File size: 2,157 Bytes
138fdcc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import time
import librosa
import torch
import torch.nn.functional as F
import soundfile as sf
import logging
logging.getLogger("numba").setLevel(logging.WARNING)
from transformers import (
Wav2Vec2FeatureExtractor,
HubertModel,
)
from torchaudio.transforms import Resample
import utils
import torch.nn as nn
class CNHubert(nn.Module):
def __init__(self, input_sample_rate=32000):
super().__init__()
self.model = HubertModel.from_pretrained("TencentGameMate/chinese-hubert-base")
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("TencentGameMate/chinese-hubert-base")
if input_sample_rate != 16000:
self.resample = Resample(orig_freq=input_sample_rate, new_freq=16000)
else:
self.resample = None
self.input_sample_rate = input_sample_rate
self.log_flag = False
def forward(self, x):
if self.resample is not None:
if not self.log_flag:
print(f"Resampling from {x.shape[-1]} to 16000")
self.log_flag = True
x = self.resample(x)
input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device).to(x.dtype)
feats = self.model(input_values)["last_hidden_state"]
return feats
def get_model(input_sample_rate=32000):
model = CNHubert(input_sample_rate=input_sample_rate)
model.eval()
return model
def get_content(hmodel, src_path):
input_sample_rate = hmodel.input_sample_rate
wav, _ = librosa.load(src_path, sr=input_sample_rate)
device = hmodel.parameters().__next__().device
dtype = hmodel.parameters().__next__().dtype
wav_16k_tensor = torch.from_numpy(wav).to(device).to(dtype)
with torch.no_grad():
feats = hmodel(wav_16k_tensor)
return feats.transpose(1,2)
if __name__ == '__main__':
model = get_model()
src_path = "/Users/Shared/原音频2.wav"
wav_16k_tensor = utils.load_wav_to_torch_and_resample(src_path, 16000)
model = model
wav_16k_tensor = wav_16k_tensor
feats = get_content(model,wav_16k_tensor)
print(feats.shape)
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