From d75ea7c21c1fc2ddecb931a7b55b7bd19c6c64cf Mon Sep 17 00:00:00 2001 From: Leng Yue Date: Sun, 5 Mar 2023 16:07:22 -0500 Subject: [PATCH] Reorganize folders & better inference archs (#34) * move files for future archs * remove redundent code * organize files * continue reorganize * split gradio ui from inference & integrate batch inference * fix imports * move files to correct folder * moved files & optimize for future archs * move files & optimize for future archs * fix onnx export --- README.en.md | 12 +- README.md | 12 +- fish_diffusion/archs/diffsinger.py | 109 --- .../archs/diffsinger/diffsinger.py | 202 ++--- .../diffsinger}/diffusions/__init__.py | 0 .../archs/diffsinger/diffusions/builder.py | 8 + .../diffsinger}/diffusions/diffusion.py | 14 +- .../diffsinger}/diffusions/noise_predictor.py | 0 fish_diffusion/denoisers/__init__.py | 5 - fish_diffusion/denoisers/attention.py | 57 -- fish_diffusion/denoisers/builder.py | 3 - fish_diffusion/diffusions/builder.py | 3 - .../{ => modules}/encoders/__init__.py | 0 .../{ => modules}/encoders/attention.py | 0 .../{ => modules}/encoders/builder.py | 0 .../{ => modules}/encoders/fast_speech.py | 206 ++++- .../{ => modules}/encoders/identity.py | 0 .../encoders/naive_projection.py | 0 .../feature_extractors/__init__.py | 2 - .../{ => modules}/feature_extractors/base.py | 1 - .../feature_extractors/builder.py | 0 .../feature_extractors/chinese_hubert.py | 0 .../feature_extractors/content_vec.py | 0 .../feature_extractors/hubert_soft.py | 0 .../opencpop_transcription.py | 0 .../feature_extractors/whisper.py | 0 .../pitch_extractors}/__init__.py | 0 .../pitch_extractors}/builder.py | 0 .../pitch_extractors}/crepe.py | 0 .../pitch_extractors}/parsel_mouth.py | 0 .../pitch_extractors}/world.py | 0 .../modules/positional_embedding.py | 205 ----- .../{ => modules}/vocoders/__init__.py | 0 .../{ => modules}/vocoders/builder.py | 0 .../vocoders/istft_net/istft_net.py | 0 .../{ => modules}/vocoders/istft_net/mel.py | 0 .../vocoders/istft_net/models.py | 0 .../vocoders/nsf_hifigan/__init__.py | 0 .../vocoders/nsf_hifigan/models.py | 0 .../vocoders/nsf_hifigan/nsf_hifigan.py | 0 .../{denoisers => modules}/wavenet.py | 12 +- fish_diffusion/schedulers/__init__.py | 3 + .../scheduler.py => schedulers/builder.py} | 0 fish_diffusion/utils/audio.py | 31 +- fish_diffusion/utils/inference.py | 8 +- fish_diffusion/utils/ssim.py | 114 --- inference.py | 431 ---------- poetry.lock | 772 +++++++++--------- tests/test_nsf_hifigan.py | 4 +- tests/test_visualize_pitch.py | 2 +- tools/batch_inference.py | 53 -- .../clean_speaker_embeddings.py | 0 tools/{ => diffusion}/diff_svc_converter.py | 1 - .../flask_api.py} | 87 +- tools/diffusion/gradio_ui.py | 92 +++ tools/diffusion/inference.py | 354 ++++++++ .../diffusion/inference_svs.py | 13 +- .../diffusion/inference_svs_ds.py | 14 +- tools/{ => diffusion}/onnx/demo.py | 7 +- tools/{ => diffusion}/onnx/export.py | 15 +- tools/{ => diffusion}/onnx/export_moess.py | 14 +- tools/diffusion/train.py | 93 +++ tools/nsf_hifigan/export.py | 2 +- tools/nsf_hifigan/train.py | 6 +- tools/preprocessing/extract_features.py | 11 +- tools/whisper/train.py | 2 +- 66 files changed, 1373 insertions(+), 1607 deletions(-) delete mode 100644 fish_diffusion/archs/diffsinger.py rename train.py => fish_diffusion/archs/diffsinger/diffsinger.py (57%) rename fish_diffusion/{ => archs/diffsinger}/diffusions/__init__.py (100%) create mode 100644 fish_diffusion/archs/diffsinger/diffusions/builder.py rename fish_diffusion/{ => archs/diffsinger}/diffusions/diffusion.py (95%) rename fish_diffusion/{ => archs/diffsinger}/diffusions/noise_predictor.py (100%) delete mode 100644 fish_diffusion/denoisers/__init__.py delete mode 100644 fish_diffusion/denoisers/attention.py delete mode 100644 fish_diffusion/denoisers/builder.py delete mode 100644 fish_diffusion/diffusions/builder.py rename fish_diffusion/{ => modules}/encoders/__init__.py (100%) rename fish_diffusion/{ => modules}/encoders/attention.py (100%) rename fish_diffusion/{ => modules}/encoders/builder.py (100%) rename fish_diffusion/{ => modules}/encoders/fast_speech.py (76%) rename fish_diffusion/{ => modules}/encoders/identity.py (100%) rename fish_diffusion/{ => modules}/encoders/naive_projection.py (100%) rename fish_diffusion/{ => modules}/feature_extractors/__init__.py (88%) rename fish_diffusion/{ => modules}/feature_extractors/base.py (97%) rename fish_diffusion/{ => modules}/feature_extractors/builder.py (100%) rename fish_diffusion/{ => modules}/feature_extractors/chinese_hubert.py (100%) rename fish_diffusion/{ => modules}/feature_extractors/content_vec.py (100%) rename fish_diffusion/{ => modules}/feature_extractors/hubert_soft.py (100%) rename fish_diffusion/{ => modules}/feature_extractors/opencpop_transcription.py (100%) rename fish_diffusion/{ => modules}/feature_extractors/whisper.py (100%) rename fish_diffusion/{feature_extractors/pitch => modules/pitch_extractors}/__init__.py (100%) rename fish_diffusion/{feature_extractors/pitch => modules/pitch_extractors}/builder.py (100%) rename fish_diffusion/{feature_extractors/pitch => modules/pitch_extractors}/crepe.py (100%) rename fish_diffusion/{feature_extractors/pitch => modules/pitch_extractors}/parsel_mouth.py (100%) rename fish_diffusion/{feature_extractors/pitch => modules/pitch_extractors}/world.py (100%) delete mode 100644 fish_diffusion/modules/positional_embedding.py rename fish_diffusion/{ => modules}/vocoders/__init__.py (100%) rename fish_diffusion/{ => modules}/vocoders/builder.py (100%) rename fish_diffusion/{ => modules}/vocoders/istft_net/istft_net.py (100%) rename fish_diffusion/{ => modules}/vocoders/istft_net/mel.py (100%) rename fish_diffusion/{ => modules}/vocoders/istft_net/models.py (100%) rename fish_diffusion/{ => modules}/vocoders/nsf_hifigan/__init__.py (100%) rename fish_diffusion/{ => modules}/vocoders/nsf_hifigan/models.py (100%) rename fish_diffusion/{ => modules}/vocoders/nsf_hifigan/nsf_hifigan.py (100%) rename fish_diffusion/{denoisers => modules}/wavenet.py (97%) create mode 100644 fish_diffusion/schedulers/__init__.py rename fish_diffusion/{utils/scheduler.py => schedulers/builder.py} (100%) delete mode 100644 fish_diffusion/utils/ssim.py delete mode 100644 inference.py delete mode 100644 tools/batch_inference.py rename tools/{ => diffusion}/clean_speaker_embeddings.py (100%) rename tools/{ => diffusion}/diff_svc_converter.py (99%) rename tools/{vst_flask_api.py => diffusion/flask_api.py} (50%) create mode 100644 tools/diffusion/gradio_ui.py create mode 100644 tools/diffusion/inference.py rename inference_svs.py => tools/diffusion/inference_svs.py (94%) rename inference_svs_ds.py => tools/diffusion/inference_svs_ds.py (96%) rename tools/{ => diffusion}/onnx/demo.py (90%) rename tools/{ => diffusion}/onnx/export.py (95%) rename tools/{ => diffusion}/onnx/export_moess.py (96%) create mode 100644 tools/diffusion/train.py diff --git a/README.en.md b/README.en.md index 52e51c23..fb335982 100644 --- a/README.en.md +++ b/README.en.md @@ -99,34 +99,34 @@ python tools/preprocessing/extract_features.py --config configs/svc_hubert_soft. ```bash # Single machine single card / multi-card training -python train.py --config configs/svc_hubert_soft.py +python tools/diffusion/train.py --config configs/svc_hubert_soft.py # Resume training -python train.py --config configs/svc_hubert_soft.py --resume [checkpoint] +python tools/diffusion/train.py --config configs/svc_hubert_soft.py --resume [checkpoint] # Fine-tune the pre-trained model # Note: You should adjust the learning rate scheduler in the config file to warmup_cosine_finetune -python train.py --config configs/svc_cn_hubert_soft_finetune.py --pretrained [checkpoint] +python tools/diffusion/train.py --config configs/svc_cn_hubert_soft_finetune.py --pretrained [checkpoint] ``` ## Inference ```bash # Inference using shell, you can use --help to view more parameters -python inference.py --config [config] \ +python tools/diffusion/inference.py --config [config] \ --checkpoint [checkpoint] \ --input [input audio] \ --output [output audio] # Gradio Web Inference, other parameters will be used as gradio default parameters -python inference.py --config [config] \ +python tools/diffusion/inference.py --config [config] \ --checkpoint [checkpoint] \ --gradio ``` ## Convert a DiffSVC model to Fish Diffusion ```bash -python tools/diff_svc_converter.py --config configs/svc_hubert_soft_diff_svc.py \ +python tools/diffusion/diff_svc_converter.py --config configs/svc_hubert_soft_diff_svc.py \ --input-path [DiffSVC ckpt] \ --output-path [Fish Diffusion ckpt] ``` diff --git a/README.md b/README.md index 2539d767..91083498 100644 --- a/README.md +++ b/README.md @@ -118,34 +118,34 @@ python tools/preprocessing/extract_features.py --config configs/svc_hubert_soft. ```bash # 单机单卡 / 单机多卡训练 -python train.py --config configs/svc_hubert_soft.py +python tools/diffusion/train.py --config configs/svc_hubert_soft.py # 继续训练 -python train.py --config configs/svc_hubert_soft.py --resume [checkpoint] +python tools/diffusion/train.py --config configs/svc_hubert_soft.py --resume [checkpoint] # 微调预训练模型 # 注意: 你应该调整配置文件中的学习率调度器为 warmup_cosine_finetune -python train.py --config configs/svc_cn_hubert_soft_finetune.py --pretrained [checkpoint] +python tools/diffusion/train.py --config configs/svc_cn_hubert_soft_finetune.py --pretrained [checkpoint] ``` ## 推理 ```bash # 命令行推理, 你可以使用 --help 查看更多参数 -python inference.py --config [config] \ +python tools/diffusion/inference.py --config [config] \ --checkpoint [checkpoint] \ --input [input audio] \ --output [output audio] # Gradio Web 推理, 其他参数会被转为 Gradio 默认参数 -python inference.py --config [config] \ +python tools/diffusion/inference.py --config [config] \ --checkpoint [checkpoint] \ --gradio ``` ## 将 DiffSVC 模型转换为 Fish Diffusion 模型 ```bash -python tools/diff_svc_converter.py --config configs/svc_hubert_soft_diff_svc.py \ +python tools/diffusion/diff_svc_converter.py --config configs/svc_hubert_soft_diff_svc.py \ --input-path [DiffSVC ckpt] \ --output-path [Fish Diffusion ckpt] ``` diff --git a/fish_diffusion/archs/diffsinger.py b/fish_diffusion/archs/diffsinger.py deleted file mode 100644 index fb823329..00000000 --- a/fish_diffusion/archs/diffsinger.py +++ /dev/null @@ -1,109 +0,0 @@ -import torch -import torch.nn as nn - -from fish_diffusion.diffusions import DIFFUSIONS -from fish_diffusion.encoders import ENCODERS - - -class DiffSinger(nn.Module): - """DiffSinger""" - - def __init__(self, model_config): - super(DiffSinger, self).