import os, random, copy import numpy as np import torch import pandas as pd import torchaudio from tqdm.notebook import tqdm import collections, json import re, sys import os, copy from pathlib import Path from typing import Optional import whisper DEVICE = "cuda" if torch.cuda.is_available() else "cpu" model = whisper.load_model('large-v2') model.eval() data = torch.load('./train_chime4.pt') data_with_speech = [] for item in data: with torch.no_grad(): ### TO FILL BY USERS: # use utterance id (item['id']) to retrieve parallel audio paths: clean_audio_path, noisy_audio_path ### extract clean audio feats clean_audio = whisper.load_audio(clean_audio_path) clean_audio = whisper.pad_or_trim(clean_audio) clean_mel = whisper.log_mel_spectrogram(clean_audio).to(model.device) clean_audio_features = model.encoder(clean_mel.unsqueeze(0))[0] # noisy audio feats noisy_audio = whisper.load_audio(noisy_audio_path) noisy_audio = whisper.pad_or_trim(noisy_audio) noisy_mel = whisper.log_mel_spectrogram(noisy_audio).to(model.device) noisy_audio_features = model.encoder(noisy_mel.unsqueeze(0))[0] item_with_speech = {**item, 'audio_features': noisy_audio_features, 'clean_audio_features': clean_audio_features} data_with_speech.append(item_with_speech) torch.save(data_with_speech, './train_chime4_with_speech.pt')