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basic structure

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SWivid
2024-10-30 02:38:58 +08:00
parent 5b10099d33
commit da1b40968a
4 changed files with 58 additions and 191 deletions
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134
src/f5_tts/model/trainer.py
View File

@@ -3,9 +3,10 @@ from __future__ import annotations
import os
import gc
from tqdm import tqdm
import wandb
import torch
import torchaudio
from torch.optim import AdamW
from torch.utils.data import DataLoader, Dataset, SequentialSampler
from torch.optim.lr_scheduler import LinearLR, SequentialLR
@@ -19,6 +20,7 @@ from f5_tts.model import CFM
from f5_tts.model.utils import exists, default
from f5_tts.model.dataset import DynamicBatchSampler, collate_fn
# trainer
@@ -38,33 +40,32 @@ class Trainer:
max_grad_norm=1.0,
noise_scheduler: str | None = None,
duration_predictor: torch.nn.Module | None = None,
logger: str = "wandb", # Add logger parameter wandb,tensorboard , none
log_dir: str = "logs", # Add log directory parameter
logger: str | None = "wandb", # "wandb" | "tensorboard" | None
wandb_project="test_e2-tts",
wandb_run_name="test_run",
wandb_resume_id: str = None,
log_samples: bool = False,
last_per_steps=None,
accelerate_kwargs: dict = dict(),
ema_kwargs: dict = dict(),
bnb_optimizer: bool = False,
export_samples=False,
):
# export audio and mel
self.export_samples = export_samples
if export_samples:
self.path_ckpts_project = checkpoint_path
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
if logger == "wandb" and not wandb.api.api_key:
logger = None
print(f"Using logger: {logger}")
self.log_samples = log_samples
self.accelerator = Accelerator(
log_with=logger if logger == "wandb" else None,
kwargs_handlers=[ddp_kwargs],
gradient_accumulation_steps=grad_accumulation_steps,
**accelerate_kwargs,
)
self.logger = logger
if self.logger == "wandb":
self.accelerator = Accelerator(
log_with="wandb",
kwargs_handlers=[ddp_kwargs],
gradient_accumulation_steps=grad_accumulation_steps,
**accelerate_kwargs,
)
if exists(wandb_resume_id):
init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
else:
@@ -86,24 +87,11 @@ class Trainer:
"noise_scheduler": noise_scheduler,
},
)
elif self.logger == "tensorboard":
from torch.utils.tensorboard import SummaryWriter
self.accelerator = Accelerator(
kwargs_handlers=[ddp_kwargs],
gradient_accumulation_steps=grad_accumulation_steps,
**accelerate_kwargs,
)
if self.is_main:
path_log_dir = os.path.join(log_dir, wandb_project)
os.makedirs(path_log_dir, exist_ok=True)
existing_folders = [folder for folder in os.listdir(path_log_dir) if folder.startswith("exp")]
next_number = len(existing_folders) + 2
folder_name = f"exp{next_number}"
folder_path = os.path.join(path_log_dir, folder_name)
os.makedirs(folder_path, exist_ok=True)
self.writer = SummaryWriter(log_dir=folder_path)
self.writer = SummaryWriter(log_dir=f"runs/{wandb_run_name}")
self.model = model
@@ -198,31 +186,13 @@ class Trainer:
gc.collect()
return step
def log(self, metrics, step):
"""Unified logging method for both WandB and TensorBoard"""
if self.logger == "none":
return
if self.logger == "wandb":
self.accelerator.log(metrics, step=step)
elif self.is_main:
for key, value in metrics.items():
self.writer.add_scalar(key, value, step)
def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None):
# import only when export_sample True
if self.export_samples:
from f5_tts.infer.utils_infer import (
target_sample_rate,
hop_length,
nfe_step,
cfg_strength,
sway_sampling_coef,
vocos,
)
from f5_tts.model.utils import get_sample
if self.log_samples:
from f5_tts.infer.utils_infer import vocos, nfe_step, cfg_strength, sway_sampling_coef
self.file_path_samples = os.path.join(self.path_ckpts_project, "samples")
os.makedirs(self.file_path_samples, exist_ok=True)
target_sample_rate = self.model.mel_spec.mel_stft.sample_rate
log_samples_path = f"{self.checkpoint_path}/samples"
os.makedirs(log_samples_path, exist_ok=True)
if exists(resumable_with_seed):
generator = torch.Generator()
@@ -307,7 +277,6 @@ class Trainer:
for batch in progress_bar:
with self.accelerator.accumulate(self.model):
text_inputs = batch["text"]
mel_spec = batch["mel"].permute(0, 2, 1)
mel_lengths = batch["mel_lengths"]
@@ -319,40 +288,6 @@ class Trainer:
loss, cond, pred = self.model(
mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler
)
# save 4 audio per save step
if (
self.accelerator.is_local_main_process
and self.export_samples
and global_step % (int(self.save_per_updates * 0.25) * self.grad_accumulation_steps) == 0
):
try:
wave_org, wave_gen, mel_org, mel_gen = get_sample(
vocos,
self.model,
self.file_path_samples,
global_step,
batch["mel"][0],
text_inputs,
target_sample_rate,
hop_length,
nfe_step,
cfg_strength,
sway_sampling_coef,
)
if self.logger == "tensorboard":
self.writer.add_audio(
"Audio/original", wave_org, global_step, sample_rate=target_sample_rate
)
self.writer.add_audio(
"Audio/generate", wave_gen, global_step, sample_rate=target_sample_rate
)
self.writer.add_image("Mel/original", mel_org, global_step, dataformats="CHW")
self.writer.add_image("Mel/generate", mel_gen, global_step, dataformats="CHW")
except Exception as e:
print("An error occurred:", e)
self.accelerator.backward(loss)
if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
@@ -368,13 +303,32 @@ class Trainer:
global_step += 1
if self.accelerator.is_local_main_process:
self.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step)
self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step)
if self.logger == "tensorboard":
self.writer.add_scalar("loss", loss.item(), global_step)
self.writer.add_scalar("lr", self.scheduler.get_last_lr()[0], global_step)
progress_bar.set_postfix(step=str(global_step), loss=loss.item())
if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0:
self.save_checkpoint(global_step)
if self.log_samples:
ref_audio, ref_audio_len = vocos.decode([batch["mel"][0]].cpu()), mel_lengths[0]
torchaudio.save(f"{log_samples_path}/step_{global_step}_ref.wav", ref_audio, target_sample_rate)
with torch.inference_mode():
generated, _ = self.model.sample(
cond=[mel_spec[0][:ref_audio_len]],
text=[text_inputs[0] + [" "] + text_inputs[0]],
duration=ref_audio_len * 2,
steps=nfe_step,
cfg_strength=cfg_strength,
sway_sampling_coef=sway_sampling_coef,
)
generated = generated.to(torch.float32)
gen_audio = vocos.decode(generated[:, ref_audio_len:, :].permute(0, 2, 1).cpu())
torchaudio.save(f"{log_samples_path}/step_{global_step}_gen.wav", gen_audio, target_sample_rate)
if global_step % self.last_per_steps == 0:
self.save_checkpoint(global_step, last=True)

