Update extract_feature_print.py

extract_feature_print.py

@@ -1,122 +1,123 @@
-import os, sys, traceback
-
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
-
-# device=sys.argv[1]
-n_part = int(sys.argv[2])
-i_part = int(sys.argv[3])
-if len(sys.argv) == 6:
-    exp_dir = sys.argv[4]
-    version = sys.argv[5]
-else:
-    i_gpu = sys.argv[4]
-    exp_dir = sys.argv[5]
-    os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
-    version = sys.argv[6]
-import torch
-import torch.nn.functional as F
-import soundfile as sf
-import numpy as np
-from fairseq import checkpoint_utils
-
-device = "cpu"
-if torch.cuda.is_available():
-    device = "cuda"
-elif torch.backends.mps.is_available():
-    device = "mps"
-
-f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
-
-
-def printt(strr):
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-
-
-printt(sys.argv)
-model_path = os.environ.get('feature_extraction_method')
-
-printt(exp_dir)
-wavPath = "%s/1_16k_wavs" % exp_dir
-outPath = (
-    "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir
-)
-os.makedirs(outPath, exist_ok=True)
-
-# wave must be 16k, hop_size=320
-def readwave(wav_path, normalize=False):
-    wav, sr = sf.read(wav_path)
-    assert sr == 16000
-    feats = torch.from_numpy(wav).float()
-    if feats.dim() == 2:  # double channels
-        feats = feats.mean(-1)
-    assert feats.dim() == 1, feats.dim()
-    if normalize:
-        with torch.no_grad():
-            feats = F.layer_norm(feats, feats.shape)
-    feats = feats.view(1, -1)
-    return feats
-
-
-# HuBERT model
-printt("load model(s) from {}".format(model_path))
-# if hubert model is exist
-if os.access(model_path, os.F_OK) == False:
-    printt(
-        "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main"
-        % model_path
-    )
-    exit(0)
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [model_path],
-    suffix="",
-)
-model = models[0]
-model = model.to(device)
-printt("move model to %s" % device)
-if device not in ["mps", "cpu"]:
-    model = model.half()
-model.eval()
-
-todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
-n = max(1, len(todo) // 10)  # 最多打印十条
-if len(todo) == 0:
-    printt("no-feature-todo")
-else:
-    printt("all-feature-%s" % len(todo))
-    for idx, file in enumerate(todo):
-        try:
-            if file.endswith(".wav"):
-                wav_path = "%s/%s" % (wavPath, file)
-                out_path = "%s/%s" % (outPath, file.replace("wav", "npy"))
-
-                if os.path.exists(out_path):
-                    continue
-
-                feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
-                padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-                inputs = {
-                    "source": feats.half().to(device)
-                    if device not in ["mps", "cpu"]
-                    else feats.to(device),
-                    "padding_mask": padding_mask.to(device),
-                    "output_layer": 9 if version == "v1" else 12,  # layer 9
-                }
-                with torch.no_grad():
-                    logits = model.extract_features(**inputs)
-                    feats = (
-                        model.final_proj(logits[0]) if version == "v1" else logits[0]
-                    )
-
-                feats = feats.squeeze(0).float().cpu().numpy()
-                if np.isnan(feats).sum() == 0:
-                    np.save(out_path, feats, allow_pickle=False)
-                else:
-                    printt("%s-contains nan" % file)
-                if idx % n == 0:
-                    printt("now-%s,all-%s,%s,%s" % (idx, len(todo), file, feats.shape))
-        except:
-            printt(traceback.format_exc())
-    printt("all-feature-done")
+import os, sys, traceback
+import feature_extraction_method
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
+
+# device=sys.argv[1]
+n_part = int(sys.argv[2])
+i_part = int(sys.argv[3])
+if len(sys.argv) == 6:
+    exp_dir = sys.argv[4]
+    version = sys.argv[5]
+else:
+    i_gpu = sys.argv[4]
+    exp_dir = sys.argv[5]
+    os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
+    version = sys.argv[6]
+import torch
+import torch.nn.functional as F
+import soundfile as sf
+import numpy as np
+from fairseq import checkpoint_utils
+
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+elif torch.backends.mps.is_available():
+    device = "mps"
+
+f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
+
+
+def printt(strr):
+    print(strr)
+    f.write("%s\n" % strr)
+    f.flush()
+
+
+printt(sys.argv)
+model_path = feature_extraction_method
+
+printt(exp_dir)
+wavPath = "%s/1_16k_wavs" % exp_dir
+outPath = (
+    "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir
+)
+os.makedirs(outPath, exist_ok=True)
+
+# wave must be 16k, hop_size=320
+def readwave(wav_path, normalize=False):
+    wav, sr = sf.read(wav_path)
+    assert sr == 16000
+    feats = torch.from_numpy(wav).float()
+    if feats.dim() == 2:  # double channels
+        feats = feats.mean(-1)
+    assert feats.dim() == 1, feats.dim()
+    if normalize:
+        with torch.no_grad():
+            feats = F.layer_norm(feats, feats.shape)
+    feats = feats.view(1, -1)
+    return feats
+
+
+# HuBERT model
+printt("load model(s) from {}".format(model_path))
+# if hubert model is exist
+if os.access(model_path, os.F_OK) == False:
+    printt(
+        "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main"
+        % model_path
+    )
+    exit(0)
+models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+    [model_path],
+    suffix="",
+)
+model = models[0]
+model = model.to(device)
+printt("move model to %s" % device)
+if device not in ["mps", "cpu"]:
+    model = model.half()
+model.eval()
+
+todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
+n = max(1, len(todo) // 10)  # 最多打印十条
+if len(todo) == 0:
+    printt("no-feature-todo")
+else:
+    printt("all-feature-%s" % len(todo))
+    for idx, file in enumerate(todo):
+        try:
+            if file.endswith(".wav"):
+                wav_path = "%s/%s" % (wavPath, file)
+                out_path = "%s/%s" % (outPath, file.replace("wav", "npy"))
+
+                if os.path.exists(out_path):
+                    continue
+
+                feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
+                padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+                inputs = {
+                    "source": feats.half().to(device)
+                    if device not in ["mps", "cpu"]
+                    else feats.to(device),
+                    "padding_mask": padding_mask.to(device),
+                    "output_layer": 9 if version == "v1" else 12,  # layer 9
+                }
+                with torch.no_grad():
+                    logits = model.extract_features(**inputs)
+                    feats = (
+                        model.final_proj(logits[0]) if version == "v1" else logits[0]
+                    )
+
+                feats = feats.squeeze(0).float().cpu().numpy()
+                if np.isnan(feats).sum() == 0:
+                    np.save(out_path, feats, allow_pickle=False)
+                else:
+                    printt("%s-contains nan" % file)
+                if idx % n == 0:
+                    printt("now-%s,all-%s,%s,%s" % (idx, len(todo), file, feats.shape))
+        except:
+            printt(traceback.format_exc())
+    printt("all-feature-done")