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import soundfile as sf
import torch
import torch.multiprocessing
import torch.multiprocessing
from numpy import trim_zeros
from speechbrain.pretrained import EncoderClassifier
from Preprocessing.AudioPreprocessor import AudioPreprocessor
class ProsodicConditionExtractor:
def __init__(self, sr, device=torch.device("cpu")):
self.ap = AudioPreprocessor(input_sr=sr, output_sr=16000, melspec_buckets=80, hop_length=256, n_fft=1024, cut_silence=False)
# https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb
self.speaker_embedding_func_ecapa = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb",
run_opts={"device": str(device)},
savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_ecapa")
# https://huggingface.co/speechbrain/spkrec-xvect-voxceleb
self.speaker_embedding_func_xvector = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb",
run_opts={"device": str(device)},
savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_xvector")
def extract_condition_from_reference_wave(self, wave, already_normalized=False):
if already_normalized:
norm_wave = wave
else:
norm_wave = self.ap.audio_to_wave_tensor(normalize=True, audio=wave)
norm_wave = torch.tensor(trim_zeros(norm_wave.numpy()))
spk_emb_ecapa = self.speaker_embedding_func_ecapa.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze()
spk_emb_xvector = self.speaker_embedding_func_xvector.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze()
combined_utt_condition = torch.cat([spk_emb_ecapa.cpu(),
spk_emb_xvector.cpu()], dim=0)
return combined_utt_condition
if __name__ == '__main__':
wave, sr = sf.read("../audios/1.wav")
ext = ProsodicConditionExtractor(sr=sr)
print(ext.extract_condition_from_reference_wave(wave=wave).shape)
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