__init__() - - self.text_encoder = ENCODERS.build(model_config.text_encoder) - self.diffusion = DIFFUSIONS.build(model_config.diffusion) - self.speaker_encoder = ENCODERS.build(model_config.speaker_encoder) - self.pitch_encoder = ENCODERS.build(model_config.pitch_encoder) - - if "pitch_shift_encoder" in model_config: - self.pitch_shift_encoder = ENCODERS.build(model_config.pitch_shift_encoder) - - @staticmethod - def get_mask_from_lengths(lengths, max_len=None): - batch_size = lengths.shape[0] - if max_len is None: - max_len = torch.max(lengths).item() - - ids = ( - torch.arange(0, max_len) - .unsqueeze(0) - .expand(batch_size, -1) - .to(lengths.device) - ) - mask = ids >= lengths.unsqueeze(1).expand(-1, max_len) - - return mask - - def forward_features( - self, - speakers, - contents, - contents_lens, - contents_max_len, - mel_lens=None, - mel_max_len=None, - pitches=None, - pitch_shift=None, - ): - src_masks = ( - self.get_mask_from_lengths(contents_lens, contents_max_len) - if contents_lens is not None - else None - ) - - features = self.text_encoder(contents, src_masks) - - speaker_embed = ( - self.speaker_encoder(speakers).unsqueeze(1).expand(-1, contents_max_len, -1) - ) - - features += speaker_embed - features += self.pitch_encoder(pitches) - - if pitch_shift is not None and hasattr(self, "pitch_shift_encoder"): - features += self.pitch_shift_encoder(pitch_shift)[:, None] - - mel_masks = ( - self.get_mask_from_lengths(mel_lens, mel_max_len) - if mel_lens is not None - else None - ) - - return dict( - features=features, - src_masks=src_masks, - mel_masks=mel_masks, - ) - - def forward( - self, - speakers, - contents, - contents_lens, - contents_max_len, - mel=None, - mel_lens=None, - mel_max_len=None, - pitches=None, - pitch_shift=None, - ): - features = self.forward_features( - speakers=speakers, - contents=contents, - contents_lens=contents_lens, - contents_max_len=contents_max_len, - mel_lens=mel_lens, - mel_max_len=mel_max_len, - pitches=pitches, - pitch_shift=pitch_shift, - ) - - output_dict = self.diffusion.train_step( - features["features"], mel, features["mel_masks"] - ) - - # For validation - output_dict["features"] = features["features"] - - return output_dict diff --git a/train.py b/fish_diffusion/archs/diffsinger/diffsinger.py similarity index 57% rename from train.py rename to fish_diffusion/archs/diffsinger/diffsinger.py index a9d4c941..c0a7ec10 100644 --- a/train.py +++ b/fish_diffusion/archs/diffsinger/diffsinger.py @@ -1,24 +1,125 @@ -from argparse import ArgumentParser - import matplotlib.pyplot as plt import pytorch_lightning as pl import torch +import torch.nn as nn import wandb -from loguru import logger -from mmengine import Config from mmengine.optim import OPTIMIZERS from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger -from fish_diffusion.archs.diffsinger import DiffSinger -from fish_diffusion.datasets.utils import build_loader_from_config -from fish_diffusion.utils.scheduler import LR_SCHEUDLERS +from fish_diffusion.modules.encoders import ENCODERS +from fish_diffusion.modules.vocoders import VOCODERS +from fish_diffusion.modules.vocoders.builder import VOCODERS +from fish_diffusion.schedulers import LR_SCHEUDLERS from fish_diffusion.utils.viz import viz_synth_sample -from fish_diffusion.vocoders import VOCODERS -torch.set_float32_matmul_precision("medium") +from .diffusions import DIFFUSIONS + + +class DiffSinger(nn.Module): + """DiffSinger""" + + def __init__(self, model_config): + super(DiffSinger, self).__init__() + + self.text_encoder = ENCODERS.build(model_config.text_encoder) + self.diffusion = DIFFUSIONS.build(model_config.diffusion) + self.speaker_encoder = ENCODERS.build(model_config.speaker_encoder) + self.pitch_encoder = ENCODERS.build(model_config.pitch_encoder) + + if "pitch_shift_encoder" in model_config: + self.pitch_shift_encoder = ENCODERS.build(model_config.pitch_shift_encoder) + + @staticmethod + def get_mask_from_lengths(lengths, max_len=None): + batch_size = lengths.shape[0] + if max_len is None: + max_len = torch.max(lengths).item() + + ids = ( + torch.arange(0, max_len) + .unsqueeze(0) + .expand(batch_size, -1) + .to(lengths.device) + ) + mask = ids >= lengths.unsqueeze(1).expand(-1, max_len) + + return mask + + def forward_features( + self, + speakers, + contents, + contents_lens, + contents_max_len, + mel_lens=None, + mel_max_len=None, + pitches=None, + pitch_shift=None, + ): + src_masks = ( + self.get_mask_from_lengths(contents_lens, contents_max_len) + if contents_lens is not None + else None + ) + + features = self.text_encoder(contents, src_masks) + + speaker_embed = ( + self.speaker_encoder(speakers).unsqueeze(1).expand(-1, contents_max_len, -1) + ) + + features += speaker_embed + features += self.pitch_encoder(pitches) + + if pitch_shift is not None and hasattr(self, "pitch_shift_encoder"): + features += self.pitch_shift_encoder(pitch_shift)[:, None] + + mel_masks = ( + self.get_mask_from_lengths(mel_lens, mel_max_len) + if mel_lens is not None + else None + ) + + return dict( + features=features, + src_masks=src_masks, + mel_masks=mel_masks, + ) + + def forward( + self, + speakers, + contents, + contents_lens, + contents_max_len, + mel=None, + mel_lens=None, + mel_max_len=None, + pitches=None, + pitch_shift=None, + ): + features = self.forward_features( + speakers=speakers, + contents=contents, + contents_lens=contents_lens, + contents_max_len=contents_max_len, + mel_lens=mel_lens, + mel_max_len=mel_max_len, + pitches=pitches, + pitch_shift=pitch_shift, + ) + + output_dict = self.diffusion.train_step( + features["features"], mel, features["mel_masks"] + ) + # For validation + output_dict["features"] = features["features"] -class FishDiffusion(pl.LightningModule): + return output_dict + + +class DiffSingerLightning(pl.LightningModule): def __init__(self, config): super().__init__() self.save_hyperparameters() @@ -129,84 +230,3 @@ def training_step(self, batch, batch_idx): def validation_step(self, batch, batch_idx): return self._step(batch, batch_idx, mode="valid") - - -if __name__ == "__main__": - pl.seed_everything(42, workers=True) - - parser = ArgumentParser() - parser.add_argument("--config", type=str, required=True) - parser.add_argument("--resume", type=str, default=None) - parser.add_argument( - "--tensorboard", - action="store_true", - default=False, - help="Use tensorboard logger, default is wandb.", - ) - parser.add_argument("--resume-id", type=str, default=None, help="Wandb run id.") - parser.add_argument("--entity", type=str, default=None, help="Wandb entity.") - parser.add_argument("--name", type=str, default=None, help="Wandb run name.") - parser.add_argument( - "--pretrained", type=str, default=None, help="Pretrained model." - ) - parser.add_argument( - "--only-train-speaker-embeddings", - action="store_true", - default=False, - help="Only train speaker embeddings.", - ) - - args = parser.parse_args() - - cfg = Config.fromfile(args.config) - - model = FishDiffusion(cfg) - - # We only load the state_dict of the model, not the optimizer. - if args.pretrained: - state_dict = torch.load(args.pretrained, map_location="cpu") - if "state_dict" in state_dict: - state_dict = state_dict["state_dict"] - - result = model.load_state_dict(state_dict, strict=False) - - missing_keys = set(result.missing_keys) - unexpected_keys = set(result.unexpected_keys) - - # Make sure incorrect keys are just noise predictor keys. - unexpected_keys = unexpected_keys - set( - i.replace(".naive_noise_predictor.", ".") for i in missing_keys - ) - - assert len(unexpected_keys) == 0 - - if args.only_train_speaker_embeddings: - for name, param in model.named_parameters(): - if "speaker_encoder" not in name: - param.requires_grad = False - - logger.info( - "Only train speaker embeddings, all other parameters are frozen." - ) - - logger = ( - TensorBoardLogger("logs", name=cfg.model.type) - if args.tensorboard - else WandbLogger( - project=cfg.model.type, - save_dir="logs", - log_model=True, - name=args.name, - entity=args.entity, - resume="must" if args.resume_id else False, - id=args.resume_id, - ) - ) - - trainer = pl.Trainer( - logger=logger, - **cfg.trainer, - ) - - train_loader, valid_loader = build_loader_from_config(cfg, trainer.num_devices) - trainer.fit(model, train_loader, valid_loader, ckpt_path=args.resume) diff --git a/fish_diffusion/diffusions/__init__.py b/fish_diffusion/archs/diffsinger/diffusions/__init__.py similarity index 100% rename from fish_diffusion/diffusions/__init__.py rename to fish_diffusion/archs/diffsinger/diffusions/__init__.py diff --git a/fish_diffusion/archs/diffsinger/diffusions/builder.py b/fish_diffusion/archs/diffsinger/diffusions/builder.py new file mode 100644 index 00000000..cd8a5dc1 --- /dev/null +++ b/fish_diffusion/archs/diffsinger/diffusions/builder.py @@ -0,0 +1,8 @@ +from mmengine import Registry + +from fish_diffusion.modules.wavenet import WaveNet + +DIFFUSIONS = Registry("diffusions") +DENOISERS = Registry("denoisers") + +DENOISERS.register_module(name="WaveNetDenoiser", module=WaveNet) diff --git a/fish_diffusion/diffusions/diffusion.py b/fish_diffusion/archs/diffsinger/diffusions/diffusion.py similarity index 95% rename from fish_diffusion/diffusions/diffusion.py rename to fish_diffusion/archs/diffsinger/diffusions/diffusion.py index 5e46ba5c..393428ad 100644 --- a/fish_diffusion/diffusions/diffusion.py +++ b/fish_diffusion/archs/diffsinger/diffusions/diffusion.py @@ -1,5 +1,4 @@ import json -from collections import deque from functools import partial import numpy as np @@ -8,14 +7,8 @@ from torch import nn from tqdm import tqdm -from fish_diffusion.denoisers import DENOISERS -from fish_diffusion.diffusions.noise_predictor import ( - NaiveNoisePredictor, - PLMSNoisePredictor, -) -from fish_diffusion.utils.ssim import ssim_loss - -from .builder import DIFFUSIONS +from .builder import DENOISERS, DIFFUSIONS +from .noise_predictor import NaiveNoisePredictor, PLMSNoisePredictor def get_noise_schedule_list(schedule_mode, timesteps, max_beta=0.01, s=0.008): @@ -157,9 +150,6 @@ def get_mel_loss(self, loss_fn, noise, epsilon): loss = F.smooth_l1_loss(noise, epsilon) elif loss_fn == "l2": loss = F.mse_loss(noise, epsilon) - elif loss_fn == "ssim": - # There is a bug we need to fix in the SSIM implementation - loss = ssim_loss(noise, epsilon) elif callable(loss_fn): loss = loss_fn(noise, epsilon) else: diff --git a/fish_diffusion/diffusions/noise_predictor.py b/fish_diffusion/archs/diffsinger/diffusions/noise_predictor.py similarity index 100% rename from fish_diffusion/diffusions/noise_predictor.py rename to fish_diffusion/archs/diffsinger/diffusions/noise_predictor.py diff --git a/fish_diffusion/denoisers/__init__.py b/fish_diffusion/denoisers/__init__.py deleted file mode 100644 index eba5d3ff..00000000 --- a/fish_diffusion/denoisers/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .attention import AttentionDenoiser -from .builder import DENOISERS -from .wavenet import WaveNetDenoiser - -__all__ = ["DENOISERS", "WaveNetDenoiser", "AttentionDenoiser"] diff --git a/fish_diffusion/denoisers/attention.py b/fish_diffusion/denoisers/attention.py deleted file mode 100644 index 7e9a85ff..00000000 --- a/fish_diffusion/denoisers/attention.py +++ /dev/null @@ -1,57 +0,0 @@ -from typing import Iterable - -from torch import nn -from torch.nn import functional as F -from whisper.model import LayerNorm, ResidualAttentionBlock, sinusoids - -from fish_diffusion.denoisers.wavenet import DiffusionEmbedding - -from .builder import DENOISERS - - -@DENOISERS.register_module() -class AttentionDenoiser(nn.Module): - def __init__( - self, - mel_channels: int = 128, - condition_channels: int = 128, - hidden_size: int = 256, - n_head: int = 4, - n_layer: int = 4, - max_len: int = 10000, - ): - super().