0:
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printt("no-feature-todo")
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else:
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printt("all-feature-%s" % len(todo))
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for idx, file in enumerate(todo):
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try:
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if file.endswith(".wav"):
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wav_path = "%s/%s" % (wavPath, file)
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out_path = "%s/%s" % (outPath, file.replace("wav", "npy"))
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if os.path.exists(out_path):
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continue
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feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.half().to(device)
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if device not in ["mps", "cpu"]
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else feats.to(device),
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"padding_mask": padding_mask.to(device),
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"output_layer": 9, # layer 9
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}
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0])
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feats = feats.squeeze(0).float().cpu().numpy()
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if np.isnan(feats).sum() == 0:
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np.save(out_path, feats, allow_pickle=False)
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else:
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printt("%s-contains nan" % file)
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if idx % n == 0:
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printt("now-%s,all-%s,%s,%s" % (len(todo), idx, file, feats.shape))
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except:
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printt(traceback.format_exc())
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printt("all-feature-done")
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