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@@ -27,6 +27,7 @@ print("You are in ", str(cur_work_dir))
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config_path = cur_work_dir / "fish_speech" / "configs"
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vqgan_yml_path = config_path / "vqgan_finetune.yaml"
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llama_yml_path = config_path / "text2semantic_finetune.yaml"
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+vits_yml_path = config_path / "vits_decoder_finetune.yaml"
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env = os.environ.copy()
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env["no_proxy"] = "127.0.0.1, localhost, 0.0.0.0"
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@@ -105,15 +106,19 @@ def change_label(if_label):
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if if_label == True and p_label is None:
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url = "http://localhost:3000"
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remote_url = "https://text-labeler.pages.dev/"
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- p_label = subprocess.Popen(
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- [
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- (
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- "asr-label-linux-x64"
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- if sys.platform == "linux"
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- else "asr-label-win-x64.exe"
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- )
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- ]
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- )
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+ try:
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+ p_label = subprocess.Popen(
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+ [
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+ (
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+ "asr-label-linux-x64"
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+ if sys.platform == "linux"
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+ else "asr-label-win-x64.exe"
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+ )
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+ ]
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+ )
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+ except FileNotFoundError:
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+ logger.warning("asr-label execution not found!")
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+
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yield build_html_href(
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link=remote_url,
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desc=i18n("Optional online ver"),
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@@ -146,7 +151,8 @@ def change_infer(
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if_infer,
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host,
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port,
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- infer_vqgan_model,
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+ infer_decoder_model,
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+ infer_decoder_config,
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infer_llama_model,
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infer_llama_config,
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infer_compile,
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@@ -169,8 +175,10 @@ def change_infer(
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[
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PYTHON,
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"tools/webui.py",
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- "--vqgan-checkpoint-path",
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- infer_vqgan_model,
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+ "--decoder-checkpoint-path",
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+ infer_decoder_model,
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+ "--decoder-config-name",
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+ infer_decoder_config,
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"--llama-checkpoint-path",
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infer_llama_model,
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"--llama-config-name",
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@@ -398,6 +406,8 @@ def check_files(data_path: str, max_depth: int, label_model: str, label_device:
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def train_process(
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data_path: str,
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option: str,
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+ min_duration: float,
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+ max_duration: float,
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# vq-gan config
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vqgan_ckpt,
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vqgan_lr,
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@@ -407,6 +417,15 @@ def train_process(
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vqgan_data_val_batch_size,
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vqgan_precision,
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vqgan_check_interval,
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+ # vits config
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+ vits_ckpt,
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+ vits_lr,
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+ vits_maxsteps,
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+ vits_data_num_workers,
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+ vits_data_batch_size,
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+ vits_data_val_batch_size,
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+ vits_precision,
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+ vits_check_interval,
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# llama config
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llama_ckpt,
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llama_base_config,
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@@ -434,27 +453,39 @@ def train_process(
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print("New Project Name: ", new_project)
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- if option == "VQGAN" or option == "all":
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+ if min_duration > max_duration:
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+ min_duration, max_duration = max_duration, min_duration
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+
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+ if option == "VQGAN" or option == "VITS":
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subprocess.run(
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[
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PYTHON,
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"tools/vqgan/create_train_split.py",
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str(data_pre_output.relative_to(cur_work_dir)),
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+ "--min-duration",
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+ str(min_duration),
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+ "--max-duration",
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+ str(max_duration),
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]
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)
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- latest = list(
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- sorted(
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- [
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- str(p.relative_to("results"))
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- for p in Path("results").glob("vqgan_*/")
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- ],
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- reverse=True,
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- )
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- )[0]
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+
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+ if option == "VQGAN":
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+ latest = next(
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+ iter(
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+ sorted(
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+ [
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+ str(p.relative_to("results"))
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+ for p in Path("results").glob("vqgan_*/")
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+ ],
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+ reverse=True,
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+ )
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+ ),
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+ ("vqgan_" + new_project),
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+ )
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project = (
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("vqgan_" + new_project)
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- if vqgan_ckpt == "new"
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- else latest if vqgan_ckpt == "latest" else vqgan_ckpt
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+ if vqgan_ckpt == i18n("new")
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+ else latest if vqgan_ckpt == i18n("latest") else vqgan_ckpt
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)