__init__() - - self.mel_proj = nn.Linear(mel_channels, hidden_size) - self.diffusion_embedding = DiffusionEmbedding(hidden_size) - self.condition_proj = nn.Linear(condition_channels, hidden_size) - - self.register_buffer("positional_embedding", sinusoids(max_len, hidden_size)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(hidden_size, n_head) for _ in range(n_layer)] - ) - - self.ln_post = LayerNorm(hidden_size) - self.output_proj = nn.Linear(hidden_size, mel_channels) - - def forward(self, x, diffusion_step, conditioner): - """ - - :param x: [B, M, T] - :param diffusion_step: [B,] - :param conditioner: [B, M, T] - :return: - """ - - x = F.gelu(self.mel_proj(x.transpose(1, 2))) - step = self.diffusion_embedding(diffusion_step)[:, None] - positional_embedding = self.positional_embedding[None, : x.shape[1]] - condition = F.gelu(self.condition_proj(conditioner.transpose(1, 2))) - - for block in self.blocks: - x = block(x + step + condition + positional_embedding) - - x = self.ln_post(x) - - return self.output_proj(x).transpose(1, 2) diff --git a/fish_diffusion/denoisers/builder.py b/fish_diffusion/denoisers/builder.py deleted file mode 100644 index d7558a09..00000000 --- a/fish_diffusion/denoisers/builder.py +++ /dev/null @@ -1,3 +0,0 @@ -from mmengine import Registry - -DENOISERS = Registry("denoiser") diff --git a/fish_diffusion/diffusions/builder.py b/fish_diffusion/diffusions/builder.py deleted file mode 100644 index c8975d36..00000000 --- a/fish_diffusion/diffusions/builder.py +++ /dev/null @@ -1,3 +0,0 @@ -from mmengine import Registry - -DIFFUSIONS = Registry("diffusions") diff --git a/fish_diffusion/encoders/__init__.py b/fish_diffusion/modules/encoders/__init__.py similarity index 100% rename from fish_diffusion/encoders/__init__.py rename to fish_diffusion/modules/encoders/__init__.py diff --git a/fish_diffusion/encoders/attention.py b/fish_diffusion/modules/encoders/attention.py similarity index 100% rename from fish_diffusion/encoders/attention.py rename to fish_diffusion/modules/encoders/attention.py diff --git a/fish_diffusion/encoders/builder.py b/fish_diffusion/modules/encoders/builder.py similarity index 100% rename from fish_diffusion/encoders/builder.py rename to fish_diffusion/modules/encoders/builder.py diff --git a/fish_diffusion/encoders/fast_speech.py b/fish_diffusion/modules/encoders/fast_speech.py similarity index 76% rename from fish_diffusion/encoders/fast_speech.py rename to fish_diffusion/modules/encoders/fast_speech.py index 6ee20678..107ff767 100644 --- a/fish_diffusion/encoders/fast_speech.py +++ b/fish_diffusion/modules/encoders/fast_speech.py @@ -4,14 +4,210 @@ import torch.nn as nn import torch.nn.functional as F -from fish_diffusion.modules.positional_embedding import ( - RelPositionalEncoding, - SinusoidalPositionalEmbedding, -) - from .builder import ENCODERS +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + reverse (bool): Whether to reverse the input position. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model) + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class ScaledPositionalEncoding(PositionalEncoding): + """Scaled positional encoding module. + See Sec. 3.2 https://arxiv.org/abs/1809.08895 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) + self.alpha = torch.nn.Parameter(torch.tensor(1.0)) + + def reset_parameters(self): + """Reset parameters.""" + self.alpha.data = torch.tensor(1.0) + + def forward(self, x): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x + self.alpha * self.pe[:, : x.size(1)] + return self.dropout(x) + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward(self, x): + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.pe[:, : x.size(1)] + + return self.dropout(x) + self.dropout(pos_emb) + + +class SinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length. + + Padding symbols are ignored. + """ + + def __init__(self, embedding_dim, padding_idx, init_size=1024): + super().__init__() + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.weights = SinusoidalPositionalEmbedding.get_embedding( + init_size, + embedding_dim, + padding_idx, + ) + self.register_buffer("_float_tensor", torch.FloatTensor(1)) + + @staticmethod + def get_embedding(num_embeddings, embedding_dim, padding_idx=None): + """Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( + 1 + ) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( + num_embeddings, -1 + ) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + return emb + + @staticmethod + def make_positions(tensor, padding_idx): + """Replace non-padding symbols with their position numbers. + + Position numbers begin at padding_idx+1. Padding symbols are ignored. + """ + # The series of casts and type-conversions here are carefully + # balanced to both work with ONNX export and XLA. In particular XLA + # prefers ints, cumsum defaults to output longs, and ONNX doesn"t know + # how to handle the dtype kwarg in cumsum. + mask = tensor.ne(padding_idx).int() + return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx + + def forward( + self, input, incremental_state=None, timestep=None, positions=None, **kwargs + ): + """Input is expected to be of size [bsz x seqlen].""" + bsz, seq_len = input.shape[:2] + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.size(0): + # recompute/expand embeddings if needed + self.weights = SinusoidalPositionalEmbedding.get_embedding( + max_pos, + self.embedding_dim, + self.padding_idx, + ) + self.weights = self.weights.to(self._float_tensor) + + if incremental_state is not None: + # positions is the same for every token when decoding a single step + pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len + return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) + + positions = ( + self.make_positions(input, self.padding_idx) + if positions is None + else positions + ) + return ( + self.weights.index_select(0, positions.view(-1)) + .view(bsz, seq_len, -1) + .detach() + ) + + def max_positions(self): + """Maximum number of supported positions.""" + return int(1e5) # an arbitrary large number + + class Swish(torch.autograd.Function): @staticmethod def forward(ctx, i): diff --git a/fish_diffusion/encoders/identity.py b/fish_diffusion/modules/encoders/identity.py similarity index 100% rename from fish_diffusion/encoders/identity.py rename to fish_diffusion/modules/encoders/identity.py diff --git a/fish_diffusion/encoders/naive_projection.py b/fish_diffusion/modules/encoders/naive_projection.py similarity index 100% rename from fish_diffusion/encoders/naive_projection.py rename to fish_diffusion/modules/encoders/naive_projection.py diff --git a/fish_diffusion/feature_extractors/__init__.py b/fish_diffusion/modules/feature_extractors/__init__.py similarity index 88% rename from fish_diffusion/feature_extractors/__init__.py rename to fish_diffusion/modules/feature_extractors/__init__.py index bcfb16f6..536d7762 100644 --- a/fish_diffusion/feature_extractors/__init__.py +++ b/fish_diffusion/modules/feature_extractors/__init__.py @@ -3,12 +3,10 @@ from .content_vec import ContentVec from .hubert_soft import HubertSoft from .opencpop_transcription import OpenCpopTranscriptionToPhonemesDuration -from .pitch import PITCH_EXTRACTORS from .whisper import AlignedWhisper __all__ = [ "FEATURE_EXTRACTORS", - "PITCH_EXTRACTORS", "ChineseHubertSoft", "HubertSoft", "OpenCpopTranscriptionToPhonemesDuration", diff --git a/fish_diffusion/feature_extractors/base.py b/fish_diffusion/modules/feature_extractors/base.py similarity index 97% rename from fish_diffusion/feature_extractors/base.py rename to fish_diffusion/modules/feature_extractors/base.py index bc1bb50c..21811279 100644 --- a/fish_diffusion/feature_extractors/base.py +++ b/fish_diffusion/modules/feature_extractors/base.py @@ -1,4 +1,3 @@ -import librosa import torchaudio from torch import nn diff --git a/fish_diffusion/feature_extractors/builder.py b/fish_diffusion/modules/feature_extractors/builder.py similarity index 100% rename from fish_diffusion/feature_extractors/builder.py rename to fish_diffusion/modules/feature_extractors/builder.py diff --git a/fish_diffusion/feature_extractors/chinese_hubert.py b/fish_diffusion/modules/feature_extractors/chinese_hubert.py similarity index 100% rename from fish_diffusion/feature_extractors/chinese_hubert.py rename to fish_diffusion/modules/feature_extractors/chinese_hubert.py diff --git a/fish_diffusion/feature_extractors/content_vec.py b/fish_diffusion/modules/feature_extractors/content_vec.py similarity index 100% rename from fish_diffusion/feature_extractors/content_vec.py rename to fish_diffusion/modules/feature_extractors/content_vec.py diff --git a/fish_diffusion/feature_extractors/hubert_soft.py b/fish_diffusion/modules/feature_extractors/hubert_soft.py similarity index 100% rename from fish_diffusion/feature_extractors/hubert_soft.py rename to fish_diffusion/modules/feature_extractors/hubert_soft.py diff --git a/fish_diffusion/feature_extractors/opencpop_transcription.py b/fish_diffusion/modules/feature_extractors/opencpop_transcription.py similarity index 100% rename from fish_diffusion/feature_extractors/opencpop_transcription.py rename to fish_diffusion/modules/feature_extractors/opencpop_transcription.py diff --git a/fish_diffusion/feature_extractors/whisper.py b/fish_diffusion/modules/feature_extractors/whisper.py similarity index 100% rename from fish_diffusion/feature_extractors/whisper.py rename to fish_diffusion/modules/feature_extractors/whisper.py diff --git a/fish_diffusion/feature_extractors/pitch/__init__.py b/fish_diffusion/modules/pitch_extractors/__init__.py similarity index 100% rename from fish_diffusion/feature_extractors/pitch/__init__.py rename to fish_diffusion/modules/pitch_extractors/__init__.py diff --git a/fish_diffusion/feature_extractors/pitch/builder.py b/fish_diffusion/modules/pitch_extractors/builder.py similarity index 100% rename from fish_diffusion/feature_extractors/pitch/builder.py rename to fish_diffusion/modules/pitch_extractors/builder.py diff --git a/fish_diffusion/feature_extractors/pitch/crepe.py b/fish_diffusion/modules/pitch_extractors/crepe.py similarity index 100% rename from fish_diffusion/feature_extractors/pitch/crepe.py rename to fish_diffusion/modules/pitch_extractors/crepe.py diff --git a/fish_diffusion/feature_extractors/pitch/parsel_mouth.py b/fish_diffusion/modules/pitch_extractors/parsel_mouth.py similarity index 100% rename from fish_diffusion/feature_extractors/pitch/parsel_mouth.py rename to fish_diffusion/modules/pitch_extractors/parsel_mouth.py diff --git a/fish_diffusion/feature_extractors/pitch/world.py b/fish_diffusion/modules/pitch_extractors/world.py similarity index 100% rename from fish_diffusion/feature_extractors/pitch/world.py rename to fish_diffusion/modules/pitch_extractors/world.py diff --git a/fish_diffusion/modules/positional_embedding.py b/fish_diffusion/modules/positional_embedding.py deleted file mode 100644 index 7deb8272..00000000 --- a/fish_diffusion/modules/positional_embedding.py +++ /dev/null @@ -1,205 +0,0 @@ -import math - -import torch -from torch import nn - - -class PositionalEncoding(torch.nn.Module): - """Positional encoding. - Args: - d_model (int): Embedding dimension. - dropout_rate (float): Dropout rate. - max_len (int): Maximum input length. - reverse (bool): Whether to reverse the input position. - """ - - def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): - """Construct an PositionalEncoding object.""" - super(PositionalEncoding, self).