Upload add_speech_feats_to_train_data.py

add_speech_feats_to_train_data.py

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+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')
+