另外,torch 提供了许多实用程序,可用于有效地序列化张量和任意类型(arbitrary types),以及其他有用的实用程序(utilities). Torch 有CUDA版本,使用者可以在计算能力大于等于3.0的NVIDIA GPU上运行张量计算 Tensor操作 is_tensor(),is_storage(),is_complex(),is_floating_point(),is_nonzero(),set_default_dtype()...
torch.Tensor是一个包含单一数据类型元素的多维矩阵。Torch定义了9种CPU张量类型和9种GPU张量类型:Data type dtype CPU tensor GPU tensor 32-bit floating point torch.float32 or torch.float torch.FloatTensor torch.cuda.FloatTensor 64-bit floating point torch.float64 or torch.double torch.Double...
Tacotron2是一个端到端的语音合成神经网络结构,它由两部分组成,一部分由循环神经网络组成,应用Attention机制,自回归地产生mel谱序列,另一部分是修改后的Wavenet,将mel谱序列映射成音频。在Tacotron2中,首先使用50毫秒帧长,12.5毫秒帧移,汉宁窗截取,然后施加短时傅里叶变换(STFT)得出线性频谱。接着,使用频率范围在12...
🐛 Describe the bug When a lot of GPU memory is already allocated/reserved, torch.stft can sometimes raise the exception: RuntimeError: cuFFT error: CUFFT_INTERNAL_ERROR It's not necessarily the first call to torch.stft. I had training ru...
stft = np.abs(librosa.stft(data)) chroma = np.mean(librosa.feature.chroma_stft(S = stft, sr = sr).T, axis = 0) mel = np.mean(librosa.feature.melspectrogram(data, sr).T, axis = 0) contrast = np.mean(librosa.feature.spectral_contrast(S = stft, sr = sr).T, axis = 0) ...
torch.Tensor是一个包含单一数据类型元素的多维矩阵。Torch定义了9种CPU张量类型和9种GPU张量类型: Data type dtype CPU tensor GPU tensor 32-bit floating point torch.float32 or torch.float torch.FloatTensor torch.cuda.FloatTensor 64-bit flo...torch...
phase_vocoder(给定一个 STFT 张量,通过一个速度因子在时间上加速而不改变音高) pitch_shift(将波形的音高调整 n_steps 步) compute_deltas(计算张量(通常为频谱图)的增量系数) detect_pitch_frequency(检测音高频率) sliding_window_cmn(对每个utterance应用滑动窗口倒谱均值(和可选的方差)归一化) ...
torch.Tensor,torch.Tensor是一个包含单一数据类型元素的多维矩阵。Torch定义了9种CPU张量类型和9种GPU张量类型:DatatypedtypeCPUtensorGPUtensor32-bitfloatingpointtorch.float32ortorc...
Torch定义了9种CPU张量类型和9种GPU张量类型: Data type dtype CPU tensor GPU tensor 32-bit floating point torch.float32 or torch.float torch.FloatTensor torch.cuda.FloatTensor 64-bit floating point torch.float64 or torch.double torch.DoubleTensor torch.cuda.DoubleTensor 16-bit floating point torch...
Output of compare_stft.py: Tests Test it by just cloning this repo and running pip install -r requirements.txt python -m pytest . Unfortunately, since it's implemented with 1D Convolutions, some filter_length/hop_length combinations can result in out of memory errors on your GPU when run...