Delete extract_feature_print.py

extract_feature_print.py

@@ -1,298 +0,0 @@
-import os, sys, traceback
-from transformers import HubertModel
-import librosa
-from torch import nn
-import torch
-
-import json
-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"
-    
-version_config_paths = [
-    os.path.join("", "32k.json"),
-    os.path.join("", "40k.json"),
-    os.path.join("", "48k.json"),
-    os.path.join("", "48k_v2.json"),
-    os.path.join("", "40k.json"),
-    os.path.join("", "32k_v2.json"),
-]
-
-class Config:
-    def __init__(self):
-        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
-        self.is_half = self.device != "cpu"
-        self.gpu_name = (
-            torch.cuda.get_device_name(int(self.device.split(":")[-1]))
-            if self.device.startswith("cuda")
-            else None
-        )
-        self.json_config = self.load_config_json()
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def load_config_json(self) -> dict:
-        configs = {}
-        for config_file in version_config_paths:
-            config_path = os.path.join("configs", config_file)
-            with open(config_path, "r") as f:
-                configs[config_file] = json.load(f)
-        return configs
-
-    def has_mps(self) -> bool:
-        # Check if Metal Performance Shaders are available - for macOS 12.3+.
-        return torch.backends.mps.is_available()
-
-    def has_xpu(self) -> bool:
-        # Check if XPU is available.
-        return hasattr(torch, "xpu") and torch.xpu.is_available()
-
-    def set_precision(self, precision):
-        if precision not in ["fp32", "fp16"]:
-            raise ValueError("Invalid precision type. Must be 'fp32' or 'fp16'.")
-
-        fp16_run_value = precision == "fp16"
-        preprocess_target_version = "3.7" if precision == "fp16" else "3.0"
-        preprocess_path = os.path.join(
-            os.path.dirname(__file__),
-            os.pardir,
-            ""
-            "preprocess.py",
-        )
-
-        for config_path in version_config_paths:
-            full_config_path = os.path.join("configs", config_path)
-            try:
-                with open(full_config_path, "r") as f:
-                    config = json.load(f)
-                config["train"]["fp16_run"] = fp16_run_value
-                with open(full_config_path, "w") as f:
-                    json.dump(config, f, indent=4)
-            except FileNotFoundError:
-                print(f"File not found: {full_config_path}")
-
-        if os.path.exists(preprocess_path):
-            with open(preprocess_path, "r") as f:
-                preprocess_content = f.read()
-            preprocess_content = preprocess_content.replace(
-                "3.0" if precision == "fp16" else "3.7", preprocess_target_version
-            )
-            with open(preprocess_path, "w") as f:
-                f.write(preprocess_content)
-
-        return f"Overwritten preprocess and config.json to use {precision}."
-
-    def get_precision(self):
-        if not version_config_paths:
-            raise FileNotFoundError("No configuration paths provided.")
-
-        full_config_path = os.path.join("configs", version_config_paths[0])
-        try:
-            with open(full_config_path, "r") as f:
-                config = json.load(f)
-            fp16_run_value = config["train"].get("fp16_run", False)
-            precision = "fp16" if fp16_run_value else "fp32"
-            return precision
-        except FileNotFoundError:
-            print(f"File not found: {full_config_path}")
-            return None
-
-    def device_config(self) -> tuple:
-        if self.device.startswith("cuda"):
-            self.set_cuda_config()
-        elif self.has_mps():
-            self.device = "mps"
-            self.is_half = False
-            self.set_precision("fp32")
-        else:
-            self.device = "cpu"
-            self.is_half = False
-            self.set_precision("fp32")
-
-        # Configuration for 6GB GPU memory
-        x_pad, x_query, x_center, x_max = (
-            (3, 10, 60, 65) if self.is_half else (1, 6, 38, 41)
-        )
-        if self.gpu_mem is not None and self.gpu_mem <= 4:
-            # Configuration for 5GB GPU memory
-            x_pad, x_query, x_center, x_max = (1, 5, 30, 32)
-
-        return x_pad, x_query, x_center, x_max
-
-    def set_cuda_config(self):
-        i_device = int(self.device.split(":")[-1])
-        self.gpu_name = torch.cuda.get_device_name(i_device)
-        low_end_gpus = ["16", "P40", "P10", "1060", "1070", "1080"]
-        if (
-            any(gpu in self.gpu_name for gpu in low_end_gpus)
-            and "V100" not in self.gpu_name.upper()
-        ):
-            self.is_half = False
-            self.set_precision("fp32")
-
-        self.gpu_mem = torch.cuda.get_device_properties(i_device).total_memory // (
-            1024**3
-        )
-config = Config()
-
-def load_audio(file, sample_rate):
-    try:
-        file = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        audio, sr = sf.read(file)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.T)
-        if sr != sample_rate:
-            audio = librosa.resample(audio, orig_sr=sr, target_sr=sample_rate)
-    except Exception as error:
-        raise RuntimeError(f"An error occurred loading the audio: {error}")
-
-    return audio.flatten()
-
-#HuggingFacePlaceHolder = None
-class HubertModelWithFinalProj(HubertModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size)
-        print(config.hidden_size, config.classifier_proj_size)
-
-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 = sys.argv[7]
-Custom_Embed = False
-sample_embedding = sys.argv[8]
-if os.path.split(model_path)[-1] == "Custom" and sample_embedding == "hubert_base":
-    model_path = "hubert_base.pt"
-    Custom_Embed = True
-elif os.path.split(model_path)[-1] == "Custom" and sample_embedding == "contentvec_base":
-    model_path = "contentvec_base.pt"
-    Custom_Embed = True
-elif os.path.split(model_path)[-1] == "Custom" and sample_embedding == "hubert_base_japanese":
-    model_path = "japanese_hubert_base.pt"
-    Custom_Embed = True
-
-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
-    if Custom_Embed == False:
-        feats = torch.from_numpy(wav).float()
-    else:
-        feats = torch.from_numpy(load_audio(wav_path, sr)).to(dtype).to(device)
-    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="",
-)
-if Custom_Embed == False:
-    model = models[0]
-    if device not in ["mps", "cpu"]:
-        model = model.half()
-else:
-    dtype = torch.float16 if config.is_half and "cuda" in device else torch.float32
-    model = HubertModelWithFinalProj.from_pretrained("Custom/").to(dtype).to(device)
-model = model.to(device)
-printt("move model to %s" % device)
-model.eval()
-
-todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
-n = max(1, len(todo) // 10) 
-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():
-                    if Custom_Embed == False:
-                        logits = model.extract_features(**inputs)
-                        feats = (
-                            model.final_proj(logits[0]) if version == "v1" else logits[0]
-                        )
-                    elif Custom_Embed == True:
-                        feats = model(feats)["last_hidden_state"]
-                        feats = (
-                            model.final_proj(feats[0]).unsqueeze(0) if version == "v1" else feats
-                        )
-
-                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")