A PyTorch implementation of HiFi-GAN vocoder for music.
import torch
import os
import torchaudio
import torchaudio.transforms as T
class AudioPipeline(torch.nn.Module):
def __init__(
self,
freq=16000,
n_fft=1024,
n_mel=128,
win_length=1024,
hop_length=256,
):
super().__init__()
self.freq=freq
pad = int((n_fft-hop_length)/2)
self.spec = T.Spectrogram(n_fft=n_fft, win_length=win_length, hop_length=hop_length,
pad=pad, power=None,center=False, pad_mode='reflect', normalized=False, onesided=True)
self.mel_scale = T.MelScale(n_mels=n_mel, sample_rate=freq, n_stft=n_fft // 2 + 1)
def forward(self, waveform: torch.Tensor) -> torch.Tensor:
shift_waveform = waveform
# Convert to power spectrogram
spec = self.spec(shift_waveform)
spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
# Convert to mel-scale
mel = self.mel_scale(spec)
return mel
device = "cpu"
hifigan = torch.hub.load("vtuber-plan/hifi-gan:v0.3.0", "hifigan_48k", force_reload=True).to(device)
# Load audio
wav, sr = torchaudio.load("test.wav")
assert sr == 48000
audio_pipeline = AudioPipeline(freq=48000,
n_fft=2048,
n_mel=128,
win_length=2048,
hop_length=512)
mel = audio_pipeline(wav)
out = hifigan(mel)
wav_out = out.squeeze(0).cpu()
torchaudio.save("test_out.wav", wav_out, sr)| Name | Dataset | Fine-tuned |
|---|---|---|
| Hifi-GAN-48k | Universal | No |
| Hifi-GAN-44.1k | Universal | No |
| Hifi-GAN-36k | Universal | No |
| Hifi-GAN-24k | Universal | No |
| Hifi-GAN-22.05k | Universal | No |
| Hifi-GAN-16k | Universal | No |
Training Datasets: VCTK, JSUT and RAVDESS
Place all audio files under the dataset folder. Then run these commands:
python filelist.py
python split.py
python train.pyMIT License
Copyright (c) 2022 Vtuber Plan
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Hifi-GAN models from jik876.