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Running
on
Zero
File size: 1,629 Bytes
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from transformers import HubertModel
import torch.nn as nn
import torch
import torch.nn.functional as F
import torchaudio
import librosa
class HubertModelWithFinalProj(HubertModel):
def __init__(self, config):
super().__init__(config)
# The final projection layer is only used for backward compatibility.
# Following https://github.com/auspicious3000/contentvec/issues/6
# Remove this layer is necessary to achieve the desired outcome.
self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size)
class VoiceConversionExtractor(nn.Module):
# training on the fly might be slow
def __init__(self, config, sr):
super().__init__()
self.encoder = HubertModelWithFinalProj.from_pretrained(config)
self.encoder.eval()
self.sr = sr
self.target_sr = 16000
if self.sr != self.target_sr:
self.resampler = torchaudio.transforms.Resample(orig_freq=self.sr,
new_freq=self.target_sr)
def forward(self, audio):
if self.sr != self.target_sr:
audio = self.resampler(audio)
audio = F.pad(audio, ((400 - 320) // 2, (400 - 320) // 2))
logits = self.encoder(audio)['last_hidden_state']
return logits
if __name__ == '__main__':
model = VoiceConversionExtractor('lengyue233/content-vec-best', 24000)
audio, sr = librosa.load('test.wav', sr=24000)
audio = audio[:round(100*320*1.5)]
audio = torch.tensor([audio])
with torch.no_grad():
content = model(audio)
print(content.shape) |