forked from mrq/tortoise-tts
143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
import os
|
|
from glob import glob
|
|
|
|
import torch
|
|
import torchaudio
|
|
import numpy as np
|
|
from scipy.io.wavfile import read
|
|
|
|
from utils.stft import STFT
|
|
|
|
|
|
def load_wav_to_torch(full_path):
|
|
sampling_rate, data = read(full_path)
|
|
if data.dtype == np.int32:
|
|
norm_fix = 2 ** 31
|
|
elif data.dtype == np.int16:
|
|
norm_fix = 2 ** 15
|
|
elif data.dtype == np.float16 or data.dtype == np.float32:
|
|
norm_fix = 1.
|
|
else:
|
|
raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
|
|
return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)
|
|
|
|
|
|
def load_audio(audiopath, sampling_rate):
|
|
if audiopath[-4:] == '.wav':
|
|
audio, lsr = load_wav_to_torch(audiopath)
|
|
elif audiopath[-4:] == '.mp3':
|
|
# https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it.
|
|
from pyfastmp3decoder.mp3decoder import load_mp3
|
|
audio, lsr = load_mp3(audiopath, sampling_rate)
|
|
audio = torch.FloatTensor(audio)
|
|
|
|
# Remove any channel data.
|
|
if len(audio.shape) > 1:
|
|
if audio.shape[0] < 5:
|
|
audio = audio[0]
|
|
else:
|
|
assert audio.shape[1] < 5
|
|
audio = audio[:, 0]
|
|
|
|
if lsr != sampling_rate:
|
|
audio = torchaudio.functional.resample(audio, lsr, sampling_rate)
|
|
|
|
# Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
|
|
# '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
|
|
if torch.any(audio > 2) or not torch.any(audio < 0):
|
|
print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
|
|
audio.clip_(-1, 1)
|
|
|
|
return audio.unsqueeze(0)
|
|
|
|
|
|
TACOTRON_MEL_MAX = 2.3143386840820312
|
|
TACOTRON_MEL_MIN = -11.512925148010254
|
|
|
|
|
|
def denormalize_tacotron_mel(norm_mel):
|
|
return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN
|
|
|
|
|
|
def normalize_tacotron_mel(mel):
|
|
return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1
|
|
|
|
|
|
def dynamic_range_compression(x, C=1, clip_val=1e-5):
|
|
"""
|
|
PARAMS
|
|
------
|
|
C: compression factor
|
|
"""
|
|
return torch.log(torch.clamp(x, min=clip_val) * C)
|
|
|
|
|
|
def dynamic_range_decompression(x, C=1):
|
|
"""
|
|
PARAMS
|
|
------
|
|
C: compression factor used to compress
|
|
"""
|
|
return torch.exp(x) / C
|
|
|
|
|
|
def get_voices():
|
|
subs = os.listdir('voices')
|
|
voices = {}
|
|
for sub in subs:
|
|
subj = os.path.join('voices', sub)
|
|
if os.path.isdir(subj):
|
|
voices[sub] = glob(f'{subj}/*.wav')
|
|
return voices
|
|
|
|
|
|
class TacotronSTFT(torch.nn.Module):
|
|
def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
|
|
n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
|
|
mel_fmax=8000.0):
|
|
super(TacotronSTFT, self).__init__()
|
|
self.n_mel_channels = n_mel_channels
|
|
self.sampling_rate = sampling_rate
|
|
self.stft_fn = STFT(filter_length, hop_length, win_length)
|
|
from librosa.filters import mel as librosa_mel_fn
|
|
mel_basis = librosa_mel_fn(
|
|
sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
|
|
mel_basis = torch.from_numpy(mel_basis).float()
|
|
self.register_buffer('mel_basis', mel_basis)
|
|
|
|
def spectral_normalize(self, magnitudes):
|
|
output = dynamic_range_compression(magnitudes)
|
|
return output
|
|
|
|
def spectral_de_normalize(self, magnitudes):
|
|
output = dynamic_range_decompression(magnitudes)
|
|
return output
|
|
|
|
def mel_spectrogram(self, y):
|
|
"""Computes mel-spectrograms from a batch of waves
|
|
PARAMS
|
|
------
|
|
y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
|
|
|
|
RETURNS
|
|
-------
|
|
mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
|
|
"""
|
|
assert(torch.min(y.data) >= -10)
|
|
assert(torch.max(y.data) <= 10)
|
|
y = torch.clip(y, min=-1, max=1)
|
|
|
|
magnitudes, phases = self.stft_fn.transform(y)
|
|
magnitudes = magnitudes.data
|
|
mel_output = torch.matmul(self.mel_basis, magnitudes)
|
|
mel_output = self.spectral_normalize(mel_output)
|
|
return mel_output
|
|
|
|
|
|
def wav_to_univnet_mel(wav, do_normalization=False):
|
|
stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000)
|
|
stft = stft.cuda()
|
|
mel = stft.mel_spectrogram(wav)
|
|
if do_normalization:
|
|
mel = normalize_tacotron_mel(mel)
|
|
return mel |