added BigVGAN and HiFiGAN (from https://git.ecker.tech/jarod/tortoise-tts), vocoder selectable in webUI
This commit is contained in:
parent
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0b1a71430c
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@ -1,3 +1,5 @@
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vocoder: "hifigan"
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models:
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- name: "autoregressive"
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training: True
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@ -71,7 +73,7 @@ trainer:
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backend: deepspeed
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deepspeed:
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inferencing: True
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inferencing: False
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zero_optimization_level: 0
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use_compression_training: False
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@ -24,6 +24,7 @@ def main():
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parser.add_argument("--diffusion-sampler", type=str, default="ddim")
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parser.add_argument("--cond-free", action="store_true")
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parser.add_argument("--vocoder", type=str, default="bigvgan")
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parser.add_argument("--yaml", type=Path, default=None)
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parser.add_argument("--device", type=str, default=None)
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@ -62,6 +63,8 @@ def main():
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diffusion_sampler=args.diffusion_sampler,
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cond_free=args.cond_free,
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vocoder_type=args.vocoder,
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)
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"""
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language=args.language,
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@ -526,6 +526,8 @@ class Config(BaseConfig):
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sample_rate: int = 24_000
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audio_backend: str = "mel"
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vocoder: str = "bigvgan" # "vocoder" | "bigvgan" | "hifigan"
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@property
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def model(self):
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for i, model in enumerate(self.models):
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@ -5,6 +5,7 @@ import soundfile
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from torch import Tensor
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from einops import rearrange
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from pathlib import Path
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from tqdm import tqdm
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from .emb.mel import encode_from_files as encode_mel, trim, trim_random
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from .utils import to_device
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@ -96,6 +97,21 @@ class TTS():
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# merge inputs
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return encode_mel( paths, device=self.device )
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# taken from here https://github.com/coqui-ai/TTS/blob/d21f15cc850788f9cdf93dac0321395138665287/TTS/tts/models/xtts.py#L666
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def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
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"""Handle chunk formatting in streaming mode"""
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wav_chunk = wav_gen[:-overlap_len]
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if wav_gen_prev is not None:
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wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len) : -overlap_len]
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if wav_overlap is not None:
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crossfade_wav = wav_chunk[:overlap_len]
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crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_len).to(crossfade_wav.device)
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wav_chunk[:overlap_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_len).to(wav_overlap.device)
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wav_chunk[:overlap_len] += crossfade_wav
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wav_overlap = wav_gen[-overlap_len:]
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wav_gen_prev = wav_gen
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return wav_chunk, wav_gen_prev, wav_overlap
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@torch.inference_mode()
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def inference(
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self,
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@ -122,7 +138,9 @@ class TTS():
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diffusion_sampler="ddim",
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cond_free=True,
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out_path=None
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vocoder_type="bigvgan",
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out_path=None,
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):
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lines = text.split("\n")
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@ -142,7 +160,7 @@ class TTS():
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diffusion = engine.module
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elif "clvp" in name:
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clvp = engine.module
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elif "vocoder" in name:
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elif vocoder_type in name:
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vocoder = engine.module
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if autoregressive is None:
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@ -152,7 +170,7 @@ class TTS():
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if clvp is None:
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clvp = load_model("clvp", device=cfg.device)
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if vocoder is None:
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vocoder = load_model("vocoder", device=cfg.device)
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vocoder = load_model(vocoder_type, device=cfg.device)
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autoregressive = autoregressive.to(cfg.device)
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diffusion = diffusion.to(cfg.device)
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@ -183,6 +201,88 @@ class TTS():
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text_tokens = pad_sequence([ text ], batch_first = True)
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text_lengths = torch.Tensor([ text.shape[0] ]).to(dtype=torch.int32)
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# streaming interface spits out the final hidden state, which HiFiGAN seems to be trained against
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if vocoder_type == "hifigan":
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waves = []
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all_latents = []
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all_codes = []
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wav_gen_prev = None
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wav_overlap = None
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is_end = False
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first_buffer = 60
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stream_chunk_size = 40
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overlap_wav_len = 1024
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with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
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with ml.auto_unload(autoregressive, enabled=cfg.inference.auto_unload):
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with ml.auto_unload(vocoder, enabled=cfg.inference.auto_unload):
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inputs = autoregressive.compute_embeddings( autoregressive_latents, text_tokens )
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gpt_generator = autoregressive.get_generator(
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inputs=inputs,
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top_k=top_k,
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top_p=top_p,
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temperature=ar_temp,
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do_sample=True,
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num_beams=max(1, beam_width),
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num_return_sequences=1,
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length_penalty=length_penalty,
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repetition_penalty=repetition_penalty,
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output_attentions=False,
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output_hidden_states=True,
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)
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bar = tqdm( unit="it", total=500 )
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while not is_end:
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try:
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codes, latent = next(gpt_generator)
