| import os | |
| import sys | |
| import tqdm | |
| import torch | |
| import torch.nn.functional as F | |
| import fairseq | |
| import soundfile as sf | |
| import numpy as np | |
| import logging | |
| logging.getLogger("fairseq").setLevel(logging.WARNING) | |
| device = sys.argv[1] | |
| n_parts = int(sys.argv[2]) | |
| i_part = int(sys.argv[3]) | |
| if len(sys.argv) == 7: | |
| exp_dir, version, is_half = sys.argv[4], sys.argv[5], bool(sys.argv[6]) | |
| else: | |
| i_gpu, exp_dir = sys.argv[4], sys.argv[5] | |
| os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu) | |
| version, is_half = sys.argv[6], bool(sys.argv[7]) | |
| def forward_dml(ctx, x, scale): | |
| ctx.scale = scale | |
| res = x.clone().detach() | |
| return res | |
| fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml | |
| model_path = "hubert_base.pt" | |
| wav_path = f"{exp_dir}/1_16k_wavs" | |
| out_path = f"{exp_dir}/3_feature256" if version == "v1" else f"{exp_dir}/3_feature768" | |
| os.makedirs(out_path, exist_ok=True) | |
| def read_wave(wav_path, normalize=False): | |
| wav, sr = sf.read(wav_path) | |
| assert sr == 16000 | |
| feats = torch.from_numpy(wav) | |
| feats = feats.half() if is_half else feats.float() | |
| feats = feats.mean(-1) if feats.dim() == 2 else feats | |
| feats = feats.view(1, -1) | |
| if normalize: | |
| with torch.no_grad(): | |
| feats = F.layer_norm(feats, feats.shape) | |
| return feats | |
| print("Starting feature extraction...") | |
| models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( | |
| [model_path], | |
| suffix="", | |
| ) | |
| model = models[0] | |
| model = model.to(device) | |
| if device not in ["mps", "cpu"]: | |
| model = model.half() | |
| model.eval() | |
| todo = sorted(os.listdir(wav_path))[i_part::n_parts] | |
| n = max(1, len(todo) // 10) | |
| if len(todo) == 0: | |
| print( | |
| "An error occurred in the feature extraction, make sure you have provided the audios correctly." | |
| ) | |
| else: | |
| print(f"{len(todo)}") | |
| with tqdm.tqdm(total=len(todo)) as pbar: | |
| for idx, file in enumerate(todo): | |
| try: | |
| if file.endswith(".wav"): | |
| wav_file_path = os.path.join(wav_path, file) | |
| out_file_path = os.path.join(out_path, file.replace("wav", "npy")) | |
| if os.path.exists(out_file_path): | |
| continue | |
| feats = read_wave(wav_file_path, normalize=saved_cfg.task.normalize) | |
| padding_mask = torch.BoolTensor(feats.shape).fill_(False) | |
| inputs = { | |
| "source": feats.to(device), | |
| "padding_mask": padding_mask.to(device), | |
| "output_layer": 9 if version == "v1" else 12, | |
| } | |
| 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_file_path, feats, allow_pickle=False) | |
| else: | |
| print(f"{file} - contains nan") | |
| pbar.set_description(f"Processing {file} {feats.shape}") | |
| except Exception as error: | |
| print(error) | |
| pbar.update(1) | |
| print("Feature extraction completed successfully!") | |