75
src/f5_tts/model/utils.py
View File

@@ -11,10 +11,6 @@ from torch.nn.utils.rnn import pad_sequence
import jieba
from pypinyin import lazy_pinyin, Style
import numpy as np
import matplotlib.pyplot as plt
import soundfile as sf
import torchaudio
# seed everything
@@ -187,74 +183,3 @@ def repetition_found(text, length=2, tolerance=10):
if count > tolerance:
return True
return False
def normalize_and_colorize_spectrogram(mel_org):
mel_min, mel_max = mel_org.min(), mel_org.max()
mel_norm = (mel_org - mel_min) / (mel_max - mel_min + 1e-8)
mel_colored = plt.get_cmap("viridis")(mel_norm.detach().cpu().numpy())[:, :, :3]
mel_colored = np.transpose(mel_colored, (2, 0, 1))
mel_colored = np.flip(mel_colored, axis=1)
return mel_colored
def export_audio(file_out, wav, target_sample_rate):
sf.write(file_out, wav, samplerate=target_sample_rate)
def export_mel(mel_colored_hwc, file_out):
plt.imsave(file_out, mel_colored_hwc)
def gen_sample(model, vocos, file_wav_org, text_inputs, hop_length, nfe_step, cfg_strength, sway_sampling_coef):
audio, sr = torchaudio.load(file_wav_org)
audio = audio.to("cuda")
ref_audio_len = audio.shape[-1] // hop_length
text = [text_inputs[0] + [" . "] + text_inputs[0]]
duration = int((audio.shape[1] / 256) * 2.0)
with torch.inference_mode():
generated_gen, _ = model.sample(
cond=audio,
text=text,
duration=duration,
steps=nfe_step,
cfg_strength=cfg_strength,
sway_sampling_coef=sway_sampling_coef,
)
generated_gen = generated_gen.to(torch.float32)
generated_gen = generated_gen[:, ref_audio_len:, :]
generated_mel_spec_gen = generated_gen.permute(0, 2, 1)
generated_wave_gen = vocos.decode(generated_mel_spec_gen.cpu())
generated_wave_gen = generated_wave_gen.squeeze().cpu().numpy()
return generated_wave_gen, generated_mel_spec_gen
def get_sample(
vocos,
model,
file_path_samples,
global_step,
mel_org,
text_inputs,
target_sample_rate,
hop_length,
nfe_step,
cfg_strength,
sway_sampling_coef,
):
generated_wave_org = vocos.decode(mel_org.unsqueeze(0).cpu())
generated_wave_org = generated_wave_org.squeeze().cpu().numpy()
file_wav_org = os.path.join(file_path_samples, f"step_{global_step}_org.wav")
export_audio(file_wav_org, generated_wave_org, target_sample_rate)
generated_wave_gen, generated_mel_spec_gen = gen_sample(
model, vocos, file_wav_org, text_inputs, hop_length, nfe_step, cfg_strength, sway_sampling_coef
)
file_wav_gen = os.path.join(file_path_samples, f"step_{global_step}_gen.wav")
export_audio(file_wav_gen, generated_wave_gen, target_sample_rate)
mel_org = normalize_and_colorize_spectrogram(mel_org)
mel_gen = normalize_and_colorize_spectrogram(generated_mel_spec_gen[0])
file_gen_org = os.path.join(file_path_samples, f"step_{global_step}_org.png")
export_mel(np.transpose(mel_org, (1, 2, 0)), file_gen_org)
file_gen_gen = os.path.join(file_path_samples, f"step_{global_step}_gen.png")
export_mel(np.transpose(mel_gen, (1, 2, 0)), file_gen_gen)
return generated_wave_org, generated_wave_gen, mel_org, mel_gen