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all_latents += [latent]
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all_codes += [codes]
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except StopIteration:
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is_end = True
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if is_end or (stream_chunk_size > 0 and len(all_codes) >= max(stream_chunk_size, first_buffer)):
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first_buffer = 0
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all_codes = []
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bar.update( stream_chunk_size )
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latents = torch.cat(all_latents, dim=0)[None, :].to(cfg.device)
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wav_gen = vocoder.inference(latents, autoregressive_latents)
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wav_gen = wav_gen.squeeze()
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wav_chunk = wav_gen[:-overlap_wav_len]
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if wav_gen_prev is not None:
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wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_wav_len) : -overlap_wav_len]
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if wav_overlap is not None:
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crossfade_wav = wav_chunk[:overlap_wav_len]
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crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_wav_len).to(crossfade_wav.device)
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wav_chunk[:overlap_wav_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_wav_len).to(wav_overlap.device)
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wav_chunk[:overlap_wav_len] += crossfade_wav
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wav_overlap = wav_gen[-overlap_wav_len:]
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wav_gen_prev = wav_gen
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# yielding requires to do a bunch of pain to work around it turning into an async function
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"""
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yield wav_chunk
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"""
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waves.append( wav_chunk.unsqueeze(0) )
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bar.close()
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wav = torch.concat(waves, dim=-1)
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if out_path is not None:
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torchaudio.save( out_path, wav.cpu(), sr )
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wavs.append(wav)
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continue
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with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
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with ml.auto_unload(autoregressive, enabled=cfg.inference.auto_unload):
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# autoregressive pass
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@ -190,9 +290,11 @@ class TTS():
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autoregressive_latents,
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text_tokens,
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do_sample=True,
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top_k=top_k,
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top_p=top_p,
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temperature=ar_temp,
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num_return_sequences=candidates,
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num_beams=max(1,beam_width),
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length_penalty=length_penalty,
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repetition_penalty=repetition_penalty,
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max_generate_length=max_ar_steps,
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@ -1,5 +1,4 @@
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# https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models
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# All code under this folder is licensed as Apache License 2.0 per the original repo
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# All other ccode in this folder are licensed per the attributions at the top
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from functools import cache
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@ -8,6 +7,8 @@ from .arch_utils import TorchMelSpectrogram, TacotronSTFT
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from .unified_voice import UnifiedVoice
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from .diffusion import DiffusionTTS
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from .vocoder import UnivNetGenerator
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from .bigvgan import BigVGAN
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from .hifigan import HifiganGenerator
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from .clvp import CLVP
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from .dvae import DiscreteVAE
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from .random_latent_generator import RandomLatentConverter
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@ -15,6 +16,8 @@ from .random_latent_generator import RandomLatentConverter
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import os
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import torch
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from pathlib import Path
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import requests
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from tqdm import tqdm
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DEFAULT_MODEL_PATH = Path(__file__).parent.parent.parent / 'data/models'
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DEFAULT_MODEL_URLS = {
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@ -28,10 +31,18 @@ DEFAULT_MODEL_URLS = {
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'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth',
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'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth',
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'mel_norms.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/data/mel_norms.pth',
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# BigVGAN
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'bigvgan_base_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_base_24khz_100band.pth',
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'bigvgan_24khz_100band.pth': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_24khz_100band.pth',
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'bigvgan_base_24khz_100band.json': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_base_24khz_100band.json',
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'bigvgan_24khz_100band.json': 'https://huggingface.co/ecker/tortoise-tts-models/resolve/main/models/bigvgan_24khz_100band.json',
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# HiFiGAN
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'hifigan.pth': 'https://huggingface.co/Manmay/tortoise-tts/resolve/main/hifidecoder.pth',
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}
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import requests
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from tqdm import tqdm
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# kludge, probably better to use HF's model downloader function
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# to-do: write to a temp file then copy so downloads can be interrupted
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@ -75,6 +86,7 @@ def download_model( save_path, chunkSize = 1024, unit = "MiB" ):
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@cache
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def load_model(name, device="cuda", **kwargs):
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load_path = None
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config_path = None
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state_dict_key = None
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strict = True
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@ -95,6 +107,31 @@ def load_model(name, device="cuda", **kwargs):
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elif "clvp" in name:
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model = CLVP(**kwargs)
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load_path = DEFAULT_MODEL_PATH / 'clvp2.pth'
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elif "bigvgan" in name:
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# download any JSONs (BigVGAN)
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load_path = DEFAULT_MODEL_PATH / 'bigvgan_24khz_100band.pth'
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config_path = load_path.with_suffix(".json")
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if config_path.name in DEFAULT_MODEL_URLS:
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if not config_path.exists():
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download_model( config_path )
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else:
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config_path = None
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model = BigVGAN(config=config_path, **kwargs)
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state_dict_key = 'generator'
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elif "hifigan" in name:
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model = HifiganGenerator(
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in_channels=1024,
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out_channels = 1,
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resblock_type = "1",
