added BigVGAN and HiFiGAN (from https://git.ecker.tech/jarod/tortoise-tts), vocoder selectable in webUI

data/config.yaml

@@ -1,3 +1,5 @@
+vocoder: "hifigan"
+
 models:
 - name: "autoregressive"
   training: True
@@ -71,7 +73,7 @@ trainer:
 
   backend: deepspeed
   deepspeed:
-    inferencing: True
+    inferencing: False
     zero_optimization_level: 0
     use_compression_training: False
 

tortoise_tts/__main__.py

@@ -24,6 +24,7 @@ def main():
 	
 	parser.add_argument("--diffusion-sampler", type=str, default="ddim")
 	parser.add_argument("--cond-free", action="store_true")
+	parser.add_argument("--vocoder", type=str, default="bigvgan")
 	
 	parser.add_argument("--yaml", type=Path, default=None)
 	parser.add_argument("--device", type=str, default=None)
@@ -62,6 +63,8 @@ def main():
 
 		diffusion_sampler=args.diffusion_sampler,
 		cond_free=args.cond_free,
+		
+		vocoder_type=args.vocoder,
 	)
 	"""
 		language=args.language,

tortoise_tts/config.py

@@ -526,6 +526,8 @@ class Config(BaseConfig):
 	sample_rate: int = 24_000
 	audio_backend: str = "mel"
 
+	vocoder: str = "bigvgan" # "vocoder" | "bigvgan" | "hifigan"
+
 	@property
 	def model(self):
 		for i, model in enumerate(self.models):

tortoise_tts/inference.py

@@ -5,6 +5,7 @@ import soundfile
 from torch import Tensor
 from einops import rearrange
 from pathlib import Path
+from tqdm import tqdm
 
 from .emb.mel import encode_from_files as encode_mel, trim, trim_random
 from .utils import to_device
@@ -96,6 +97,21 @@ class TTS():
 		# merge inputs
 		return encode_mel( paths, device=self.device )
 
+	# taken from here https://github.com/coqui-ai/TTS/blob/d21f15cc850788f9cdf93dac0321395138665287/TTS/tts/models/xtts.py#L666
+	def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
+		"""Handle chunk formatting in streaming mode"""
+		wav_chunk = wav_gen[:-overlap_len]
+		if wav_gen_prev is not None:
+			wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len) : -overlap_len]
+		if wav_overlap is not None:
+			crossfade_wav = wav_chunk[:overlap_len]
+			crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_len).to(crossfade_wav.device)
+			wav_chunk[:overlap_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_len).to(wav_overlap.device)
+			wav_chunk[:overlap_len] += crossfade_wav
+		wav_overlap = wav_gen[-overlap_len:]
+		wav_gen_prev = wav_gen
+		return wav_chunk, wav_gen_prev, wav_overlap
+
 	@torch.inference_mode()
 	def inference(
 		self,
@@ -122,7 +138,9 @@ class TTS():
 		diffusion_sampler="ddim",
 		cond_free=True,
 
-		out_path=None
+		vocoder_type="bigvgan",
+
+		out_path=None,
 	):
 		lines = text.split("\n")
 
@@ -142,7 +160,7 @@ class TTS():
 				diffusion = engine.module
 			elif "clvp" in name:
 				clvp = engine.module
-			elif "vocoder" in name:
+			elif vocoder_type in name:
 				vocoder = engine.module
 
 		if autoregressive is None:
@@ -152,7 +170,7 @@ class TTS():
 		if clvp is None:
 			clvp = load_model("clvp", device=cfg.device)
 		if vocoder is None:
-			vocoder = load_model("vocoder", device=cfg.device)
+			vocoder = load_model(vocoder_type, device=cfg.device)
 		
 		autoregressive = autoregressive.to(cfg.device)
 		diffusion = diffusion.to(cfg.device)
@@ -183,6 +201,88 @@ class TTS():
 			text_tokens = pad_sequence([ text ], batch_first = True)
 			text_lengths = torch.Tensor([ text.shape[0] ]).to(dtype=torch.int32)
 
+			# streaming interface spits out the final hidden state, which HiFiGAN seems to be trained against
+			if vocoder_type == "hifigan":
+				waves = []
+				all_latents = []
+				all_codes = []
+
+				wav_gen_prev = None
+				wav_overlap = None
+				is_end = False
+				first_buffer = 60
+
+				stream_chunk_size = 40
+				overlap_wav_len = 1024
+
+				with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
+					with ml.auto_unload(autoregressive, enabled=cfg.inference.auto_unload):
+						with ml.auto_unload(vocoder, enabled=cfg.inference.auto_unload):
+							inputs = autoregressive.compute_embeddings( autoregressive_latents, text_tokens )
+
+							gpt_generator = autoregressive.get_generator(
+								inputs=inputs,
+								top_k=top_k,
+								top_p=top_p,
+								temperature=ar_temp,
+								do_sample=True,
+								num_beams=max(1, beam_width),
+								num_return_sequences=1,
+								length_penalty=length_penalty,
+								repetition_penalty=repetition_penalty,
+								output_attentions=False,
+								output_hidden_states=True,
+							)
+
+							bar = tqdm( unit="it", total=500 )
+							while not is_end:
+								try:
+									codes, latent = next(gpt_generator)
+									all_latents += [latent]
+									all_codes += [codes]
+								except StopIteration:
+									is_end = True
+
+								if is_end or (stream_chunk_size > 0 and len(all_codes) >= max(stream_chunk_size, first_buffer)):
+									first_buffer = 0
+									all_codes = []
+									bar.update( stream_chunk_size )
+
+									latents = torch.cat(all_latents, dim=0)[None, :].to(cfg.device)
+									wav_gen = vocoder.inference(latents, autoregressive_latents)
+									wav_gen = wav_gen.squeeze()
+
+									wav_chunk = wav_gen[:-overlap_wav_len]
+									if wav_gen_prev is not None:
+										wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_wav_len) : -overlap_wav_len]
+									if wav_overlap is not None:
+										crossfade_wav = wav_chunk[:overlap_wav_len]
+										crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_wav_len).to(crossfade_wav.device)
+										wav_chunk[:overlap_wav_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_wav_len).to(wav_overlap.device)
+										wav_chunk[:overlap_wav_len] += crossfade_wav
+									
+									wav_overlap = wav_gen[-overlap_wav_len:]
+									wav_gen_prev = wav_gen
+									
+
+									# yielding requires to do a bunch of pain to work around it turning into an async function
+									"""
+									yield wav_chunk
+									"""
+
+									waves.append( wav_chunk.unsqueeze(0) )
+
+							bar.close()
+
+				wav = torch.concat(waves, dim=-1)
+
+				if out_path is not None:
+					torchaudio.save( out_path, wav.cpu(), sr )
+
+				wavs.append(wav)
+
+				continue
+
 			with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
 				with ml.auto_unload(autoregressive, enabled=cfg.inference.auto_unload):
 					# autoregressive pass
@@ -190,9 +290,11 @@ class TTS():
 						autoregressive_latents,
 						text_tokens,
 						do_sample=True,
+						top_k=top_k,
 						top_p=top_p,
 						temperature=ar_temp,
 						num_return_sequences=candidates,
+						num_beams=max(1,beam_width),
 						length_penalty=length_penalty,
 						repetition_penalty=repetition_penalty,
 						max_generate_length=max_ar_steps,