10
src/f5_tts/train/finetune_cli.py
View File

@@ -57,12 +57,12 @@ def parse_args():
)
parser.add_argument(
"--export_samples",
"--log_samples",
type=bool,
default=False,
help="Export 4 audio and spect samples for the checkpoint audio, per step.",
help="Log inferenced samples per ckpt save steps",
)
parser.add_argument("--logger", type=str, default="wandb", choices=["none", "wandb", "tensorboard"], help="logger")
parser.add_argument("--logger", type=str, default=None, choices=["wandb", "tensorboard"], help="logger")
return parser.parse_args()
@@ -141,12 +141,12 @@ def main():
max_samples=args.max_samples,
grad_accumulation_steps=args.grad_accumulation_steps,
max_grad_norm=args.max_grad_norm,
logger=args.logger,
wandb_project=args.dataset_name,
wandb_run_name=args.exp_name,
wandb_resume_id=wandb_resume_id,
log_samples=args.log_samples,
last_per_steps=args.last_per_steps,
logger=args.logger,
export_samples=args.export_samples,
)
train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)

30
src/f5_tts/train/finetune_gradio.py
View File

@@ -453,7 +453,7 @@ def start_training(
cmd += f" --tokenizer {tokenizer_type} "
cmd += f" --export_samples True --logger {logger} "
cmd += f" --log_samples True --logger {logger} "
print(cmd)
@@ -1321,18 +1321,14 @@ def get_combined_stats():
def get_audio_select(file_sample):
select_audio_org = file_sample
select_audio_ref = file_sample
select_audio_gen = file_sample
select_image_org = file_sample
select_image_gen = file_sample
if file_sample is not None:
select_audio_org += "_org.wav"
select_audio_ref += "_ref.wav"
select_audio_gen += "_gen.wav"
select_image_org += "_org.png"
select_image_gen += "_gen.png"
return select_audio_org, select_audio_gen, select_image_org, select_image_gen
return select_audio_ref, select_audio_gen
with gr.Blocks() as app:
@@ -1515,7 +1511,7 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
with gr.Row():
mixed_precision = gr.Radio(label="mixed_precision", choices=["none", "fp16", "fpb16"], value="none")
cd_logger = gr.Radio(label="logger", choices=["none", "wandb", "tensorboard"], value="wandb")
cd_logger = gr.Radio(label="logger", choices=["wandb", "tensorboard"], value="wandb")
start_button = gr.Button("Start Training")
stop_button = gr.Button("Stop Training", interactive=False)
@@ -1562,16 +1558,12 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
list_audios, select_audio = get_audio_project(projects_selelect, False)
select_audio_org = select_audio
select_audio_ref = select_audio
select_audio_gen = select_audio
select_image_org = select_audio
select_image_gen = select_audio
if select_audio is not None:
select_audio_org += "_org.wav"
select_audio_ref += "_ref.wav"
select_audio_gen += "_gen.wav"
select_image_org += "_org.png"
select_image_gen += "_gen.png"
with gr.Row():
ch_list_audio = gr.Dropdown(
@@ -1587,17 +1579,13 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
cm_project.change(fn=get_audio_project, inputs=[cm_project], outputs=[ch_list_audio])
with gr.Row():
audio_org_stream = gr.Audio(label="original", type="filepath", value=select_audio_org)
mel_org_stream = gr.Image(label="original", type="filepath", value=select_image_org)
with gr.Row():
audio_ref_stream = gr.Audio(label="original", type="filepath", value=select_audio_ref)
audio_gen_stream = gr.Audio(label="generate", type="filepath", value=select_audio_gen)
mel_gen_stream = gr.Image(label="generate", type="filepath", value=select_image_gen)
ch_list_audio.change(
fn=get_audio_select,
inputs=[ch_list_audio],
outputs=[audio_org_stream, audio_gen_stream, mel_org_stream, mel_gen_stream],
outputs=[audio_ref_stream, audio_gen_stream],
)
start_button.click(