"""modified from https://github.com/yesheng-THU/GFGE/blob/main/data_processing/audio_features.py""" import numpy as np import librosa import math import os import scipy.io.wavfile as wav import torch import torch.nn as nn import torch.nn.functional as F import copy from tqdm import tqdm from transformers import Wav2Vec2Model, Wav2Vec2Config from transformers.modeling_outputs import BaseModelOutput from typing import Optional, Tuple _CONFIG_FOR_DOC = "Wav2Vec2Config" # the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model # initialize our encoder with the pre-trained wav2vec 2.0 weights. def _compute_mask_indices( shape: Tuple[int, int], mask_prob: float, mask_length: int, attention_mask: Optional[torch.Tensor] = None, min_masks: int = 0, ) -> np.ndarray: bsz, all_sz = shape mask = np.full((bsz, all_sz), False) all_num_mask = int( mask_prob * all_sz / float(mask_length) + np.random.rand() ) all_num_mask = max(min_masks, all_num_mask) mask_idcs = [] padding_mask = attention_mask.ne(1) if attention_mask is not None else None for i in range(bsz): if padding_mask is not None: sz = all_sz - padding_mask[i].long().sum().item() num_mask = int( mask_prob * sz / float(mask_length) + np.random.rand() ) num_mask = max(min_masks, num_mask) else: sz = all_sz num_mask = all_num_mask lengths = np.full(num_mask, mask_length) if sum(lengths) == 0: lengths[0] = min(mask_length, sz - 1) min_len = min(lengths) if sz - min_len <= num_mask: min_len = sz - num_mask - 1 mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]) mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) min_len = min([len(m) for m in mask_idcs]) for i, mask_idc in enumerate(mask_idcs): if len(mask_idc) > min_len: mask_idc = np.random.choice(mask_idc, min_len, replace=False) mask[i, mask_idc] = True return mask # linear interpolation layer def linear_interpolation(features, input_fps, output_fps, output_len=None): features = features.transpose(1, 2) seq_len = features.shape[2] / float(input_fps) if output_len is None: output_len = int(seq_len * output_fps) output_features = F.interpolate(features,size=output_len,align_corners=True,mode='linear') return output_features.transpose(1, 2) class Wav2Vec2Model(Wav2Vec2Model): def __init__(self, config): super().__init__(config) self.audio_fps = 15 #args.audio_fps #input_values 16K hz, 49fps, 20ms overlap, 25ms recepion field def forward( self, input_values, dataset="beat", attention_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, frame_num=None ): #print(input_values.shape) self.config.output_attentions = True output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = self.feature_extractor(input_values) hidden_states = hidden_states.transpose(1, 2) #print(hidden_states.shape) if dataset == "beat": hidden_states = linear_interpolation(hidden_states, 49, self.audio_fps, output_len=frame_num) #print(hidden_states.shape) if attention_mask is not None: output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)) attention_mask = torch.zeros( hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device ) attention_mask[ (torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1) ] = 1 attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() hidden_states = self.feature_projection(hidden_states)[0] #print(hidden_states.shape) if self.config.apply_spec_augment and self.training: batch_size, sequence_length, hidden_size = hidden_states.size() if self.config.mask_time_prob > 0: mask_time_indices = _compute_mask_indices( (batch_size, sequence_length), self.config.mask_time_prob, self.config.mask_time_length, attention_mask=attention_mask, min_masks=2, ) hidden_states[torch.from_numpy(mask_time_indices)] = self.masked_spec_embed.to(hidden_states.dtype) if self.config.mask_feature_prob > 0: mask_feature_indices = _compute_mask_indices( (batch_size, hidden_size), self.config.mask_feature_prob, self.config.mask_feature_length, ) mask_feature_indices = torch.from_numpy(mask_feature_indices).to(hidden_states.device) hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0 encoder_outputs = self.encoder( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = encoder_outputs[0] #print(encoder_outputs.shape) if not return_dict: return (hidden_states,) + encoder_outputs[1:] return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def extract_wav2vec2(file_folder, destpath, fps, inference_length=16000*20): wav2vec_model = Wav2Vec2Model.from_pretrained("/home/ma-user/work/datasets/hub/transformer/wav2vec2-base-960h") wav2vec_model.feature_extractor._freeze_parameters() wav2vec_model = wav2vec_model.cuda() wav2vec_model.eval() audio_mean = np.load("/home/ma-user/work/datasets/beat_cache/beat_english_15_141/train/wave16k/npy_mean.npy") audio_std = np.load("/home/ma-user/work/datasets/beat_cache/beat_english_15_141/train/wave16k/npy_std.npy") if not os.path.exists(destpath): os.mkdir(destpath) with torch.no_grad(): for file_name in tqdm(os.listdir(file_folder)): if "mean" in file_name or "std" in file_name or "pynb" in file_name: continue audio_np = np.load(file_folder+file_name) audio_np = (audio_np-audio_mean)/audio_std audio_torch = torch.from_numpy(audio_np).cuda() audio_torch = audio_torch.reshape(1, -1) #print(audio_torch.shape, audio_np.shape) if audio_torch.shape[1] > inference_length: num_div = audio_torch.shape[1] // inference_length remain = audio_torch.shape[1] % inference_length for i in range(num_div): audio_feat = wav2vec_model(audio_torch[:, i*inference_length:(i+1)*inference_length]).last_hidden_state.cpu().numpy().reshape(-1, 768) if i == 0: audio_feat_all = audio_feat else: audio_feat_all = np.concatenate((audio_feat_all, audio_feat), 0) if remain > 1600: #0.25s audio_feat = wav2vec_model(audio_torch[:, num_div*inference_length:num_div*inference_length+remain]).last_hidden_state.cpu().numpy().reshape(-1, 768) audio_feat_all = np.concatenate((audio_feat_all, audio_feat), 0) else: audio_feat_all = wav2vec_model(audio_torch).last_hidden_state.cpu().numpy().reshape(-1, 768) #print(audio_feat_all.shape, audio_np.shape[0]/16000*15, torch.cuda.memory_cached() / 1E9) np.save(destpath+file_name, audio_feat_all) def extract_melspec(file, destpath, fps, n_mels=128): fs,X = wav.read(file) X = X.astype(float)/math.pow(2,15) target_sr = 48000 X_48k = librosa.resample(X, orig_sr=fs, target_sr=target_sr, res_type="kaiser_best") n_fft=int(target_sr*0.13) hop_len=int(target_sr/fps) C = librosa.feature.melspectrogram(y=X_48k, sr=target_sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels, fmin=0.0, fmax=8000) #C2 = librosa.feature.melspectrogram(y=X, sr=fs, n_fft=1024, hop_length=512) #print(C.shape, C2.shape) C = np.log(C) np.save(destpath,np.transpose(C)) if __name__ == "__main__": #calculate mean and build cache for data. target_fps = 15 ori_data_path = f"/home/ma-user/work/datasets/beat_cache/beat_english_{target_fps}_141/" for data_type in ["train", "val", "test"]: extract_wav2vec2(ori_data_path+data_type+"/wave16k/", ori_data_path+data_type+f"/wav2vec2_{target_fps}/", target_fps)