tortoise_tts/models/__init__.py

@@ -1,5 +1,4 @@
-# https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models
+# All other ccode in this folder are licensed per the attributions at the top
-# All code under this folder is licensed as Apache License 2.0 per the original repo
 
 from functools import cache
 
@@ -8,6 +7,8 @@ from .arch_utils import TorchMelSpectrogram, TacotronSTFT
 from .unified_voice import UnifiedVoice
 from .diffusion import DiffusionTTS
 from .vocoder import UnivNetGenerator
+from .bigvgan import BigVGAN
+from .hifigan import HifiganGenerator
 from .clvp import CLVP
 from .dvae import DiscreteVAE
 from .random_latent_generator import RandomLatentConverter
@@ -15,6 +16,8 @@ from .random_latent_generator import RandomLatentConverter
 import os
 import torch
 from pathlib import Path
+import requests
+from tqdm import tqdm
 
 DEFAULT_MODEL_PATH = Path(__file__).parent.parent.parent / 'data/models'
 DEFAULT_MODEL_URLS = {
@@ -28,10 +31,18 @@ DEFAULT_MODEL_URLS = {
     'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth',
     'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth',
     'mel_norms.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/data/mel_norms.pth',
+
+    # BigVGAN
+    'bigvgan_base_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_base_24khz_100band.pth',
+    'bigvgan_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_24khz_100band.pth',
+
+    'bigvgan_base_24khz_100band.json': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_base_24khz_100band.json',
+    'bigvgan_24khz_100band.json': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_24khz_100band.json',
+
+    # HiFiGAN
+    'hifigan.pth': 'https://huggingface.co/Manmay/tortoise-tts/resolve/main/hifidecoder.pth',
 }
 
-import requests
-from tqdm import tqdm
 
 # kludge, probably better to use HF's model downloader function
 # to-do: write to a temp file then copy so downloads can be interrupted
@@ -75,6 +86,7 @@ def download_model( save_path, chunkSize = 1024, unit = "MiB" ):
 @cache
 def load_model(name, device="cuda", **kwargs):
 	load_path = None
+	config_path = None
 	state_dict_key = None
 	strict = True
 
@@ -95,6 +107,31 @@ def load_model(name, device="cuda", **kwargs):
 	elif "clvp" in name:
 		model = CLVP(**kwargs)
 		load_path = DEFAULT_MODEL_PATH / 'clvp2.pth'
+	elif "bigvgan" in name:
+		# download any JSONs (BigVGAN)
+		load_path = DEFAULT_MODEL_PATH / 'bigvgan_24khz_100band.pth'
+		config_path = load_path.with_suffix(".json")
+		if config_path.name in DEFAULT_MODEL_URLS:
+			if not config_path.exists():
+				download_model( config_path )
+		else:
+			config_path = None
+
+		model = BigVGAN(config=config_path, **kwargs)
+		state_dict_key = 'generator'
+	elif "hifigan" in name:
+		model = HifiganGenerator(
+			in_channels=1024,
+			out_channels = 1,
+			resblock_type = "1",
+			resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+			resblock_kernel_sizes = [3, 7, 11],
+			upsample_kernel_sizes = [16, 16, 4, 4],
+			upsample_initial_channel = 512,
+			upsample_factors = [8, 8, 2, 2],
+			cond_channels=1024
+		)
+		load_path = DEFAULT_MODEL_PATH / 'hifigan.pth'
 	elif "vocoder" in name:
 		model = UnivNetGenerator(**kwargs)
 		load_path = DEFAULT_MODEL_PATH / 'vocoder.pth'
@@ -126,6 +163,11 @@ def load_model(name, device="cuda", **kwargs):
 
 	model.eval()
 
+	try:
+		print(f"{name} ({next(model.parameters()).dtype}): {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
+	except Exception as e:
+		print(f"{name}: {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
+
 	return model
 
 def unload_model():
@@ -138,8 +180,6 @@ def get_model(config, training=True):
 	config.training = "autoregressive" in config.name
 	model.config = config
 
-	print(f"{name} ({next(model.parameters()).dtype}): {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
-
 	return model
 
 def get_models(models, training=True):

tortoise_tts/models/arch_utils.py

@@ -1,3 +1,5 @@
+# Adapted from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/arch_utils.py
+
 import os
 import functools
 import math