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logger.info(project)
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train_cmd = [
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@@ -477,7 +508,49 @@ def train_process(
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logger.info(train_cmd)
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subprocess.run(train_cmd)
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- if option == "LLAMA" or option == "all":
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+ if option == "VITS":
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+ latest = next(
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+ iter(
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+ sorted(
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+ [
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+ str(p.relative_to("results"))
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+ for p in Path("results").glob("vits_*/")
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+ ],
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+ reverse=True,
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+ )
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+ ),
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+ ("vits_" + new_project),
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+ )
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+ project = (
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+ ("vits_" + new_project)
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+ if vits_ckpt == i18n("new")
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+ else latest if vits_ckpt == i18n("latest") else vits_ckpt
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+ )
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+ ckpt_path = str(Path("checkpoints/vits_decoder_v1.1.ckpt"))
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+ logger.info(project)
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+ train_cmd = [
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+ PYTHON,
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+ "fish_speech/train.py",
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+ "--config-name",
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+ "vits_decoder_finetune",
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+ f"project={project}",
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+ f"ckpt_path={ckpt_path}",
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+ f"trainer.strategy.process_group_backend={backend}",
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+ "tokenizer.pretrained_model_name_or_path=checkpoints",
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+ f"model.optimizer.lr={vits_lr}",
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+ f"trainer.max_steps={vits_maxsteps}",
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+ f"data.num_workers={vits_data_num_workers}",
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+ f"data.batch_size={vits_data_batch_size}",
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+ f"data.val_batch_size={vits_data_val_batch_size}",
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+ f"trainer.precision={vits_precision}",
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+ f"trainer.val_check_interval={vits_check_interval}",
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+ f"train_dataset.filelist={str(data_pre_output / 'vq_train_filelist.txt')}",
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+ f"val_dataset.filelist={str(data_pre_output / 'vq_val_filelist.txt')}",
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+ ]
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+ logger.info(train_cmd)
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+ subprocess.run(train_cmd)
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+
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+ if option == "LLAMA":
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subprocess.run(
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[
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PYTHON,
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@@ -507,24 +580,27 @@ def train_process(
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]
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)
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ckpt_path = (
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- "text2semantic-pretrain-medium-2k-v1.pth"
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+ "text2semantic-sft-medium-v1.1-4k.pth"
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if llama_base_config == "dual_ar_2_codebook_medium"
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- else "text2semantic-sft-medium-v1-4k.pth"
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+ else "text2semantic-sft-large-v1.1-4k.pth"
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)
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- latest = list(
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- sorted(
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- [
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- str(p.relative_to("results"))
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- for p in Path("results").glob("text2sem*/")
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- ],
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- reverse=True,
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- )
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- )[0]
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+ latest = next(
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+ iter(
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+ sorted(
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+ [
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+ str(p.relative_to("results"))
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+ for p in Path("results").glob("text2sem*/")
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+ ],
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+ reverse=True,
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+ )
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+ ),
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+ ("text2semantic_" + new_project),
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+ )
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project = (
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("text2semantic_" + new_project)
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- if llama_ckpt == "new"
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- else latest if llama_ckpt == "latest" else llama_ckpt
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+ if llama_ckpt == i18n("new")
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+ else latest if llama_ckpt == i18n("latest") else llama_ckpt
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)
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logger.info(project)
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train_cmd = [
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@@ -596,22 +672,33 @@ def fresh_tb_dir():
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)
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-def fresh_vqgan_model():
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+def fresh_decoder_model():
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return gr.Dropdown(
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choices=[init_vqgan_yml["ckpt_path"]]
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+ + [str(Path("checkpoints/vits_decoder_v1.1.ckpt"))]
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+ [str(p) for p in Path("results").glob("vqgan*/**/*.ckpt")]
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+ + [str(p) for p in Path("results").glob("vits*/**/*.ckpt")]
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)
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def fresh_vqgan_ckpt():
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return gr.Dropdown(
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- choices=["latest", "new"] + [str(p) for p in Path("results").glob("vqgan_*/")]
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+ choices=[i18n("latest"), i18n("new")]
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+ + [str(p) for p in Path("results").glob("vqgan_*/")]
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+ )
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+
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+
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+def fresh_vits_ckpt():
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+ return gr.Dropdown(
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+ choices=[i18n("latest"), i18n("new")]
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+ + [str(p) for p in Path("results").glob("vits_*/")]
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)
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def fresh_llama_ckpt():
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return gr.Dropdown(
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- choices=["latest", "new"] + [str(p) for p in Path("results").glob("text2sem*/")]
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+ choices=[i18n("latest"), i18n("new")]
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+ + [str(p) for p in Path("results").glob("text2sem*/")]
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)
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@@ -655,6 +742,7 @@ def llama_lora_merge(llama_weight, lora_llama_config, lora_weight, llama_lora_ou
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init_vqgan_yml = load_yaml_data_in_fact(vqgan_yml_path)
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init_llama_yml = load_yaml_data_in_fact(llama_yml_path)
