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import pandas as pd |
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import soundfile as sf |
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import pdb |
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from pydub import AudioSegment |
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from transformers import AutoTokenizer, Wav2Vec2ForCTC |
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import torch |
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import numpy as np |
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import glob |
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import numpy |
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import os.path |
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processor = AutoTokenizer.from_pretrained("facebook/wav2vec2-large-960h-lv60") |
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60") |
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def convert_mp3_to_wav(audio_file): |
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sound = AudioSegment.from_mp3(audio_file) |
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sound.export(audio_file + ".wav", format="wav") |
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def feature_extractor(path): |
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print("the path is", path) |
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wav_files = glob.glob(path + "/*.wav") |
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for wav_file in wav_files: |
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print("the wavfile is", wav_files) |
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if not os.path.isfile(wav_file + ".wav2vec2.pt"): |
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get_wav2vecembeddings_from_audiofile(wav_file) |
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def get_wav2vecembeddings_from_audiofile(wav_file): |
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print("the file is", wav_file) |
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speech, sample_rate = sf.read(wav_file) |
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input_values = processor(wav_file, return_tensors="pt", padding=True) |
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print("input values", input_values) |
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file_info = os.stat(wav_file) |
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file_size = file_info.st_size |
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print("the size is", file_size) |
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if file_size > 250: |
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with torch.no_grad(): |
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encoded_states = model( |
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input_values=input_values["input_ids"], |
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attention_mask=input_values["attention_mask"], |
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output_hidden_states=True |
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) |
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last_hidden_state = encoded_states.hidden_states[-1] |
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print("getting wav2vec2 embeddings") |
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print(last_hidden_state) |
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torch.save(last_hidden_state, wav_file + '.wav2vec2.pt') |
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feature_extractor("/home/bmoell/data/media.talkbank.org/dementia/English/Pitt/Control/cookie") |
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