tortoise_tts/models/bigvgan.py

@@ -0,0 +1,772 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import math
+
+import json
+import os
+import torch.utils.data
+
+from torch import nn, sin, pow
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d, Parameter
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from librosa.filters import mel as librosa_mel_fn
+
+
+# filter.py
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+if 'sinc' in dir(torch):
+	sinc = torch.sinc
+else:
+	# This code is adopted from adefossez's julius.core.sinc under the MIT License
+	# https://adefossez.github.io/julius/julius/core.html
+	#   LICENSE is in incl_licenses directory.
+	def sinc(x: torch.Tensor):
+		"""
+		Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+		__Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+		"""
+		return torch.where(x == 0,
+						   torch.tensor(1., device=x.device, dtype=x.dtype),
+						   torch.sin(math.pi * x) / math.pi / x)
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+#   LICENSE is in incl_licenses directory.
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
+	even = (kernel_size % 2 == 0)
+	half_size = kernel_size // 2
+
+	#For kaiser window
+	delta_f = 4 * half_width
+	A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+	if A > 50.:
+		beta = 0.1102 * (A - 8.7)
+	elif A >= 21.:
+		beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
+	else:
+		beta = 0.
+	window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+	# ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+	if even:
+		time = (torch.arange(-half_size, half_size) + 0.5)
+	else:
+		time = torch.arange(kernel_size) - half_size
+	if cutoff == 0:
+		filter_ = torch.zeros_like(time)
+	else:
+		filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+		# Normalize filter to have sum = 1, otherwise we will have a small leakage
+		# of the constant component in the input signal.
+		filter_ /= filter_.sum()
+		filter = filter_.view(1, 1, kernel_size)
+
+	return filter
+
+
+class LowPassFilter1d(nn.Module):
+	def __init__(self,
+				 cutoff=0.5,
+				 half_width=0.6,
+				 stride: int = 1,
+				 padding: bool = True,
+				 padding_mode: str = 'replicate',
+				 kernel_size: int = 12):
+		# kernel_size should be even number for stylegan3 setup,
+		# in this implementation, odd number is also possible.
+		super().__init__()
+		if cutoff < -0.:
+			raise ValueError("Minimum cutoff must be larger than zero.")
+		if cutoff > 0.5:
+			raise ValueError("A cutoff above 0.5 does not make sense.")
+		self.kernel_size = kernel_size
+		self.even = (kernel_size % 2 == 0)
+		self.pad_left = kernel_size // 2 - int(self.even)
+		self.pad_right = kernel_size // 2
+		self.stride = stride
+		self.padding = padding
+		self.padding_mode = padding_mode
+		filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+		self.register_buffer("filter", filter)
+
+	#input [B, C, T]
+	def forward(self, x):
+		_, C, _ = x.shape
+
+		if self.padding:
+			x = F.pad(x, (self.pad_left, self.pad_right),
+					  mode=self.padding_mode)
+		out = F.conv1d(x, self.filter.expand(C, -1, -1),
+					   stride=self.stride, groups=C)
+
+		return out
+
+# resample.py
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+class UpSample1d(nn.Module):
+	def __init__(self, ratio=2, kernel_size=None):
+		super().__init__()
+		self.ratio = ratio
+		self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+		self.stride = ratio
+		self.pad = self.kernel_size // ratio - 1
+		self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+		self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+		filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
+									  half_width=0.6 / ratio,
+									  kernel_size=self.kernel_size)
+		self.register_buffer("filter", filter)
+
+	# x: [B, C, T]
+	def forward(self, x):
+		_, C, _ = x.shape
+
+		x = F.pad(x, (self.pad, self.pad), mode='replicate')
+		x = self.ratio * F.conv_transpose1d(
+			x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+		x = x[..., self.pad_left:-self.pad_right]
+
+		return x
+
+
+class DownSample1d(nn.Module):
+	def __init__(self, ratio=2, kernel_size=None):
+		super().__init__()
+		self.ratio = ratio
+		self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+		self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
+									   half_width=0.6 / ratio,
+									   stride=ratio,
+									   kernel_size=self.kernel_size)
+
+	def forward(self, x):
+		xx = self.lowpass(x)
+
+		return xx
+
+# act.py
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+class Activation1d(nn.Module):
+	def __init__(self,
+				 activation,
+				 up_ratio: int = 2,
+				 down_ratio: int = 2,
+				 up_kernel_size: int = 12,
+				 down_kernel_size: int = 12):
+		super().__init__()
+		self.up_ratio = up_ratio
+		self.down_ratio = down_ratio
+		self.act = activation
+		self.upsample = UpSample1d(up_ratio, up_kernel_size)
+		self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+	# x: [B,C,T]
+	def forward(self, x):
+		x = self.upsample(x)
+		x = self.act(x)
+		x = self.downsample(x)
+
+		return x
+
+# activations.py
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+class Snake(nn.Module):
+	'''
+	Implementation of a sine-based periodic activation function
+	Shape:
+		- Input: (B, C, T)
+		- Output: (B, C, T), same shape as the input
+	Parameters:
+		- alpha - trainable parameter
+	References:
+		- This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+		https://arxiv.org/abs/2006.08195
+	Examples:
+		>>> a1 = snake(256)
+		>>> x = torch.randn(256)
+		>>> x = a1(x)
+	'''
+	def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+		'''
+		Initialization.
+		INPUT:
+			- in_features: shape of the input
+			- alpha: trainable parameter
+			alpha is initialized to 1 by default, higher values = higher-frequency.
