import os
from glob import glob
import librosa
import soundfile as sf
import torch
import torchaudio
import numpy as np
from scipy.io.wavfile import read
from tortoise.utils.stft import STFT
def get_voice_dir():
target = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../voices')
if not os.path.exists(target):
target = os.path.dirname('./voices/')
os.makedirs(target, exist_ok=True)
return target
def load_audio(audiopath, sampling_rate):
if audiopath[-4:] == '.wav':
audio, lsr = torchaudio.load(audiopath)
elif audiopath[-4:] == '.mp3':
audio, lsr = librosa.load(audiopath, sr=sampling_rate)
audio = torch.FloatTensor(audio)
elif audiopath[-5:] == '.flac':
audio, lsr = sf.read(audiopath)
audio = torch.FloatTensor(audio)
else:
assert False, f"Unsupported audio format provided: {audiopath[-4:]}"
# 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(extra_voice_dirs=[], load_latents=True):
dirs = [get_voice_dir()] + extra_voice_dirs
voices = {}
for d in dirs:
subs = os.listdir(d)
for sub in subs:
subj = os.path.join(d, sub)
if os.path.isdir(subj):
voices[sub] = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3')) + list(glob(f'{subj}/*.flac'))
if load_latents:
voices[sub] = voices[sub] + list(glob(f'{subj}/*.pth'))
return voices
def load_voice(voice, extra_voice_dirs=[], load_latents=True, sample_rate=22050, device='cpu', model_hash=None):
if voice == 'random':
return None, None
voices = get_voices(extra_voice_dirs=extra_voice_dirs, load_latents=load_latents)
paths = voices[voice]
mtime = 0
latent = None
voices = []
for path in paths:
filename = os.path.basename(path)
if filename[-4:] == ".pth" and filename[:12] == "cond_latents":
if not model_hash and filename == "cond_latents.pth":
latent = path
elif model_hash and filename == f"cond_latents_{model_hash[:8]}.pth":
latent = path
else:
voices.append(path)
mtime = max(mtime, os.path.getmtime(path))
if load_latents and latent is not None:
if os.path.getmtime(latent) > mtime:
print(f"Reading from latent: {latent}")
return None, torch.load(latent, map_location=device)
print(f"Latent file out of date: {latent}")
samples = []
for path in voices:
c = load_audio(path, sample_rate)
samples.append(c)
return samples, None
def load_voices(voices, extra_voice_dirs=[]):
latents = []
clips = []
for voice in voices:
if voice == 'random':
if len(voices) > 1:
print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
return None, None
clip, latent = load_voice(voice, extra_voice_dirs)
if latent is None:
assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
clips.extend(clip)
elif clip is None:
assert len(clips) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
latents.append(latent)
if len(latents) == 0:
return clips, None
else:
latents_0 = torch.stack([l[0] for l in latents], dim=0).mean(dim=0)
latents_1 = torch.stack([l[1] for l in latents], dim=0).mean(dim=0)
latents = (latents_0,latents_1)
return None, latents
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(
sr=sampling_rate, n_fft=filter_length, n_mels=n_mel_channels, fmin=mel_fmin, fmax=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, device='cpu', sample_rate=24000):
stft = TacotronSTFT(1024, 256, 1024, 100, sample_rate, 0, 12000)
stft = stft.to(device)
mel = stft.mel_spectrogram(wav)
if do_normalization:
mel = normalize_tacotron_mel(mel)
return mel