__init__() - self.d_model = d_model - self.reverse = reverse - self.xscale = math.sqrt(self.d_model) - self.dropout = torch.nn.Dropout(p=dropout_rate) - self.pe = None - self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - - def extend_pe(self, x): - """Reset the positional encodings.""" - if self.pe is not None: - if self.pe.size(1) >= x.size(1): - if self.pe.dtype != x.dtype or self.pe.device != x.device: - self.pe = self.pe.to(dtype=x.dtype, device=x.device) - return - pe = torch.zeros(x.size(1), self.d_model) - if self.reverse: - position = torch.arange( - x.size(1) - 1, -1, -1.0, dtype=torch.float32 - ).unsqueeze(1) - else: - position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, self.d_model, 2, dtype=torch.float32) - * -(math.log(10000.0) / self.d_model) - ) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) - self.pe = pe.to(device=x.device, dtype=x.dtype) - - def forward(self, x: torch.Tensor): - """Add positional encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - """ - self.extend_pe(x) - x = x * self.xscale + self.pe[:, : x.size(1)] - return self.dropout(x) - - -class ScaledPositionalEncoding(PositionalEncoding): - """Scaled positional encoding module. - See Sec. 3.2 https://arxiv.org/abs/1809.08895 - Args: - d_model (int): Embedding dimension. - dropout_rate (float): Dropout rate. - max_len (int): Maximum input length. - """ - - def __init__(self, d_model, dropout_rate, max_len=5000): - """Initialize class.""" - super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) - self.alpha = torch.nn.Parameter(torch.tensor(1.0)) - - def reset_parameters(self): - """Reset parameters.""" - self.alpha.data = torch.tensor(1.0) - - def forward(self, x): - """Add positional encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - """ - self.extend_pe(x) - x = x + self.alpha * self.pe[:, : x.size(1)] - return self.dropout(x) - - -class RelPositionalEncoding(PositionalEncoding): - """Relative positional encoding module. - See : Appendix B in https://arxiv.org/abs/1901.02860 - Args: - d_model (int): Embedding dimension. - dropout_rate (float): Dropout rate. - max_len (int): Maximum input length. - """ - - def __init__(self, d_model, dropout_rate, max_len=5000): - """Initialize class.""" - super().__init__(d_model, dropout_rate, max_len, reverse=True) - - def forward(self, x): - """Compute positional encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - torch.Tensor: Positional embedding tensor (1, time, `*`). - """ - self.extend_pe(x) - x = x * self.xscale - pos_emb = self.pe[:, : x.size(1)] - - return self.dropout(x) + self.dropout(pos_emb) - - -class SinusoidalPositionalEmbedding(nn.Module): - """This module produces sinusoidal positional embeddings of any length. - - Padding symbols are ignored. - """ - - def __init__(self, embedding_dim, padding_idx, init_size=1024): - super().__init__() - self.embedding_dim = embedding_dim - self.padding_idx = padding_idx - self.weights = SinusoidalPositionalEmbedding.get_embedding( - init_size, - embedding_dim, - padding_idx, - ) - self.register_buffer("_float_tensor", torch.FloatTensor(1)) - - @staticmethod - def get_embedding(num_embeddings, embedding_dim, padding_idx=None): - """Build sinusoidal embeddings. - - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) - emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( - 1 - ) * emb.unsqueeze(0) - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( - num_embeddings, -1 - ) - if embedding_dim % 2 == 1: - # zero pad - emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) - if padding_idx is not None: - emb[padding_idx, :] = 0 - return emb - - @staticmethod - def make_positions(tensor, padding_idx): - """Replace non-padding symbols with their position numbers. - - Position numbers begin at padding_idx+1. Padding symbols are ignored. - """ - # The series of casts and type-conversions here are carefully - # balanced to both work with ONNX export and XLA. In particular XLA - # prefers ints, cumsum defaults to output longs, and ONNX doesn"t know - # how to handle the dtype kwarg in cumsum. - mask = tensor.ne(padding_idx).int() - return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx - - def forward( - self, input, incremental_state=None, timestep=None, positions=None, **kwargs - ): - """Input is expected to be of size [bsz x seqlen].""" - bsz, seq_len = input.shape[:2] - max_pos = self.padding_idx + 1 + seq_len - if self.weights is None or max_pos > self.weights.size(0): - # recompute/expand embeddings if needed - self.weights = SinusoidalPositionalEmbedding.get_embedding( - max_pos, - self.embedding_dim, - self.padding_idx, - ) - self.weights = self.weights.to(self._float_tensor) - - if incremental_state is not None: - # positions is the same for every token when decoding a single step - pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len - return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) - - positions = ( - self.make_positions(input, self.padding_idx) - if positions is None - else positions - ) - return ( - self.weights.index_select(0, positions.view(-1)) - .view(bsz, seq_len, -1) - .detach() - ) - - def max_positions(self): - """Maximum number of supported positions.""" - return int(1e5) # an arbitrary large number diff --git a/fish_diffusion/vocoders/__init__.py b/fish_diffusion/modules/vocoders/__init__.py similarity index 100% rename from fish_diffusion/vocoders/__init__.py rename to fish_diffusion/modules/vocoders/__init__.py diff --git a/fish_diffusion/vocoders/builder.py b/fish_diffusion/modules/vocoders/builder.py similarity index 100% rename from fish_diffusion/vocoders/builder.py rename to fish_diffusion/modules/vocoders/builder.py diff --git a/fish_diffusion/vocoders/istft_net/istft_net.py b/fish_diffusion/modules/vocoders/istft_net/istft_net.py similarity index 100% rename from fish_diffusion/vocoders/istft_net/istft_net.py rename to fish_diffusion/modules/vocoders/istft_net/istft_net.py diff --git a/fish_diffusion/vocoders/istft_net/mel.py b/fish_diffusion/modules/vocoders/istft_net/mel.py similarity index 100% rename from fish_diffusion/vocoders/istft_net/mel.py rename to fish_diffusion/modules/vocoders/istft_net/mel.py diff --git a/fish_diffusion/vocoders/istft_net/models.py b/fish_diffusion/modules/vocoders/istft_net/models.py similarity index 100% rename from fish_diffusion/vocoders/istft_net/models.py rename to fish_diffusion/modules/vocoders/istft_net/models.py diff --git a/fish_diffusion/vocoders/nsf_hifigan/__init__.py b/fish_diffusion/modules/vocoders/nsf_hifigan/__init__.py similarity index 100% rename from fish_diffusion/vocoders/nsf_hifigan/__init__.py rename to fish_diffusion/modules/vocoders/nsf_hifigan/__init__.py diff --git a/fish_diffusion/vocoders/nsf_hifigan/models.py b/fish_diffusion/modules/vocoders/nsf_hifigan/models.py similarity index 100% rename from fish_diffusion/vocoders/nsf_hifigan/models.py rename to fish_diffusion/modules/vocoders/nsf_hifigan/models.py diff --git a/fish_diffusion/vocoders/nsf_hifigan/nsf_hifigan.py b/fish_diffusion/modules/vocoders/nsf_hifigan/nsf_hifigan.py similarity index 100% rename from fish_diffusion/vocoders/nsf_hifigan/nsf_hifigan.py rename to fish_diffusion/modules/vocoders/nsf_hifigan/nsf_hifigan.py diff --git a/fish_diffusion/denoisers/wavenet.py b/fish_diffusion/modules/wavenet.py similarity index 97% rename from fish_diffusion/denoisers/wavenet.py rename to fish_diffusion/modules/wavenet.py index dd5e66ad..2028d2cb 100644 --- a/fish_diffusion/denoisers/wavenet.py +++ b/fish_diffusion/modules/wavenet.py @@ -4,8 +4,6 @@ import torch.nn.functional as F from torch import nn -from .builder import DENOISERS - class Mish(nn.Module): def forward(self, x): @@ -151,9 +149,11 @@ def forward(self, x): return x -@DENOISERS.register_module() -class WaveNetDenoiser(nn.Module): - """Conditional Diffusion Denoiser""" +class WaveNet(nn.Module): + """ + WaveNet + https://www.deepmind.com/blog/wavenet-a-generative-model-for-raw-audio + """ def __init__( self, @@ -164,7 +164,7 @@ def __init__( use_linear_bias=False, dilation_cycle=None, ): - super(WaveNetDenoiser, self).__init__() + super(WaveNet, self).__init__() self.input_projection = ConvNorm(mel_channels, residual_channels, kernel_size=1) self.diffusion_embedding = DiffusionEmbedding(residual_channels) diff --git a/fish_diffusion/schedulers/__init__.py b/fish_diffusion/schedulers/__init__.py new file mode 100644 index 00000000..13444963 --- /dev/null +++ b/fish_diffusion/schedulers/__init__.py @@ -0,0 +1,3 @@ +from .builder import LR_SCHEUDLERS + +__all__ = ["LR_SCHEUDLERS"] diff --git a/fish_diffusion/utils/scheduler.py b/fish_diffusion/schedulers/builder.py similarity index 100% rename from fish_diffusion/utils/scheduler.py rename to fish_diffusion/schedulers/builder.py diff --git a/fish_diffusion/utils/audio.py b/fish_diffusion/utils/audio.py index b583a049..79c32eb8 100644 --- a/fish_diffusion/utils/audio.py +++ b/fish_diffusion/utils/audio.py @@ -1,9 +1,10 @@ import math -from typing import Iterable +from typing import Iterable, Union import librosa import numpy as np import torch +from fish_audio_preprocess.utils import loudness_norm, separate_audio from torchaudio.transforms import MelSpectrogram @@ -116,3 +117,31 @@ def slice_audio( for i in range(start, end, chunk_size): yield i, i + chunk_size + + +def separate_vocals( + audio: np.ndarray, sr: int, device: Union[str, torch.device] = "cpu" +): + model = separate_audio.init_model("htdemucs", device=device) + audio = librosa.resample(audio, orig_sr=sr, target_sr=model.samplerate)[None] + + # To two channels + audio = np.concatenate([audio, audio], axis=0) + audio = torch.from_numpy(audio).to(device) + tracks = separate_audio.separate_audio( + model, audio, shifts=1, num_workers=0, progress=True + ) + audio = separate_audio.merge_tracks(tracks, filter=["vocals"]).cpu().numpy() + non_vocals = ( + separate_audio.merge_tracks(tracks, filter=["drums", "bass", "other"]) + .cpu() + .numpy() + ) + + vocals = librosa.resample(audio[0], orig_sr=model.samplerate, target_sr=sr) + non_vocals = librosa.resample(non_vocals[0], orig_sr=model.samplerate, target_sr=sr) + + # Normalize loudness + non_vocals = loudness_norm.loudness_norm(non_vocals, sr) + + return vocals, non_vocals diff --git a/fish_diffusion/utils/inference.py b/fish_diffusion/utils/inference.py index bb40f4fc..e0869cd4 100644 --- a/fish_diffusion/utils/inference.py +++ b/fish_diffusion/utils/inference.py @@ -1,9 +1,9 @@ import torch -from train import FishDiffusion +from fish_diffusion.archs.diffsinger.diffsinger import DiffSingerLightning -def load_checkpoint(config, checkpoint, device="cuda") -> FishDiffusion: +def load_checkpoint(config, checkpoint, device="cuda", model_cls=DiffSingerLightning): """Load checkpoint from path Args: @@ -12,10 +12,10 @@ def load_checkpoint(config, checkpoint, device="cuda") -> FishDiffusion: device: device Returns: - FishDiffusion: model + model """ - model = FishDiffusion(config) + model = model_cls(config) state_dict = torch.load(checkpoint, map_location="cpu") if "state_dict" in state_dict: # Checkpoint is saved by pl diff --git a/fish_diffusion/utils/ssim.py b/fish_diffusion/utils/ssim.py deleted file mode 100644 index 6a192abe..00000000 --- a/fish_diffusion/utils/ssim.py +++ /dev/null @@ -1,114 +0,0 @@ -""" -Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim -""" - -from math import exp - -import numpy as np -import torch -import torch.nn.functional as F -from torch.autograd import Variable - - -def gaussian(window_size, sigma): - gauss = torch.Tensor( - [ - exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) - for x in range(window_size) - ] - ) - return gauss / gauss.sum() - - -def create_window(window_size, channel): - _1D_window = gaussian(window_size, 1.5).unsqueeze(1) - _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) - window = Variable( - _2D_window.expand(channel, 1, window_size, window_size).contiguous() - ) - return window - - -def _ssim(img1, img2, window, window_size, channel, size_average=True): - mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) - mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) - - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1 * mu2 - - sigma1_sq = ( - F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq - ) - sigma2_sq = ( - F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq - ) - sigma12 = ( - F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - - mu1_mu2 - ) - - C1 = 0.01**2 - C2 = 0.03**2 - - ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( - (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) - ) - - if size_average: - return ssim_map.mean() - else: - return ssim_map.mean(1) - - -class SSIM(torch.nn.Module): - def __init__(self, window_size=11, size_average=True): - super(SSIM, self).__init__() - self.window_size = window_size - self.size_average = size_average - self.channel = 1 - self.window = create_window(window_size, self.channel) - - def forward(self, img1, img2): - (_, channel, _, _) = img1.size() - - if channel == self.channel and self.window.data.type() == img1.data.type(): - window = self.window - else: - window = create_window(self.window_size, channel) - - if img1.is_cuda: - window = window.cuda(img1.get_device()) - window = window.type_as(img1) - - self.window = window - self.channel = channel - - return _ssim(img1, img2, window, self.window_size, channel, self.size_average) - - -window = None - - -def ssim(img1, img2, window_size=11, size_average=True): - (_, channel, _, _) = img1.size() - global window - if window is None: - window = create_window(window_size, channel) - if img1.is_cuda: - window = window.cuda(img1.get_device()) - window = window.type_as(img1) - return _ssim(img1, img2, window, window_size, channel, size_average) - - -def ssim_loss(decoder_output, target, bias=6.0): - # decoder_output : B x T x n_mel - # target : B x T x n_mel - assert decoder_output.shape == target.shape - - decoder_output = decoder_output[:, None] + bias - target = target[:, None] + bias - ssim_loss = 1 - ssim(decoder_output, target, size_average=False) - ssim_loss = ssim_loss.mean() - - return ssim_loss diff --git a/inference.py b/inference.py deleted file mode 100644 index eddc256e..00000000 --- a/inference.py +++ /dev/null @@ -1,431 +0,0 @@ -import argparse -import json -import os -from functools import partial -from typing import Union - -import gradio as gr -import librosa -import numpy as np -import soundfile as sf -import torch -from fish_audio_preprocess.utils import loudness_norm, separate_audio -from loguru import logger -from mmengine import Config - -from fish_diffusion.feature_extractors import FEATURE_EXTRACTORS, PITCH_EXTRACTORS -from fish_diffusion.utils.audio import get_mel_from_audio, slice_audio -from fish_diffusion.utils.inference import load_checkpoint -from fish_diffusion.utils.tensor import repeat_expand - - -@torch.no_grad() -def inference( - config, - checkpoint, - input_path, - output_path, - speaker_id=0, - pitch_adjust=0, - silence_threshold=60, - max_slice_duration=30.0, - extract_vocals=True, - merge_non_vocals=True, - vocals_loudness_gain=0.0, - sampler_interval=None, - sampler_progress=False, - device="cuda", - gradio_progress=None, -): - """Inference - - Args: - config: config - checkpoint: checkpoint path - input_path: input path - output_path: output path - speaker_id: speaker id - pitch_adjust: pitch adjust - silence_threshold: silence threshold of librosa.effects.split - max_slice_duration: maximum duration of each slice - extract_vocals: extract vocals - merge_non_vocals: merge non-vocals, only works when extract_vocals is True - vocals_loudness_gain: loudness gain of vocals (dB) - sampler_interval: sampler interval, lower value means higher quality - sampler_progress: show sampler progress - device: device - gradio_progress: gradio progress callback - """ - - if sampler_interval is not None: - config.model.diffusion.sampler_interval = sampler_interval - - if os.path.isdir(checkpoint): - # Find the latest checkpoint - checkpoints = sorted(os.listdir(checkpoint)) - logger.info(f"Found {len(checkpoints)} checkpoints, using {checkpoints[-1]}") - checkpoint = os.path.join(checkpoint, checkpoints[-1]) - - audio, sr = librosa.load(input_path, sr=config.sampling_rate, mono=True) - - # Extract vocals - - if extract_vocals: - logger.info("Extracting vocals...") - - if gradio_progress is not None: - gradio_progress(0, "Extracting vocals...") - - model = separate_audio.init_model("htdemucs", device=device) - audio = librosa.resample(audio, orig_sr=sr, target_sr=model.samplerate)[None] - - # To two channels - audio = np.concatenate([audio, audio], axis=0) - audio = torch.from_numpy(audio).to(device) - tracks = separate_audio.separate_audio( - model, audio, shifts=1, num_workers=0, progress=True - ) - audio = separate_audio.merge_tracks(tracks, filter=["vocals"]).cpu().numpy() - non_vocals = ( - separate_audio.merge_tracks(tracks, filter=["drums", "bass", "other"]) - .cpu() - .numpy() - ) - - audio = librosa.resample(audio[0], orig_sr=model.samplerate, target_sr=sr) - non_vocals = librosa.resample( - non_vocals[0], orig_sr=model.samplerate, target_sr=sr - ) - - # Normalize loudness - non_vocals = loudness_norm.loudness_norm(non_vocals, sr) - - # Normalize loudness - audio = loudness_norm.loudness_norm(audio, sr) - - # Slice into segments - segments = list( - slice_audio( - audio, sr, max_duration=max_slice_duration, top_db=silence_threshold - ) - ) - logger.info(f"Sliced into {len(segments)} segments") - - # Load models - text_features_extractor = FEATURE_EXTRACTORS.build( - config.preprocessing.text_features_extractor - ).to(device) - text_features_extractor.eval() - - model = load_checkpoint(config, checkpoint, device=device) - - pitch_extractor = PITCH_EXTRACTORS.build(config.preprocessing.pitch_extractor) - assert pitch_extractor is not None, "Pitch extractor not found" - - generated_audio = np.zeros_like(audio) - audio_torch = torch.from_numpy(audio).to(device)[None] - - for idx, (start, end) in enumerate(segments): - if gradio_progress is not None: - gradio_progress(idx / len(segments), "Generating audio...") - - segment = audio_torch[:, start:end] - logger.info( - f"Processing segment {idx + 1}/{len(segments)}, duration: {segment.shape[-1] / sr:.2f}s" - ) - - # Extract mel - mel = get_mel_from_audio(segment, sr) - - # Extract pitch (f0) - pitch = pitch_extractor(segment, sr, pad_to=mel.shape[-1]).float() - pitch *= 2 ** (pitch_adjust / 12) - - # Extract text features - text_features = text_features_extractor(segment, sr)[0] - text_features = repeat_expand(text_features, mel.shape[-1]).T - - # Pitch shift should always be 0 for inference to avoid distortion - pitch_shift = None - if config.model.get("pitch_shift_encoder"): - pitch_shift = torch.zeros((1, 1), device=device) - - # Predict - contents_lens = torch.tensor([mel.shape[-1]]).to(device) - - features = model.model.forward_features( - speakers=torch.tensor([speaker_id]).long().to(device), - contents=text_features[None].to(device), - contents_lens=contents_lens, - contents_max_len=max(contents_lens), - mel_lens=contents_lens, - mel_max_len=max(contents_lens), - pitches=pitch[None].to(device), - pitch_shift=pitch_shift, - ) - - result = model.model.diffusion(features["features"], progress=sampler_progress) - wav = model.vocoder.spec2wav(result[0].T, f0=pitch).cpu().numpy() - max_wav_len = generated_audio.shape[-1] - start - generated_audio[start : start + wav.shape[-1]] = wav[:max_wav_len] - - # Loudness normalization - generated_audio = loudness_norm.loudness_norm(generated_audio, sr) - - # Loudness gain - loudness_float = 10 ** (vocals_loudness_gain / 20) - generated_audio = generated_audio * loudness_float - - # Merge non-vocals - if extract_vocals and merge_non_vocals: - generated_audio = (generated_audio + non_vocals) / 2 - - logger.info("Done") - - if output_path is not None: - sf.write(output_path, generated_audio, sr) - - return generated_audio, sr - - -def parse_args(): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--config", - type=str, - required=True, - help="Path to the config file", - ) - - parser.add_argument( - "--checkpoint", - type=str, - required=True, - help="Path to the checkpoint file", - ) - - parser.add_argument( - "--gradio", - action="store_true", - help="Run in gradio mode", - ) - - parser.add_argument( - "--gradio_share", - action="store_true", - help="Share gradio app", - ) - - parser.add_argument( - "--input", - type=str, - required=False, - help="Path to the input audio file", - ) - - parser.add_argument( - "--output", - type=str, - required=False, - help="Path to the output audio file", - ) - - parser.add_argument( - "--speaker_id", - type=int, - default=0, - help="Speaker id", - ) - - parser.add_argument( - "--speaker_mapping", - type=str, - default=None, - help="Speaker mapping file (gradio mode only)", - ) - - parser.add_argument( - "--pitch_adjust", - type=int, - default=0, - help="Pitch adjustment in semitones", - ) - - parser.add_argument( - "--extract_vocals", - action="store_true", - help="Extract vocals", - ) - - parser.add_argument( - "--merge_non_vocals", - action="store_true", - help="Merge non-vocals", - ) - - parser.add_argument( - "--vocals_loudness_gain", - type=float, - default=0, - help="Loudness gain for vocals", - ) - - parser.add_argument( - "--sampler_interval", - type=int, - default=None, - required=False, - help="Sampler interval, if not specified, will be taken from config", - ) - - parser.add_argument( - "--sampler_progress", - action="store_true", - help="Show sampler progress", - ) - - parser.add_argument( - "--device", - type=str, - default=None, - required=False, - help="Device to use", - ) - - return parser.parse_args() - - -def run_inference( - config_path: str, - model_path: str, - input_path: str, - speaker: Union[int, str], - pitch_adjust: int, - sampler_interval: int, - extract_vocals: bool, - device: str, - progress=gr.Progress(), - speaker_mapping: dict = None, -): - if speaker_mapping is not None and isinstance(speaker, str): - speaker = speaker_mapping[speaker] - - audio, sr = inference( - Config.fromfile(config_path), - model_path, - input_path=input_path, - output_path=None, - speaker_id=speaker, - pitch_adjust=pitch_adjust, - sampler_interval=round(sampler_interval), - extract_vocals=extract_vocals, - merge_non_vocals=False, - device=device, - gradio_progress=progress, - ) - - return (sr, audio) - - -def launch_gradio(args): - with gr.Blocks(title="Fish Diffusion") as app: - gr.Markdown("# Fish Diffusion SVC Inference") - - with gr.Row(): - with gr.Column(): - input_audio = gr.Audio( - label="Input Audio", - type="filepath", - value=args.input, - ) - output_audio = gr.Audio(label="Output Audio") - - with gr.Column(): - if args.speaker_mapping is not None: - speaker_mapping = json.load(open(args.speaker_mapping)) - - speaker = gr.Dropdown( - label="Speaker Name (Used for Multi-Speaker Models)", - choices=list(speaker_mapping.keys()), - value=list(speaker_mapping.keys())[0], - ) - else: - speaker_mapping = None - speaker = gr.Number( - label="Speaker ID (Used for Multi-Speaker Models)", - value=args.speaker_id, - ) - - pitch_adjust = gr.Number( - label="Pitch Adjust (Semitones)", value=args.pitch_adjust - ) - sampler_interval = gr.Slider( - label="Sampler Interval (⬆️ Faster Generation, ⬇️ Better Quality)", - value=args.sampler_interval or 10, - minimum=1, - maximum=100, - ) - extract_vocals = gr.Checkbox( - label="Extract Vocals (For low quality audio)", - value=args.extract_vocals, - ) - device = gr.Radio( - label="Device", choices=["cuda", "cpu"], value=args.device or "cuda" - ) - - run_btn = gr.Button(label="Run") - - run_btn.click( - partial( - run_inference, - args.config, - args.checkpoint, - speaker_mapping=speaker_mapping, - ), - [ - input_audio, - speaker, - pitch_adjust, - sampler_interval, - extract_vocals, - device, - ], - output_audio, - ) - - app.queue(concurrency_count=2).launch(share=args.gradio_share) - - -if __name__ == "__main__": - args = parse_args() - - assert args.gradio or ( - args.input is not None and args.output is not None - ), "Either --gradio or --input and --output should be specified" - - if args.device is None: - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - else: - device = torch.device(args.device) - - if args.gradio: - args.device = device - launch_gradio(args) - - else: - - inference( - Config.fromfile(args.config), - args.checkpoint, - args.input, - args.output, - speaker_id=args.speaker_id, - pitch_adjust=args.pitch_adjust, - extract_vocals=args.extract_vocals, - merge_non_vocals=args.merge_non_vocals, - vocals_loudness_gain=args.vocals_loudness_gain, - sampler_interval=args.sampler_interval, - sampler_progress=args.sampler_progress, - device=device, - ) diff --git a/poetry.lock b/poetry.lock index 3726a07c..2c3695dc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -283,19 +283,16 @@ files = [ [[package]] name = "babel" -version = "2.11.0" +version = "2.12.1" description = "Internationalization utilities" category = "dev" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "Babel-2.11.0-py3-none-any.whl", hash = "sha256:1ad3eca1c885218f6dce2ab67291178944f810a10a9b5f3cb8382a5a232b64fe"}, - {file = "Babel-2.11.0.tar.gz", hash = "sha256:5ef4b3226b0180dedded4229651c8b0e1a3a6a2837d45a073272f313e4cf97f6"}, + {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"}, + {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"}, ] -[package.dependencies] -pytz = ">=2015.7" - [[package]] name = "beautifulsoup4" version = "4.11.2" @@ -1112,14 +1109,14 @@ testing = ["covdefaults (>=2.2.2)", "coverage (>=7.0.1)", "pytest (>=7.2)", "pyt [[package]] name = "fish-audio-preprocess" -version = "0.1.9" +version = "0.1.10" description = "Preprocess audio data" category = "main" optional = false python-versions = ">=3.9,<4.0" files = [ - {file = "fish_audio_preprocess-0.1.9-py3-none-any.whl", hash = "sha256:12de186e6401d5b1d321a1e11ec03fffb78065caf47696db6c596dcc22161bd7"}, - {file = "fish_audio_preprocess-0.1.9.tar.gz", hash = "sha256:2652d9516539c8b2601496b9704f8caa7d06241aa303d2896a15d28785bd1ae8"}, + {file = "fish_audio_preprocess-0.1.10-py3-none-any.whl", hash = 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a/tests/test_visualize_pitch.py +++ b/tests/test_visualize_pitch.py @@ -5,7 +5,7 @@ from loguru import logger from matplotlib import pyplot as plt -from fish_diffusion.feature_extractors.pitch import ( +from fish_diffusion.modules.pitch_extractors import ( CrepePitchExtractor, DioPitchExtractor, HarvestPitchExtractor, diff --git a/tools/batch_inference.py b/tools/batch_inference.py deleted file mode 100644 index 0404b9e7..00000000 --- a/tools/batch_inference.py +++ /dev/null @@ -1,53 +0,0 @@ -import argparse -import os - -parser = argparse.ArgumentParser(description="Fish-SVC batch inferencing tool") -parser.add_argument( - "--config", type=str, required=True, help="Path of the config you want to use" -) -parser.add_argument( - "--sampler_interval", - type=int, - default=10, - help="Speedup value for the inference process. Higher values will decrease render quality but increase inference speed.", -) -parser.add_argument( - "--checkpoint", - type=str, - required=True, - help="Path to the checkpoint file to use for rendering.", -) -parser.add_argument( - "--input_audio_folder", - type=str, - required=True, - help="Path to the folder containing input audio WAV files.", -) -parser.add_argument( - "--output_folder", - type=str, - required=True, - help="Path to the folder where rendered audio WAV files will be saved.", -) -parser.add_argument( - "--pitch_adjust", - type=int, - default=0, - help="Pitch adjustment value for the rendered audio, in semitones. Positive values increase pitch, negative values decrease pitch.", -) -args = parser.parse_args() - -config = args.config -sampler_interval = args.sampler_interval -checkpoint = args.checkpoint -input_audio_folder = args.input_audio_folder -output_folder = args.output_folder -pitch_adjust = args.pitch_adjust - -for name in os.listdir(input_audio_folder): - if name.endswith(".wav"): - input_file = os.path.join(input_audio_folder, name) - print(input_file) - render = os.path.join(output_folder, name) - command = f"python inference.py --config {config} --checkpoint {checkpoint} --sampler_interval {sampler_interval} --pitch_adjust {pitch_adjust} --input {input_audio_folder} --output {output_folder}" - os.system(command) diff --git a/tools/clean_speaker_embeddings.py b/tools/diffusion/clean_speaker_embeddings.py similarity index 100% rename from tools/clean_speaker_embeddings.py rename to tools/diffusion/clean_speaker_embeddings.py diff --git a/tools/diff_svc_converter.py b/tools/diffusion/diff_svc_converter.py similarity index 99% rename from tools/diff_svc_converter.py rename to tools/diffusion/diff_svc_converter.py index a1c8c184..94e04ad4 100644 --- a/tools/diff_svc_converter.py +++ b/tools/diffusion/diff_svc_converter.py @@ -3,7 +3,6 @@ import torch from loguru import logger from mmengine import Config - from train import FishDiffusion diff --git a/tools/vst_flask_api.py b/tools/diffusion/flask_api.py similarity index 50% rename from tools/vst_flask_api.py rename to tools/diffusion/flask_api.py index 32502160..63b34f28 100644 --- a/tools/vst_flask_api.py +++ b/tools/diffusion/flask_api.py @@ -7,11 +7,12 @@ import librosa import soundfile +import torch from flask import Flask, request, send_file from flask_cors import CORS from mmengine import Config -from inference import inference +from tools.diffusion.inference import SVCInference app = Flask(__name__) @@ -27,63 +28,38 @@ def voice_change_model(): # 变调信息 f_pitch_change = float(request_form.get("fPitchChange", 0)) # 获取spkid - int_speak_Id = int(request_form.get("sSpeakId", 0)) + int_speaker_id = int(request_form.get("sSpeakId", 0)) if enable_spk_id_cover: - int_speak_Id = spk_id - print("说话人:" + str(int_speak_Id)) + int_speaker_id = spk_id + + print(f"Speaker: {int_speaker_id}, pitch: {f_pitch_change}") + # DAW所需的采样率 daw_sample = int(float(request_form.get("sampleRate", 0))) + # http获得wav文件并转换 input_wav_file = io.BytesIO(wave_file.read()) + # 模型推理 - _audio, _model_sr = svc_model.infer(input_wav_file, f_pitch_change, int_speak_Id) + _audio, _model_sr = model.inference( + input_path=input_wav_file, + output_path=None, + speaker=int_speaker_id, + pitch_adjust=f_pitch_change, + silence_threshold=silence_threshold, + max_slice_duration=max_slice_duration, + extract_vocals=extract_vocals, + sampler_interval=sampler_interval, + ) + tar_audio = librosa.resample(_audio, _model_sr, daw_sample) + # 返回音频 out_wav_path = io.BytesIO() soundfile.write(out_wav_path, tar_audio, daw_sample, format="wav") out_wav_path.seek(0) - return send_file(out_wav_path, download_name="temp.wav", as_attachment=True) - -class SvcFish: - def __init__( - self, - checkpoint_path, - config_path, - sampler_interval=None, - extract_vocals=True, - merge_non_vocals=True, - vocals_loudness_gain=0.0, - silence_threshold=60, - max_slice_duration=30.0, - ): - self.config = Config.fromfile(config_path) - self.checkpoint_path = checkpoint_path - self.sampler_interval = sampler_interval - self.silence_threshold = silence_threshold - self.max_slice_duration = max_slice_duration - self.extract_vocals = extract_vocals - self.merge_non_vocals = merge_non_vocals - self.vocals_loudness_gain = vocals_loudness_gain - - def infer(self, input_path, pitch_adjust, speaker_id): - return inference( - config=self.config, - checkpoint=self.checkpoint_path, - input_path=input_path, - output_path=None, - speaker_id=speaker_id, - pitch_adjust=pitch_adjust, - silence_threshold=self.silence_threshold, - max_slice_duration=self.max_slice_duration, - extract_vocals=self.extract_vocals, - merge_non_vocals=self.merge_non_vocals, - vocals_loudness_gain=self.vocals_loudness_gain, - sampler_interval=self.sampler_interval, - sampler_progress=True, - device="cuda", - gradio_progress=None, - ) + return send_file(out_wav_path, download_name="temp.wav", as_attachment=True) if __name__ == "__main__": @@ -94,10 +70,8 @@ def infer(self, input_path, pitch_adjust, speaker_id): config_path = "configs/svc_cn_hubert_soft_ms.py" # 加速倍率，None即采用配置文件的值 sampler_interval = None - # 是否提取人声，是否合成非人声，以及人声响度增益 + # 是否提取人声 extract_vocals = False - merge_non_vocals = False - vocals_loudness_gain = 0.0 # 默认说话人。以及是否优先使用默认说话人覆盖vst传入的参数。 