+			alpha will be trained along with the rest of your model.
+		'''
+		super(Snake, self).__init__()
+		self.in_features = in_features
+
+		# initialize alpha
+		self.alpha_logscale = alpha_logscale
+		if self.alpha_logscale: # log scale alphas initialized to zeros
+			self.alpha = Parameter(torch.zeros(in_features) * alpha)
+		else: # linear scale alphas initialized to ones
+			self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+		self.alpha.requires_grad = alpha_trainable
+
+		self.no_div_by_zero = 0.000000001
+
+	def forward(self, x):
+		'''
+		Forward pass of the function.
+		Applies the function to the input elementwise.
+		Snake ∶= x + 1/a * sin^2 (xa)
+		'''
+		alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+		if self.alpha_logscale:
+			alpha = torch.exp(alpha)
+		x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+		return x
+
+
+class SnakeBeta(nn.Module):
+	'''
+	A modified Snake function which uses separate parameters for the magnitude of the periodic components
+	Shape:
+		- Input: (B, C, T)
+		- Output: (B, C, T), same shape as the input
+	Parameters:
+		- alpha - trainable parameter that controls frequency
+		- beta - trainable parameter that controls magnitude
+	References:
+		- This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+		https://arxiv.org/abs/2006.08195
+	Examples:
+		>>> a1 = snakebeta(256)
+		>>> x = torch.randn(256)
+		>>> x = a1(x)
+	'''
+	def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+		'''
+		Initialization.
+		INPUT:
+			- in_features: shape of the input
+			- alpha - trainable parameter that controls frequency
+			- beta - trainable parameter that controls magnitude
+			alpha is initialized to 1 by default, higher values = higher-frequency.
+			beta is initialized to 1 by default, higher values = higher-magnitude.
+			alpha will be trained along with the rest of your model.
+		'''
+		super(SnakeBeta, self).__init__()
+		self.in_features = in_features
+
+		# initialize alpha
+		self.alpha_logscale = alpha_logscale
+		if self.alpha_logscale: # log scale alphas initialized to zeros
+			self.alpha = Parameter(torch.zeros(in_features) * alpha)
+			self.beta = Parameter(torch.zeros(in_features) * alpha)
+		else: # linear scale alphas initialized to ones
+			self.alpha = Parameter(torch.ones(in_features) * alpha)
+			self.beta = Parameter(torch.ones(in_features) * alpha)
+
+		self.alpha.requires_grad = alpha_trainable
+		self.beta.requires_grad = alpha_trainable
+
+		self.no_div_by_zero = 0.000000001
+
+	def forward(self, x):
+		'''
+		Forward pass of the function.
+		Applies the function to the input elementwise.
+		SnakeBeta ∶= x + 1/b * sin^2 (xa)
+		'''
+		alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+		beta = self.beta.unsqueeze(0).unsqueeze(-1)
+		if self.alpha_logscale:
+			alpha = torch.exp(alpha)
+			beta = torch.exp(beta)
+		x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+		return x
+
+# bigvgan.py
+# Copyright (c) 2022 NVIDIA CORPORATION. 
+#   Licensed under the MIT license.
+
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+LRELU_SLOPE = 0.1
+
+class AMPBlock1(torch.nn.Module):
+	def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
+		super(AMPBlock1, self).__init__()
+		self.h = h
+
+		self.convs1 = nn.ModuleList([
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+							   padding=get_padding(kernel_size, dilation[0]))),
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+							   padding=get_padding(kernel_size, dilation[1]))),
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+							   padding=get_padding(kernel_size, dilation[2])))
+		])
+		self.convs1.apply(init_weights)
+
+		self.convs2 = nn.ModuleList([
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+							   padding=get_padding(kernel_size, 1))),
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+							   padding=get_padding(kernel_size, 1))),
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+							   padding=get_padding(kernel_size, 1)))
+		])
+		self.convs2.apply(init_weights)
+
+		self.num_layers = len(self.convs1) + len(self.convs2)  # total number of conv layers
+
+		if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
+			self.activations = nn.ModuleList([
+				Activation1d(
+					activation=Snake(channels, alpha_logscale=h.snake_logscale))
+				for _ in range(self.num_layers)
+			])
+		elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
+			self.activations = nn.ModuleList([
+				Activation1d(
+					activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+				for _ in range(self.num_layers)
+			])
+		else:
+			raise NotImplementedError(
+				"activation incorrectly specified. check the config file and look for 'activation'.")
+
+	def forward(self, x):
+		acts1, acts2 = self.activations[::2], self.activations[1::2]
+		for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
+			xt = a1(x)
+			xt = c1(xt)
+			xt = a2(xt)
+			xt = c2(xt)
+			x = xt + x
+
+		return x
+
+	def remove_weight_norm(self):
+		for l in self.convs1:
+			remove_weight_norm(l)
+		for l in self.convs2:
+			remove_weight_norm(l)
+
+
+class AMPBlock2(torch.nn.Module):
+	def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
+		super(AMPBlock2, self).__init__()
+		self.h = h
+
+		self.convs = nn.ModuleList([
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+							   padding=get_padding(kernel_size, dilation[0]))),
+			weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+							   padding=get_padding(kernel_size, dilation[1])))
+		])
+		self.convs.apply(init_weights)
+
+		self.num_layers = len(self.convs)  # total number of conv layers
+
+		if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
+			self.activations = nn.ModuleList([
+				Activation1d(
+					activation=Snake(channels, alpha_logscale=h.snake_logscale))
+				for _ in range(self.num_layers)
+			])