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resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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resblock_kernel_sizes = [3, 7, 11],
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upsample_kernel_sizes = [16, 16, 4, 4],
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upsample_initial_channel = 512,
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upsample_factors = [8, 8, 2, 2],
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cond_channels=1024
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)
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load_path = DEFAULT_MODEL_PATH / 'hifigan.pth'
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elif "vocoder" in name:
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model = UnivNetGenerator(**kwargs)
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load_path = DEFAULT_MODEL_PATH / 'vocoder.pth'
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@ -126,6 +163,11 @@ def load_model(name, device="cuda", **kwargs):
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model.eval()
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try:
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print(f"{name} ({next(model.parameters()).dtype}): {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
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except Exception as e:
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print(f"{name}: {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
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return model
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def unload_model():
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@ -138,8 +180,6 @@ def get_model(config, training=True):
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config.training = "autoregressive" in config.name
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model.config = config
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print(f"{name} ({next(model.parameters()).dtype}): {sum(p.numel() for p in model.parameters() if p.requires_grad)} parameters")
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return model
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def get_models(models, training=True):
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# Adapted from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/arch_utils.py
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import os
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import functools
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import math
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772
tortoise_tts/models/bigvgan.py
Normal file
772
tortoise_tts/models/bigvgan.py
Normal file
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@ -0,0 +1,772 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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import json
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import os
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import torch.utils.data
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from torch import nn, sin, pow
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from torch.nn import Conv1d, ConvTranspose1d, Conv2d, Parameter
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from librosa.filters import mel as librosa_mel_fn
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# filter.py
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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
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# LICENSE is in incl_licenses directory.
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if 'sinc' in dir(torch):
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sinc = torch.sinc
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else:
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# This code is adopted from adefossez's julius.core.sinc under the MIT License
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# https://adefossez.github.io/julius/julius/core.html
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# LICENSE is in incl_licenses directory.
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def sinc(x: torch.Tensor):
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"""
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Implementation of sinc, i.e. sin(pi * x) / (pi * x)
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__Warning__: Different to julius.sinc, the input is multiplied by `pi`!
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"""
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return torch.where(x == 0,
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torch.tensor(1., device=x.device, dtype=x.dtype),
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torch.sin(math.pi * x) / math.pi / x)
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# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
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# https://adefossez.github.io/julius/julius/lowpass.html
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# LICENSE is in incl_licenses directory.
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def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
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even = (kernel_size % 2 == 0)
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half_size = kernel_size // 2
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#For kaiser window
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delta_f = 4 * half_width
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A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
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if A > 50.:
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beta = 0.1102 * (A - 8.7)
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elif A >= 21.:
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beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
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else:
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beta = 0.
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window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
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# ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
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if even:
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time = (torch.arange(-half_size, half_size) + 0.5)
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else:
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time = torch.arange(kernel_size) - half_size
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if cutoff == 0:
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filter_ = torch.zeros_like(time)
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else:
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filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
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# Normalize filter to have sum = 1, otherwise we will have a small leakage
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# of the constant component in the input signal.
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filter_ /= filter_.sum()
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filter = filter_.view(1, 1, kernel_size)
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return filter
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class LowPassFilter1d(nn.Module):
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def __init__(self,
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cutoff=0.5,
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half_width=0.6,
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stride: int = 1,
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padding: bool = True,
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padding_mode: str = 'replicate',
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kernel_size: int = 12):
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# kernel_size should be even number for stylegan3 setup,
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# in this implementation, odd number is also possible.
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super().__init__()
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if cutoff < -0.:
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raise ValueError("Minimum cutoff must be larger than zero.")
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if cutoff > 0.5:
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raise ValueError("A cutoff above 0.5 does not make sense.")
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self.kernel_size = kernel_size
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self.even = (kernel_size % 2 == 0)
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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)
|
||||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
# Adapted from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/diffusion.py
|
||||
|
||||
import enum
|
||||
import math
|
||||
import random
|
||||
|
|
|
|||
305
tortoise_tts/models/hifigan.py
Normal file
305
tortoise_tts/models/hifigan.py
Normal file
|
|
@ -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)
|
||||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -1,3 +1,6 @@
|
|||
# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/random_latent_generator.py
|
||||
|
||||
|
||||
import math
|
||||
|
||||
import torch
|
||||
|
|
|
|||
1064
tortoise_tts/models/stream_generator.py
Normal file
1064
tortoise_tts/models/stream_generator.py
Normal file
File diff suppressed because it is too large
Load Diff
|
|
@ -1,3 +1,5 @@
|
|||
# Copied from https://github.com/neonbjb/tortoise-tts/tree/98a891e66e7a1f11a830f31bd1ce06cc1f6a88af/tortoise/models/transformer.py
|
||||
|
||||
from functools import partial
|
||||
|
||||
import torch
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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.")
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user