spk_id = 0 enable_spk_id_cover = False @@ -106,16 +80,11 @@ def infer(self, input_path, pitch_adjust, speaker_id): # 静音阈值 silence_threshold = 60 - svc_model = SvcFish( - checkpoint_path, - config_path, - sampler_interval=sampler_interval, - extract_vocals=extract_vocals, - merge_non_vocals=merge_non_vocals, - vocals_loudness_gain=vocals_loudness_gain, - silence_threshold=silence_threshold, - max_slice_duration=max_slice_duration, - ) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + config = Config.fromfile(config_path) + model = SVCInference(config, checkpoint_path) + model = model.to(device) # 此处与vst插件对应，不建议更改 app.run(port=6842, host="0.0.0.0", debug=False, threaded=False) diff --git a/tools/diffusion/gradio_ui.py b/tools/diffusion/gradio_ui.py new file mode 100644 index 00000000..67acad9a --- /dev/null +++ b/tools/diffusion/gradio_ui.py @@ -0,0 +1,92 @@ +from functools import partial +from typing import Union + +import gradio as gr + + +def run_inference( + inference_fn, + input_path: str, + speaker: Union[int, str], + pitch_adjust: int, + sampler_interval: int, + extract_vocals: bool, + progress=gr.Progress(), +): + audio, sr = inference_fn( + input_path=input_path, + output_path=None, + speaker=speaker, + pitch_adjust=pitch_adjust, + sampler_interval=sampler_interval, + extract_vocals=extract_vocals, + gradio_progress=progress, + ) + + return sr, audio + + +def launch_gradio( + config, + inference_fn, + speaker, + pitch_adjust: int, + sampler_interval: int, + extract_vocals: bool, + share: bool = False, +): + with gr.Blocks(title="Fish Diffusion") as app: + gr.Markdown("# Fish Diffusion SVC Inference") + + with gr.Row(): + with gr.Column(): + input_audio = gr.Audio( + label="Input Audio", + type="filepath", + ) + output_audio = gr.Audio(label="Output Audio") + + with gr.Column(): + if hasattr(config, "speaker_mapping"): + speaker_mapping = config.speaker_mapping + speaker = gr.Dropdown( + label="Speaker Name (Used for Multi-Speaker Models)", + choices=list(speaker_mapping.keys()), + value=speaker + if speaker in speaker_mapping + else list(speaker_mapping.keys())[0], + ) + else: + speaker = gr.Number( + label="Speaker ID (Used for Multi-Speaker Models)", + value=int(speaker), + ) + + pitch_adjust = gr.Number( + label="Pitch Adjust (Semitones)", value=pitch_adjust + ) + sampler_interval = gr.Slider( + label="Sampler Interval (⬆️ Faster Generation, ⬇️ Better Quality)", + value=sampler_interval or 10, + minimum=1, + maximum=100, + ) + extract_vocals = gr.Checkbox( + label="Extract Vocals (For low quality audio)", + value=extract_vocals, + ) + run_btn = gr.Button(label="Run") + + run_btn.click( + partial(run_inference, inference_fn), + [ + input_audio, + speaker, + pitch_adjust, + sampler_interval, + extract_vocals, + ], + output_audio, + ) + + app.queue(concurrency_count=2).launch(share=share) diff --git a/tools/diffusion/inference.py b/tools/diffusion/inference.py new file mode 100644 index 00000000..7f794625 --- /dev/null +++ b/tools/diffusion/inference.py @@ -0,0 +1,354 @@ +import argparse +import json +import os +from typing import Optional + +import librosa +import numpy as np +import soundfile as sf +import torch +from fish_audio_preprocess.utils import loudness_norm +from loguru import logger +from mmengine import Config +from torch import nn + +from fish_diffusion.modules.feature_extractors import FEATURE_EXTRACTORS +from fish_diffusion.modules.pitch_extractors import PITCH_EXTRACTORS +from fish_diffusion.utils.audio import get_mel_from_audio, separate_vocals, slice_audio +from fish_diffusion.utils.inference import load_checkpoint +from fish_diffusion.utils.tensor import repeat_expand +from tools.diffusion.gradio_ui import launch_gradio + + +class SVCInference(nn.Module): + def __init__(self, config, checkpoint): + super().__init__() + + self.config = config + + self.text_features_extractor = FEATURE_EXTRACTORS.build( + config.preprocessing.text_features_extractor + ) + self.pitch_extractor = PITCH_EXTRACTORS.build( + config.preprocessing.pitch_extractor + ) + + if os.path.isdir(checkpoint): + # Find the latest checkpoint + checkpoints = sorted(os.listdir(checkpoint)) + logger.info( + f"Found {len(checkpoints)} checkpoints, using {checkpoints[-1]}" + ) + checkpoint = os.path.join(checkpoint, checkpoints[-1]) + + self.model = load_checkpoint(config, checkpoint) + + @property + def device(self): + return next(self.parameters()).device + + @torch.no_grad() + def forward( + self, + audio: torch.Tensor, + sr: int, + pitch_adjust: int = 0, + speaker_id: int = 0, + sampler_progress: bool = False, + sampler_interval: Optional[int] = None, + ): + mel = get_mel_from_audio(audio, sr) + + # Extract and process pitch + pitch = self.pitch_extractor(audio, sr, pad_to=mel.shape[-1]).float() + pitch *= 2 ** (pitch_adjust / 12) + + # Extract and process text features + text_features = self.text_features_extractor(audio, sr)[0] + text_features = repeat_expand(text_features, mel.shape[-1]).T + + # Pitch shift should always be 0 for inference to avoid distortion + pitch_shift = None + if self.config.model.get("pitch_shift_encoder"): + pitch_shift = torch.zeros((1, 1), device=self.device) + + # Predict + contents_lens = torch.tensor([mel.shape[-1]]).to(self.device) + + features = self.model.model.forward_features( + speakers=torch.tensor([speaker_id]).long().to(self.device), + contents=text_features[None].to(self.device), + contents_lens=contents_lens, + contents_max_len=max(contents_lens), + mel_lens=contents_lens, + mel_max_len=max(contents_lens), + pitches=pitch[None].to(self.device), + pitch_shift=pitch_shift, + ) + + result = self.model.model.diffusion( + features["features"], + progress=sampler_progress, + sampler_interval=sampler_interval, + ) + wav = self.model.vocoder.spec2wav(result[0].T, f0=pitch).cpu().numpy() + + return wav + + @torch.no_grad() + def inference( + self, + input_path, + output_path, + speaker=0, + pitch_adjust=0, + silence_threshold=60, + max_slice_duration=30.0, + extract_vocals=True, + sampler_progress=False, + sampler_interval=None, + gradio_progress=None, + ): + """Inference + + Args: + input_path: input path + output_path: output path + speaker: speaker id or speaker name + pitch_adjust: pitch adjust + silence_threshold: silence threshold of librosa.effects.split + max_slice_duration: maximum duration of each slice + extract_vocals: extract vocals + sampler_progress: show sampler progress + sampler_interval: sampler interval + gradio_progress: gradio progress callback + """ + + if os.path.isdir(input_path): + # Batch inference + if output_path is None: + logger.error("Output path is required for batch inference") + return + + if os.path.exists(output_path) and not os.path.isdir(output_path): + logger.error( + f"Output path {output_path} already exists, and it's not a directory" + ) + return + + for file in os.listdir(input_path): + self.inference( + os.path.join(input_path, file), + os.path.join(output_path, file), + speaker, + pitch_adjust, + silence_threshold, + max_slice_duration, + extract_vocals, + sampler_progress, + gradio_progress, + ) + + return + + # Process speaker + try: + speaker_id = self.config.speaker_mapping[speaker] + except KeyError: + # Parse speaker id + speaker_id = int(speaker) + + # Load audio + audio, sr = librosa.load(input_path, sr=config.sampling_rate, mono=True) + + # Extract vocals + + if extract_vocals: + logger.info("Extracting vocals...") + + if gradio_progress is not None: + gradio_progress(0, "Extracting vocals...") + + audio, _ = separate_vocals(audio, sr, self.device) + + # Normalize loudness + audio = loudness_norm.loudness_norm(audio, sr) + + # Slice into segments + segments = list( + slice_audio( + audio, sr, max_duration=max_slice_duration, top_db=silence_threshold + ) + ) + logger.info(f"Sliced into {len(segments)} segments") + + generated_audio = np.zeros_like(audio) + audio_torch = torch.from_numpy(audio).to(self.device)[None] + + for idx, (start, end) in enumerate(segments): + if gradio_progress is not None: + gradio_progress(idx / len(segments), "Generating audio...") + + segment = audio_torch[:, start:end] + logger.info( + f"Processing segment {idx + 1}/{len(segments)}, duration: {segment.shape[-1] / sr:.2f}s" + ) + + wav = self( + segment, + sr, + pitch_adjust=pitch_adjust, + speaker_id=speaker_id, + sampler_progress=sampler_progress, + sampler_interval=sampler_interval, + ) + max_wav_len = generated_audio.shape[-1] - start + generated_audio[start : start + wav.shape[-1]] = wav[:max_wav_len] + + # Loudness normalization + generated_audio = loudness_norm.loudness_norm(generated_audio, sr) + + logger.info("Done") + + if output_path is not None: + sf.write(output_path, generated_audio, sr) + + return generated_audio, sr + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--config", + type=str, + required=True, + help="Path to the config file", + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint file", + ) + + parser.add_argument( + "--gradio", + action="store_true", + help="Run in gradio mode", + ) + + parser.add_argument( + "--gradio_share", + action="store_true", + help="Share gradio app", + ) + + parser.add_argument( + "--input", + type=str, + required=False, + help="Path to the input audio file", + ) + + parser.add_argument( + "--output", + type=str, + required=False, + help="Path to the output audio file", + ) + + parser.add_argument( + "--speaker", + type=str, + default="0", + help="Speaker id or speaker name", + ) + + parser.add_argument( + "--speaker_mapping", + type=str, + default=None, + help="Speaker mapping file (if not specified, will be taken from config)", + ) + + parser.add_argument( + "--pitch_adjust", + type=int, + default=0, + help="Pitch adjustment in semitones", + ) + + parser.add_argument( + "--extract_vocals", + action="store_true", + help="Extract vocals", + ) + + parser.add_argument( + "--sampler_interval", + type=int, + default=None, + required=False, + help="Sampler interval, if not specified, will be taken from config", + ) + + parser.add_argument( + "--sampler_progress", + action="store_true", + help="Show sampler progress", + ) + + parser.add_argument( + "--device", + type=str, + default=None, + required=False, + help="Device to use", + ) + + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + + assert args.gradio or ( + args.input is not None and args.output is not None + ), "Either --gradio or --input and --output should be specified" + + if args.device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + device = torch.device(args.device) + + config = Config.fromfile(args.config) + + if args.speaker_mapping is not None: + config.speaker_mapping = json.load(open(args.speaker_mapping)) + + model = SVCInference(config, args.checkpoint) + model = model.to(device) + + if args.gradio: + launch_gradio( + config, + model.inference, + speaker=args.speaker, + pitch_adjust=args.pitch_adjust, + sampler_interval=args.sampler_interval, + extract_vocals=args.extract_vocals, + share=args.gradio_share, + ) + + else: + model.inference( + input_path=args.input, + output_path=args.output, + speaker=args.speaker, + pitch_adjust=args.pitch_adjust, + extract_vocals=args.extract_vocals, + sampler_progress=args.sampler_progress, + sampler_interval=args.sampler_interval, + ) diff --git a/inference_svs.py b/tools/diffusion/inference_svs.py similarity index 94% rename from inference_svs.py rename to tools/diffusion/inference_svs.py index 0073fe00..3fc7b98e 100644 --- a/inference_svs.py +++ b/tools/diffusion/inference_svs.py @@ -10,9 +10,10 @@ from loguru import logger from mmengine import Config -from fish_diffusion.feature_extractors import FEATURE_EXTRACTORS, PITCH_EXTRACTORS +from fish_diffusion.archs.diffsinger.diffsinger import DiffSingerLightning +from fish_diffusion.modules.feature_extractors import FEATURE_EXTRACTORS +from fish_diffusion.modules.pitch_extractors import PITCH_EXTRACTORS from fish_diffusion.utils.tensor import repeat_expand -from train import FishDiffusion @torch.no_grad() @@ -56,7 +57,7 @@ def inference( ).to(device) phoneme_features_extractor.eval() - model = FishDiffusion(config) + model = DiffSingerLightning(config) state_dict = torch.load(checkpoint, map_location="cpu") if "state_dict" in state_dict: # Checkpoint is saved by pl @@ -134,10 +135,10 @@ def inference( features = model.model.forward_features( speakers=torch.tensor([speaker_id]).long().to(device), contents=phoneme_features[None].to(device), - src_lens=src_lens, - max_src_len=max(src_lens), + contents_lens=src_lens, + contents_max_len=max(src_lens), mel_lens=src_lens, - max_mel_len=max(src_lens), + mel_max_len=max(src_lens), pitches=f0_seq[None], ) diff --git a/inference_svs_ds.py b/tools/diffusion/inference_svs_ds.py similarity index 96% rename from inference_svs_ds.py rename to tools/diffusion/inference_svs_ds.py index 594dfcb9..d50b9f2d 100644 --- a/inference_svs_ds.py +++ b/tools/diffusion/inference_svs_ds.py @@ -3,7 +3,6 @@ import math import os -import librosa import numpy as np import soundfile as sf import torch @@ -11,9 +10,9 @@ from loguru import logger from mmengine import Config -from fish_diffusion.feature_extractors import FEATURE_EXTRACTORS, PITCH_EXTRACTORS +from fish_diffusion.archs.diffsinger.diffsinger import DiffSingerLightning +from fish_diffusion.modules.pitch_extractors import PITCH_EXTRACTORS from fish_diffusion.utils.tensor import repeat_expand -from train import FishDiffusion @torch.no_grad() @@ -52,7 +51,7 @@ def inference( checkpoint = os.path.join(checkpoint, checkpoints[-1]) # Load models - model = FishDiffusion(config) + model = DiffSingerLightning(config) state_dict = torch.load(checkpoint, map_location="cpu") if "state_dict" in state_dict: # Checkpoint is saved by pl @@ -98,7 +97,6 @@ def inference( phones.append(phones_list.index(phone)) durations.append(float(duration)) - print(phones, durations) phones = np.array(phones) durations = np.array(durations) @@ -162,10 +160,10 @@ def inference( features = model.model.forward_features( speakers=torch.tensor([speaker_id]).long().to(device), contents=phoneme_features[None].to(device), - src_lens=src_lens, - max_src_len=max(src_lens), + contents_lens=src_lens, + contents_max_len=max(src_lens), mel_lens=src_lens, - max_mel_len=max(src_lens), + mel_max_len=max(src_lens), pitches=f0_seq[None], ) diff --git a/tools/onnx/demo.py b/tools/diffusion/onnx/demo.py similarity index 90% rename from tools/onnx/demo.py rename to tools/diffusion/onnx/demo.py index dac28994..16fc2e37 100644 --- a/tools/onnx/demo.py +++ b/tools/diffusion/onnx/demo.py @@ -5,7 +5,7 @@ import torch from loguru import logger -from fish_diffusion.feature_extractors.pitch import ParselMouthPitchExtractor +from fish_diffusion.modules.pitch_extractors import ParselMouthPitchExtractor from fish_diffusion.utils.tensor import repeat_expand @@ -14,7 +14,7 @@ def test(): feature_extractor = ort.InferenceSession("exported/feature_extractor.onnx") feature_embedding = ort.InferenceSession("exported/feature_embedding.onnx") diffusion = ort.InferenceSession("exported/diffusion.onnx") - vocoder = ort.InferenceSession("checkpoints/nsf_hifigan_onnx/nsf_hifigan.onnx") + vocoder = ort.InferenceSession("checkpoints/nsf_hifigan/nsf_hifigan.onnx") logger.info("All models loaded.") audio, sr = librosa.load("raw/一半一半.wav", sr=44100, mono=True) @@ -73,8 +73,7 @@ def test(): }, )[0] - audio = np.squeeze(audio, 0) - sf.write("generated.wav", audio, 44100) + sf.write("generated.wav", audio[0, 0], 44100) logger.info("Congratulations! You have generated a speech sample! 🎉") diff --git a/tools/onnx/export.py b/tools/diffusion/onnx/export.py similarity index 95% rename from tools/onnx/export.py rename to tools/diffusion/onnx/export.py index 2a5df072..4eac0767 100644 --- a/tools/onnx/export.py +++ b/tools/diffusion/onnx/export.py @@ -7,9 +7,9 @@ from loguru import logger from mmengine import Config -from fish_diffusion.archs.diffsinger import DiffSinger -from fish_diffusion.feature_extractors import FEATURE_EXTRACTORS -from train import FishDiffusion +from fish_diffusion.archs.diffsinger.diffsinger import DiffSinger, DiffSingerLightning +from fish_diffusion.modules.feature_extractors import FEATURE_EXTRACTORS +from fish_diffusion.utils.inference import load_checkpoint class FeatureEmbeddingWrapper(torch.nn.Module): @@ -248,14 +248,7 @@ def main(config: str, checkpoint: str): device = "cpu" config = Config.fromfile(config) - model = FishDiffusion(config) - state_dict = torch.load( - checkpoint, - map_location=device, - )["state_dict"] - model.load_state_dict(state_dict, strict=False) - model.eval() - model.to(device) + model = load_checkpoint(config, checkpoint, device, model_cls=DiffSingerLightning) # Ignore vocoder model = model.model diff --git a/tools/onnx/export_moess.py b/tools/diffusion/onnx/export_moess.py similarity index 96% rename from tools/onnx/export_moess.py rename to tools/diffusion/onnx/export_moess.py index bb25cd2a..44201516 100644 --- a/tools/onnx/export_moess.py +++ b/tools/diffusion/onnx/export_moess.py @@ -1,7 +1,6 @@ from pathlib import Path import click -import numpy as np import onnxruntime as ort import torch from export import export_feature_extractor @@ -9,8 +8,8 @@ from mmengine import Config from torch.nn import functional as F -from fish_diffusion.archs.diffsinger import DiffSinger -from train import FishDiffusion +from fish_diffusion.archs.diffsinger.diffsinger import DiffSinger, DiffSingerLightning +from fish_diffusion.utils.inference import load_checkpoint def denorm_f0(f0, pitch_padding=None): @@ -278,14 +277,7 @@ def main(config: str, checkpoint: str): device = "cpu" config = Config.fromfile(config) - model = FishDiffusion(config) - state_dict = torch.load( - checkpoint, - map_location=device, - )["state_dict"] - model.load_state_dict(state_dict, strict=False) - model.eval() - model.to(device) + model = load_checkpoint(config, checkpoint, device, model_cls=DiffSingerLightning) # Ignore vocoder model = model.model diff --git a/tools/diffusion/train.py b/tools/diffusion/train.py new file mode 100644 index 00000000..e2e78dea --- /dev/null +++ b/tools/diffusion/train.py @@ -0,0 +1,93 @@ +from argparse import ArgumentParser + +import pytorch_lightning as pl +import torch +from loguru import logger +from mmengine import Config +from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger + +from fish_diffusion.archs.diffsinger.diffsinger import DiffSingerLightning +from fish_diffusion.datasets.utils import build_loader_from_config + +torch.set_float32_matmul_precision("medium") + + +if __name__ == "__main__": + pl.seed_everything(42, workers=True) + + parser = ArgumentParser() + parser.add_argument("--config", type=str, required=True) + parser.add_argument("--resume", type=str, default=None) + parser.add_argument( + "--tensorboard", + action="store_true", + default=False, + help="Use tensorboard logger, default is wandb.", + ) + parser.add_argument("--resume-id", type=str, default=None, help="Wandb run id.") + parser.add_argument("--entity", type=str, default=None, help="Wandb entity.") + parser.add_argument("--name", type=str, default=None, help="Wandb run name.") + parser.add_argument( + "--pretrained", type=str, default=None, help="Pretrained model." + ) + parser.add_argument( + "--only-train-speaker-embeddings", + action="store_true", + default=False, + help="Only train speaker embeddings.", + ) + + args = parser.parse_args() + + cfg = Config.fromfile(args.config) + + model = DiffSingerLightning(cfg) + + # We only load the state_dict of the model, not the optimizer. + if args.pretrained: + state_dict = torch.load(args.pretrained, map_location="cpu") + if "state_dict" in state_dict: + state_dict = state_dict["state_dict"] + + result = model.load_state_dict(state_dict, strict=False) + + missing_keys = set(result.missing_keys) + unexpected_keys = set(result.unexpected_keys) + + # Make sure incorrect keys are just noise predictor keys. + unexpected_keys = unexpected_keys - set( + i.replace(".naive_noise_predictor.", ".") for i in missing_keys + ) + + assert len(unexpected_keys) == 0 + + if args.only_train_speaker_embeddings: + for name, param in model.named_parameters(): + if "speaker_encoder" not in name: + param.requires_grad = False + + logger.info( + "Only train speaker embeddings, all other parameters are frozen." + ) + + logger = ( + TensorBoardLogger("logs", name=cfg.model.type) + if args.tensorboard + else WandbLogger( + project=cfg.model.type, + save_dir="logs", + log_model=True, + name=args.name, + entity=args.entity, + resume="must" if args.resume_id else False, + id=args.resume_id, + ) + ) + + trainer = pl.Trainer( + logger=logger, + **cfg.trainer, + ) + + train_loader, valid_loader = build_loader_from_config(cfg, trainer.num_devices) + trainer.fit(model, train_loader, valid_loader, ckpt_path=args.resume) diff --git a/tools/nsf_hifigan/export.py b/tools/nsf_hifigan/export.py index f7e3aaa0..ff5fbf82 100644 --- a/tools/nsf_hifigan/export.py +++ b/tools/nsf_hifigan/export.py @@ -6,7 +6,7 @@ import torch from loguru import logger -from fish_diffusion.vocoders.nsf_hifigan.nsf_hifigan import NsfHifiGAN +from fish_diffusion.modules.vocoders.nsf_hifigan.nsf_hifigan import NsfHifiGAN class ExportableNsfHiFiGAN(NsfHifiGAN): diff --git a/tools/nsf_hifigan/train.py b/tools/nsf_hifigan/train.py index 2877813e..9d0eda95 100644 --- a/tools/nsf_hifigan/train.py +++ b/tools/nsf_hifigan/train.py @@ -14,9 +14,7 @@ from torchaudio.transforms import MelSpectrogram from fish_diffusion.datasets.utils import build_loader_from_config -from fish_diffusion.utils.audio import dynamic_range_compression -from fish_diffusion.utils.viz import plot_mel -from fish_diffusion.vocoders.nsf_hifigan.models import ( +from fish_diffusion.modules.vocoders.nsf_hifigan.models import ( AttrDict, Generator, MultiPeriodDiscriminator, @@ -25,6 +23,8 @@ feature_loss, generator_loss, ) +from fish_diffusion.utils.audio import dynamic_range_compression +from fish_diffusion.utils.viz import plot_mel torch.set_float32_matmul_precision("medium") diff --git a/tools/preprocessing/extract_features.py b/tools/preprocessing/extract_features.py index 2923ce8f..d2b15e6f 100644 --- a/tools/preprocessing/extract_features.py +++ b/tools/preprocessing/extract_features.py @@ -16,12 +16,13 @@ from mmengine import Config from tqdm import tqdm -from fish_diffusion.feature_extractors import FEATURE_EXTRACTORS, PITCH_EXTRACTORS -from fish_diffusion.feature_extractors.base import BaseFeatureExtractor -from fish_diffusion.feature_extractors.pitch.builder import BasePitchExtractor +from fish_diffusion.modules.feature_extractors import FEATURE_EXTRACTORS +from fish_diffusion.modules.feature_extractors.base import BaseFeatureExtractor +from fish_diffusion.modules.pitch_extractors import PITCH_EXTRACTORS +from fish_diffusion.modules.pitch_extractors.builder import BasePitchExtractor +from fish_diffusion.modules.vocoders import VOCODERS +from fish_diffusion.modules.vocoders.nsf_hifigan.nsf_hifigan import NsfHifiGAN from fish_diffusion.utils.tensor import repeat_expand -from fish_diffusion.vocoders import VOCODERS -from fish_diffusion.vocoders.nsf_hifigan.nsf_hifigan import NsfHifiGAN model_caches = None diff --git a/tools/whisper/train.py b/tools/whisper/train.py index abd1e279..2cc6a105 100644 --- a/tools/whisper/train.py +++ b/tools/whisper/train.py @@ -15,7 +15,7 @@ from torch.utils.data import DataLoader, Dataset from whisper import log_mel_spectrogram, pad_or_trim -from fish_diffusion.feature_extractors.whisper import AlignedWhisper +from fish_diffusion.modules.feature_extractors.whisper import AlignedWhisper phonemes = [] for i in open("dictionaries/opencpop-strict.txt"):