+		elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
+			self.activations = nn.ModuleList([
+				Activation1d(
+					activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+				for _ in range(self.num_layers)
+			])
+		else:
+			raise NotImplementedError(
+				"activation incorrectly specified. check the config file and look for 'activation'.")
+
+	def forward(self, x):
+		for c, a in zip(self.convs, self.activations):
+			xt = a(x)
+			xt = c(xt)
+			x = xt + x
+
+		return x
+
+	def remove_weight_norm(self):
+		for l in self.convs:
+			remove_weight_norm(l)
+
+
+
+class AttrDict(dict):
+	def __init__(self, *args, **kwargs):
+		super(AttrDict, self).__init__(*args, **kwargs)
+		self.__dict__ = self
+
+class BigVGAN(nn.Module):
+	# this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
+	def __init__(self, config=None, data=None):
+		super(BigVGAN, self).__init__()
+
+		"""
+		with open(os.path.join(os.path.dirname(__file__), 'config.json'), 'r') as f:
+			data = f.read()
+		"""
+		if config and data is None:
+			with open(config, 'r') as f:
+				data = f.read()
+			jsonConfig = json.loads(data)
+		elif data is not None:
+			if isinstance(data, str):
+				jsonConfig = json.loads(data)
+			else:
+				jsonConfig = data
+		else:
+			raise Exception("no config specified")
+
+
+		global h
+		h = AttrDict(jsonConfig)
+
+		self.mel_channel = h.num_mels
+		self.noise_dim = h.n_fft
+		self.hop_length = h.hop_size
+		self.num_kernels = len(h.resblock_kernel_sizes)
+		self.num_upsamples = len(h.upsample_rates)
+
+		# pre conv
+		self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
+
+		# define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
+		resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
+
+		# transposed conv-based upsamplers. does not apply anti-aliasing
+		self.ups = nn.ModuleList()
+		for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+			self.ups.append(nn.ModuleList([
+				weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
+											h.upsample_initial_channel // (2 ** (i + 1)),
+											k, u, padding=(k - u) // 2))
+			]))
+
+		# residual blocks using anti-aliased multi-periodicity composition modules (AMP)
+		self.resblocks = nn.ModuleList()
+		for i in range(len(self.ups)):
+			ch = h.upsample_initial_channel // (2 ** (i + 1))
+			for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+				self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
+
+		# post conv
+		if h.activation == "snake":  # periodic nonlinearity with snake function and anti-aliasing
+			activation_post = Snake(ch, alpha_logscale=h.snake_logscale)
+			self.activation_post = Activation1d(activation=activation_post)
+		elif h.activation == "snakebeta":  # periodic nonlinearity with snakebeta function and anti-aliasing
+			activation_post = SnakeBeta(ch, alpha_logscale=h.snake_logscale)
+			self.activation_post = Activation1d(activation=activation_post)
+		else:
+			raise NotImplementedError(
+				"activation incorrectly specified. check the config file and look for 'activation'.")
+
+		self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+
+		# weight initialization
+		for i in range(len(self.ups)):
+			self.ups[i].apply(init_weights)
+		self.conv_post.apply(init_weights)
+
+	def forward(self,x, c):
+		# pre conv
+		x = self.conv_pre(x)
+
+		for i in range(self.num_upsamples):
+			# upsampling
+			for i_up in range(len(self.ups[i])):
+				x = self.ups[i][i_up](x)
+			# AMP blocks
+			xs = None
+			for j in range(self.num_kernels):
+				if xs is None:
+					xs = self.resblocks[i * self.num_kernels + j](x)
+				else:
+					xs += self.resblocks[i * self.num_kernels + j](x)
+			x = xs / self.num_kernels
+
+		# post conv
+		x = self.activation_post(x)
+		x = self.conv_post(x)
+		x = torch.tanh(x)
+
+		return x
+
+	def remove_weight_norm(self):
+		print('Removing weight norm...')
+		for l in self.ups:
+			for l_i in l:
+				remove_weight_norm(l_i)
+		for l in self.resblocks:
+			l.remove_weight_norm()
+		remove_weight_norm(self.conv_pre)
+		remove_weight_norm(self.conv_post)
+
+	def inference(self, c, z=None):
+		# pad input mel with zeros to cut artifact
+		# see https://github.com/seungwonpark/melgan/issues/8
+		zero = torch.full((c.shape[0], h.num_mels, 10), -11.5129).to(c.device)
+		mel = torch.cat((c, zero), dim=2)
+
+		if z is None:
+			z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device)
+
+		audio = self.forward(mel, z)
+		audio = audio[:, :, :-(self.hop_length * 10)]
+		audio = audio.clamp(min=-1, max=1)
+		return audio
+
+	def eval(self, inference=False):
+		super(BigVGAN, self).eval()
+		# don't remove weight norm while validation in training loop
+		if inference:
+			self.remove_weight_norm()
+
+
+class DiscriminatorP(nn.Module):
+	def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False):
+		super(DiscriminatorP, self).__init__()
+		self.period = period
+		self.d_mult = h.discriminator_channel_mult
+		norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+		self.convs = nn.ModuleList([
+			norm_f(Conv2d(1, int(32 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+			norm_f(Conv2d(int(32 * self.d_mult), int(128 * self.d_mult), (kernel_size, 1), (stride, 1),
+						  padding=(get_padding(5, 1), 0))),
+			norm_f(Conv2d(int(128 * self.d_mult), int(512 * self.d_mult), (kernel_size, 1), (stride, 1),
+						  padding=(get_padding(5, 1), 0))),
+			norm_f(Conv2d(int(512 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), (stride, 1),
+						  padding=(get_padding(5, 1), 0))),
+			norm_f(Conv2d(int(1024 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), 1, padding=(2, 0))),
+		])
+		self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)))
+
+	def forward(self, x):
+		fmap = []
+
+		# 1d to 2d
+		b, c, t = x.shape
+		if t % self.period != 0:  # pad first
+			n_pad = self.period - (t % self.period)
+			x = F.pad(x, (0, n_pad), "reflect")
+			t = t + n_pad
+		x = x.view(b, c, t // self.period, self.period)
+
+		for l in self.convs:
+			x = l(x)
+			x = F.leaky_relu(x, LRELU_SLOPE)
+			fmap.append(x)
+		x = self.conv_post(x)
+		fmap.append(x)
+		x = torch.flatten(x, 1, -1)
+
+		return x, fmap
+
+
+class MultiPeriodDiscriminator(nn.Module):
+	def __init__(self, h):
+		super(MultiPeriodDiscriminator, self).__init__()
+		self.mpd_reshapes = h.mpd_reshapes
+		print("mpd_reshapes: {}".format(self.mpd_reshapes))
+		discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
+		self.discriminators = nn.ModuleList(discriminators)
+
+	def forward(self, y, y_hat):
+		y_d_rs = []
+		y_d_gs = []
+		fmap_rs = []
+		fmap_gs = []
+		for i, d in enumerate(self.discriminators):
+			y_d_r, fmap_r = d(y)
+			y_d_g, fmap_g = d(y_hat)
+			y_d_rs.append(y_d_r)
+			fmap_rs.append(fmap_r)
+			y_d_gs.append(y_d_g)
+			fmap_gs.append(fmap_g)
+
+		return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorR(nn.Module):
+	def __init__(self, cfg, resolution):
+		super().__init__()
+
+		self.resolution = resolution
+		assert len(self.resolution) == 3, \
+			"MRD layer requires list with len=3, got {}".format(self.resolution)
+		self.lrelu_slope = LRELU_SLOPE
+
+		norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
+		if hasattr(cfg, "mrd_use_spectral_norm"):
+			print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm))
+			norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
+		self.d_mult = cfg.discriminator_channel_mult
+		if hasattr(cfg, "mrd_channel_mult"):
+			print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult))
+			self.d_mult = cfg.mrd_channel_mult
+
+		self.convs = nn.ModuleList([
+			norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))),
+			norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
+			norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
+			norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
+			norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 3), padding=(1, 1))),
+		])
+		self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
+
+	def forward(self, x):
+		fmap = []
+
+		x = self.spectrogram(x)
+		x = x.unsqueeze(1)
+		for l in self.convs:
+			x = l(x)
+			x = F.leaky_relu(x, self.lrelu_slope)
+			fmap.append(x)
+		x = self.conv_post(x)
+		fmap.append(x)
+		x = torch.flatten(x, 1, -1)
+
+		return x, fmap
+
+	def spectrogram(self, x):
+		n_fft, hop_length, win_length = self.resolution
+		x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
+		x = x.squeeze(1)
+		x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True)
+		x = torch.view_as_real(x)  # [B, F, TT, 2]
+		mag = torch.norm(x, p=2, dim=-1)  # [B, F, TT]
+
+		return mag
+
+
+class MultiResolutionDiscriminator(nn.Module):
+	def __init__(self, cfg, debug=False):
+		super().__init__()
+		self.resolutions = cfg.resolutions
+		assert len(self.resolutions) == 3, \
+			"MRD requires list of list with len=3, each element having a list with len=3. got {}". \
+				format(self.resolutions)
+		self.discriminators = nn.ModuleList(
+			[DiscriminatorR(cfg, resolution) for resolution in self.resolutions]
+		)
+
+	def forward(self, y, y_hat):
+		y_d_rs = []
+		y_d_gs = []
+		fmap_rs = []
+		fmap_gs = []
+
+		for i, d in enumerate(self.discriminators):
+			y_d_r, fmap_r = d(x=y)
+			y_d_g, fmap_g = d(x=y_hat)
+			y_d_rs.append(y_d_r)
+			fmap_rs.append(fmap_r)
+			y_d_gs.append(y_d_g)
+			fmap_gs.append(fmap_g)
+
+		return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+def get_mel(x):
+	return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)
+
+def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+	if torch.min(y) < -1.:
+		print('min value is ', torch.min(y))
+	if torch.max(y) > 1.:
+		print('max value is ', torch.max(y))
+
+	global mel_basis, hann_window
+	if fmax not in mel_basis:
+		mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+		mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+		hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
+
+	y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+	y = y.squeeze(1)
+
+	# complex tensor as default, then use view_as_real for future pytorch compatibility
+	spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
+					  center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
+	spec = torch.view_as_real(spec)
+	spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
+
+	spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
+	spec = torch.nn.utils.spectral_normalize_torch(spec)
+
+	return spec
+
+def feature_loss(fmap_r, fmap_g):
+	loss = 0
+	for dr, dg in zip(fmap_r, fmap_g):
+		for rl, gl in zip(dr, dg):
+			loss += torch.mean(torch.abs(rl - gl))
+
+	return loss * 2
+
+
+def init_weights(m, mean=0.0, std=0.01):
+	classname = m.__class__.__name__
+	if classname.find("Conv") != -1:
+		m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+	return int((kernel_size * dilation - dilation) / 2)
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+	loss = 0
+	r_losses = []
+	g_losses = []
+	for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+		r_loss = torch.mean((1 - dr) ** 2)
+		g_loss = torch.mean(dg ** 2)
+		loss += (r_loss + g_loss)
+		r_losses.append(r_loss.item())
+		g_losses.append(g_loss.item())
+
+	return loss, r_losses, g_losses
+
+
+def generator_loss(disc_outputs):
+	loss = 0
+	gen_losses = []
+	for dg in disc_outputs:
+		l = torch.mean((1 - dg) ** 2)
+		gen_losses.append(l)
+		loss += l
+
+	return loss, gen_losses
+
+
+if __name__ == '__main__':
+	model = BigVGAN()
+
+	c = torch.randn(3, 100, 10)
+	z = torch.randn(3, 64, 10)
+	print(c.shape)
+
+	y = model(c, z)
+	print(y.shape)
+	assert y.shape == torch.Size([3, 1, 2560])
+
+	pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+	print(pytorch_total_params)

tortoise_tts/models/classifier.py

@@ -1,3 +1,5 @@
+# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/classifier.py
+
 import torch
 import torch.nn as nn
 

tortoise_tts/models/clvp.py

@@ -1,3 +1,5 @@
+# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/clvp.py
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

tortoise_tts/models/diffusion.py

@@ -1,3 +1,5 @@
+# Adapted from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/diffusion.py
+
 import enum
 import math
 import random

tortoise_tts/models/hifigan.py

@@ -0,0 +1,305 @@
+# Grabbed from https://git.ecker.tech/Jarod/tortoise-tts/src/branch/main
+# Adapted from https://github.com/jik876/hifi-gan/blob/master/models.py
+
+import torch
+from torch import nn
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+LRELU_SLOPE = 0.1
+
+
+def get_padding(k, d):
+	return int((k * d - d) / 2)
+
+
+class ResBlock1(torch.nn.Module):
+	"""Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
+
+	Network::
+
+		x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
+		|--------------------------------------------------------------------------------------------------|
+
+
+	Args:
+		channels (int): number of hidden channels for the convolutional layers.
+		kernel_size (int): size of the convolution filter in each layer.
+		dilations (list): list of dilation value for each conv layer in a block.
+	"""
+
+	def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+		super().__init__()
+		self.convs1 = nn.ModuleList(
+			[
+				weight_norm(
+					Conv1d(
+						channels,
+						channels,
+						kernel_size,
+						1,
+						dilation=dilation[0],
+						padding=get_padding(kernel_size, dilation[0]),
+					)
+				),
+				weight_norm(
+					Conv1d(
+						channels,
+						channels,
+						kernel_size,
+						1,
+						dilation=dilation[1],
+						padding=get_padding(kernel_size, dilation[1]),
+					)
+				),
+				weight_norm(
+					Conv1d(
+						channels,
+						channels,
+						kernel_size,
+						1,
+						dilation=dilation[2],
+						padding=get_padding(kernel_size, dilation[2]),
+					)
+				),
+			]
+		)
+
+		self.convs2 = nn.ModuleList(
+			[
+				weight_norm(
+					Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+				),
+				weight_norm(
+					Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+				),
+				weight_norm(
+					Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+				),
+			]
+		)
+
+	def forward(self, x):
+		"""
+		Args:
+			x (Tensor): input tensor.
+		Returns:
+			Tensor: output tensor.
+		Shapes:
+			x: [B, C, T]
+		"""
+		for c1, c2 in zip(self.convs1, self.convs2):
+			xt = F.leaky_relu(x, LRELU_SLOPE)
+			xt = c1(xt)
+			xt = F.leaky_relu(xt, LRELU_SLOPE)
+			xt = c2(xt)
+			x = xt + x
+		return x
+
+	def remove_weight_norm(self):
+		for l in self.convs1:
+			remove_weight_norm(l)
+		for l in self.convs2:
+			remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+	"""Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
+
+	Network::
+
+		x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
+		|---------------------------------------------------|
+
+
+	Args:
+		channels (int): number of hidden channels for the convolutional layers.
+		kernel_size (int): size of the convolution filter in each layer.
+		dilations (list): list of dilation value for each conv layer in a block.
+	"""
+
+	def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+		super().__init__()
+		self.convs = nn.ModuleList(
+			[
+				weight_norm(
+					Conv1d(
+						channels,
+						channels,
+						kernel_size,
+						1,
+						dilation=dilation[0],
+						padding=get_padding(kernel_size, dilation[0]),
+					)
+				),
+				weight_norm(
+					Conv1d(
+						channels,
+						channels,
+						kernel_size,
+						1,
+						dilation=dilation[1],
+						padding=get_padding(kernel_size, dilation[1]),
+					)
+				),
+			]
+		)
+
+	def forward(self, x):
+		for c in self.convs:
+			xt = F.leaky_relu(x, LRELU_SLOPE)
+			xt = c(xt)
+			x = xt + x
+		return x
+
+	def remove_weight_norm(self):
+		for l in self.convs:
+			remove_weight_norm(l)
+
+
+class HifiganGenerator(torch.nn.Module):
+	def __init__(
+		self,
+		in_channels,
+		out_channels,
+		resblock_type,
+		resblock_dilation_sizes,
+		resblock_kernel_sizes,
+		upsample_kernel_sizes,
+		upsample_initial_channel,
+		upsample_factors,
+		inference_padding=5,
+		cond_channels=0,
+		conv_pre_weight_norm=True,
+		conv_post_weight_norm=True,
+		conv_post_bias=True,
+	):
+		r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
+
+		Network:
+			x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
+												 ..          -> zI ---|
+											  resblockN_kNx1 -> zN ---'
+
+		Args:
+			in_channels (int): number of input tensor channels.
+			out_channels (int): number of output tensor channels.
+			resblock_type (str): type of the `ResBlock`. '1' or '2'.
+			resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
+			resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
+			upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
+			upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
+				for each consecutive upsampling layer.
+			upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
+			inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
+		"""
+		super().__init__()
+		self.inference_padding = inference_padding
+		self.num_kernels = len(resblock_kernel_sizes)
+		self.num_upsamples = len(upsample_factors)
+		# initial upsampling layers
+		self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
+		resblock = ResBlock1 if resblock_type == "1" else ResBlock2
+		# upsampling layers
+		self.ups = nn.ModuleList()
+		for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
+			self.ups.append(
+				weight_norm(
+					ConvTranspose1d(
+						upsample_initial_channel // (2**i),
+						upsample_initial_channel // (2 ** (i + 1)),
+						k,
+						u,
+						padding=(k - u) // 2,
+					)
+				)
+			)
+		# MRF blocks
+		self.resblocks = nn.ModuleList()
+		for i in range(len(self.ups)):
+			ch = upsample_initial_channel // (2 ** (i + 1))
+			for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+				self.resblocks.append(resblock(ch, k, d))
+		# post convolution layer
+		self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
+		if cond_channels > 0:
+			self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
+
+		if not conv_pre_weight_norm:
+			remove_weight_norm(self.conv_pre)
+
+		if not conv_post_weight_norm:
+			remove_weight_norm(self.conv_post)
+
+		self.device = torch.device('cuda' if torch.cuda.is_available() else'cpu')
+		if torch.backends.mps.is_available():
+			self.device = torch.device('mps')
+
+	def forward(self, x, g=None):
+		"""
+		Args:
+			x (Tensor): feature input tensor.
+			g (Tensor): global conditioning input tensor.
+
+		Returns:
+			Tensor: output waveform.
+
+		Shapes:
+			x: [B, C, T]
+			Tensor: [B, 1, T]
+		"""
+		o = self.conv_pre(x)
+		if hasattr(self, "cond_layer"):
+			o = o + self.cond_layer(g)
+		for i in range(self.num_upsamples):
+			o = F.leaky_relu(o, LRELU_SLOPE)
+			o = self.ups[i](o)
+			z_sum = None
+			for j in range(self.num_kernels):
+				if z_sum is None:
+					z_sum = self.resblocks[i * self.num_kernels + j](o)
+				else:
+					z_sum += self.resblocks[i * self.num_kernels + j](o)
+			o = z_sum / self.num_kernels
+		o = F.leaky_relu(o)
+		o = self.conv_post(o)
+		o = torch.tanh(o)
+		return o
+
+	@torch.no_grad()
+	def inference(self, c, g=None):
+		"""
+		Args:
+			x (Tensor): conditioning input tensor.
+
+		Returns:
+			Tensor: output waveform.
+
+		Shapes:
+			x: [B, C, T]
+			Tensor: [B, 1, T]
+		"""
+		# c = c.to(self.conv_pre.weight.device)
+		# c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
+		up_1 = torch.nn.functional.interpolate(
+				c.transpose(1,2),
+				scale_factor=[1024 / 256],
+				mode="linear",
+			)
+		up_2 = torch.nn.functional.interpolate(
+			up_1,
+			scale_factor=[24000 / 22050],
+			mode="linear",
+		)
+		g = g.unsqueeze(0)
+		return self.forward(up_2.to(self.device), g.transpose(1,2))
+
+	def remove_weight_norm(self):
+		print("Removing weight norm...")
+		for l in self.ups:
+			remove_weight_norm(l)
+		for l in self.resblocks:
+			l.remove_weight_norm()
+		remove_weight_norm(self.conv_pre)
+		remove_weight_norm(self.conv_post)

tortoise_tts/models/lora.py

@@ -1,4 +1,5 @@
 # Adapted from https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
+
 from functools import partial
 import torch
 import torch.nn.functional as F

tortoise_tts/models/random_latent_generator.py

@@ -1,3 +1,6 @@
+# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/random_latent_generator.py
+
+
 import math
 
 import torch

tortoise_tts/models/transformer.py

@@ -1,3 +1,5 @@
+# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/transformer.py
+
 from functools import partial
 
 import torch

tortoise_tts/models/unified_voice.py

@@ -1,3 +1,5 @@
+# Adapted from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/unified_voice.py
+
 import functools
 
 import torch
@@ -14,6 +16,8 @@ from transformers import LogitsWarper
 from transformers import GPT2Config, GPT2Model
 from tqdm import tqdm
 
+from .stream_generator import NewGenerationMixin
+
 AVAILABLE_ATTENTIONS = ["mem_efficient", "math"]
 
 try:
@@ -83,12 +87,14 @@ class ResBlock(nn.Module):
 	def forward(self, x):
 		return F.relu(self.net(x) + x)
 
-class GPT2InferenceModel(GPT2PreTrainedModel):
+class GPT2InferenceModel(GPT2PreTrainedModel, NewGenerationMixin):
 	def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear, kv_cache=True):
-		super().__init__(config)
+		super(NewGenerationMixin, self).__init__()
+		super(GPT2PreTrainedModel, self).__init__(config)
 		self.transformer = gpt
 		self.text_pos_embedding = text_pos_emb
 		self.embeddings = embeddings
+		self.final_norm = norm
 		self.lm_head = nn.Sequential(norm, linear)
 
 		self.kv_cache = kv_cache
@@ -129,14 +135,14 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
 	def store_mel_emb(self, mel_emb):
 		self.cached_mel_emb = mel_emb
 
-	def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
 		token_type_ids = kwargs.get("token_type_ids", None)
 
 		if not self.kv_cache:
-			past = None
+			past_key_values = None
 
 		# only last token for inputs_ids if past is defined in kwargs
-		if past:
+		if past_key_values:
 			input_ids = input_ids[:, -1].unsqueeze(-1)
 			if token_type_ids is not None:
 				token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
@@ -148,13 +154,13 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
 			# create position_ids on the fly for batch generation
 			position_ids = attention_mask.long().cumsum(-1) - 1
 			position_ids.masked_fill_(attention_mask == 0, 1)
-			if past:
+			if past_key_values:
 				position_ids = position_ids[:, -1].unsqueeze(-1)
 		else:
 			position_ids = None
 		return {
 			"input_ids": input_ids,
-			"past_key_values": past,
+			"past_key_values": past_key_values,
 			"use_cache": kwargs.get("use_cache"),
 			"position_ids": position_ids,
 			"attention_mask": attention_mask,
@@ -597,6 +603,24 @@ class UnifiedVoice(nn.Module):
 
 		return loss_text.mean(), loss_mel.mean(), mel_logits
 
+	def compute_embeddings( self, cond_latents, text_inputs, kv_cache = True ):
+		text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
+		text_inputs = F.pad(text_inputs, (1, 0), value=self.start_text_token)
+		emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
+		conds = cond_latents.unsqueeze(1)
+		emb = torch.cat([conds, emb], dim=1)
+		
+		if not hasattr(self, 'inference_model'):
+			# TODO: Decouple gpt_config from this inference model.
+			self.post_init_gpt2_config(kv_cache = kv_cache)
+
+		self.inference_model.store_mel_emb(emb)
+
+		embs = torch.full( ( emb.shape[0], emb.shape[1] + 1 ), fill_value=1, dtype=torch.long, device=text_inputs.device )
+		embs[:, -1] = self.start_mel_token
+		
+		return embs
+
 	def inference_speech(self, speech_conditioning_latent, text_inputs, input_tokens=None, num_return_sequences=1,
 						 max_generate_length=None, typical_sampling=False, typical_mass=.9, kv_cache=True, **hf_generate_kwargs):
 
@@ -635,6 +659,17 @@ class UnifiedVoice(nn.Module):
 		self.inference_model.bar.close()
 		return gen[:, trunc_index:]
 
+	def get_generator(self, inputs, max_length=500, **hf_generate_kwargs):
+		return self.inference_model.generate(
+			inputs,
+			bos_token_id=self.start_mel_token,
+			pad_token_id=self.stop_mel_token,
+			eos_token_id=self.stop_mel_token,
+			max_length=max_length,
+			do_stream=True,
+			**hf_generate_kwargs,
+		)
+
 
 if __name__ == '__main__':
 	gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4)

tortoise_tts/models/vocoder.py

@@ -1,3 +1,5 @@
+# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/vocoder.py
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

tortoise_tts/models/xtransformers.py

@@ -1,3 +1,5 @@
+# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/xtransformers.py
+
 import math
 from collections import namedtuple
 from functools import partial

tortoise_tts/webui.py

@@ -95,6 +95,7 @@ def do_inference( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
 	parser.add_argument("--beam-width", type=int, default=kwargs["beam-width"])
 	parser.add_argument("--diffusion-sampler", type=str, default=kwargs["diffusion-sampler"])
 	parser.add_argument("--cond-free", type=str, default=kwargs["cond-free"])
+	parser.add_argument("--vocoder", type=str, default=kwargs["vocoder"].lower())
 	"""
 	parser.add_argument("--repetition-penalty-decay", type=float, default=kwargs["repetition-penalty-decay"])
 	parser.add_argument("--mirostat-tau", type=float, default=kwargs["mirostat-tau"])
@@ -126,6 +127,7 @@ def do_inference( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
 			beam_width=args.beam_width,
 
 			diffusion_sampler=args.diffusion_sampler,
+			vocoder_type=args.vocoder,
 		)
 	
 	wav = wav.squeeze(0).cpu().numpy()
@@ -210,7 +212,7 @@ with ui:
 			with gr.Column(scale=1):
 				layout["inference"]["inputs"]["reference"] = gr.Audio(label="Audio Input", sources=["upload"], type="filepath") #, info="Reference audio for TTS")
 				# layout["inference"]["stop"] = gr.Button(value="Stop")
-				layout["inference"]["outputs"]["output"] = gr.Audio(label="Output")
+				layout["inference"]["outputs"]["output"] = gr.Audio(label="Output", streaming=True)
 				layout["inference"]["buttons"]["inference"] = gr.Button(value="Inference")
 			with gr.Column(scale=7):
 				with gr.Row():
@@ -221,6 +223,7 @@ with ui:
 				with gr.Row():
 					layout["inference"]["inputs"]["ar-temp"] = gr.Slider(value=0.8, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy sample)")
 					layout["inference"]["inputs"]["diffusion-temp"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (Diffusion)", info="Modifies the initial noise during the diffusion pass.")
+					layout["inference"]["inputs"]["vocoder"] = gr.Radio( ["Vocoder", "BigVGAN", "HiFiGAN"], value="BigVGAN", label="Vocoder", type="value", info="Vocoder to use for generating the final waveform (HiFiGAN skips diffusion)." )
 				"""
 				with gr.Row():
 					layout["inference"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")