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+init_vits_yml = load_yaml_data_in_fact(vits_yml_path)
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with gr.Blocks(
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head="<style>\n" + css + "\n</style>",
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@@ -687,6 +775,22 @@ with gr.Blocks(
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if_label = gr.Checkbox(
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label=i18n("Open Labeler WebUI"), scale=0, show_label=True
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)
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+ with gr.Row():
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+ min_duration = gr.Slider(
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+ label=i18n("Minimum Audio Duration"),
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+ value=1.5,
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+ step=0.1,
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+ minimum=0.4,
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+ maximum=30,
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+ )
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+ max_duration = gr.Slider(
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+ label=i18n("Maximum Audio Duration"),
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+ value=30,
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+ step=0.1,
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+ minimum=0.4,
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+ maximum=30,
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+ )
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+
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with gr.Row():
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add_button = gr.Button(
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"\U000027A1 " + i18n("Add to Processing Area"),
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@@ -735,17 +839,17 @@ with gr.Blocks(
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model_type_radio = gr.Radio(
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label=i18n("Select the model to be trained"),
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interactive=True,
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- choices=["VQGAN", "LLAMA", "all"],
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- value="all",
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+ choices=["VQGAN", "VITS", "LLAMA"],
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+ value="VITS",
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)
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with gr.Row():
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with gr.Tab(label=i18n("VQGAN Configuration")):
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with gr.Row(equal_height=False):
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vqgan_ckpt = gr.Dropdown(
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- label="Select VQGAN ckpt",
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- choices=["latest", "new"]
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+ label=i18n("Select VQGAN ckpt"),
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+ choices=[i18n("latest"), i18n("new")]
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+ [str(p) for p in Path("results").glob("vqgan_*/")],
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- value="latest",
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+ value=i18n("latest"),
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interactive=True,
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)
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with gr.Row(equal_height=False):
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@@ -812,6 +916,79 @@ with gr.Blocks(
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value=init_vqgan_yml["trainer"]["val_check_interval"],
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)
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+ with gr.Tab(label=i18n("VITS Configuration")):
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+ with gr.Row(equal_height=False):
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+ vits_ckpt = gr.Dropdown(
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+ label=i18n("Select VITS ckpt"),
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+ choices=[i18n("latest"), i18n("new")]
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+ + [str(p) for p in Path("results").glob("vits_*/")],
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+ value=i18n("latest"),
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+ interactive=True,
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+ )
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+ with gr.Row(equal_height=False):
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+ vits_lr_slider = gr.Slider(
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+ label=i18n("Initial Learning Rate"),
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+ interactive=True,
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+ minimum=1e-5,
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+ maximum=1e-4,
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+ step=1e-5,
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+ value=init_vits_yml["model"]["optimizer"]["lr"],
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+ )
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+ vits_maxsteps_slider = gr.Slider(
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+ label=i18n("Maximum Training Steps"),
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+ interactive=True,
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+ minimum=1000,
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+ maximum=100000,
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+ step=1000,
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+ value=init_vits_yml["trainer"]["max_steps"],
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+ )
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+
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+ with gr.Row(equal_height=False):
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+ vits_data_num_workers_slider = gr.Slider(
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+ label=i18n("Number of Workers"),
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+ interactive=True,
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+ minimum=1,
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+ maximum=16,
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+ step=1,
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+ value=init_vits_yml["data"]["num_workers"],
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+ )
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+
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+ vits_data_batch_size_slider = gr.Slider(
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+ label=i18n("Batch Size"),
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+ interactive=True,
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+ minimum=1,
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+ maximum=32,
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+ step=1,
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+ value=init_vits_yml["data"]["batch_size"],
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+ )
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+ with gr.Row(equal_height=False):
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+ vits_data_val_batch_size_slider = gr.Slider(
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+ label=i18n("Validation Batch Size"),
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+ interactive=True,
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+ minimum=1,
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+ maximum=32,
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+ step=1,
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+ value=init_vits_yml["data"]["val_batch_size"],
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+ )
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+ vits_precision_dropdown = gr.Dropdown(
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+ label=i18n("Precision"),
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+ interactive=True,
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+ choices=["32", "bf16-true", "bf16-mixed"],
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+ info=i18n(
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+ "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU"
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+ ),
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+ value=str(init_vits_yml["trainer"]["precision"]),
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+ )
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+ with gr.Row(equal_height=False):
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+ vits_check_interval_slider = gr.Slider(
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+ label=i18n("Save model every n steps"),
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+ interactive=True,
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+ minimum=500,
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+ maximum=10000,
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+ step=500,
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+ value=init_vits_yml["trainer"]["val_check_interval"],
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+ )
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+
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with gr.Tab(label=i18n("LLAMA Configuration")):
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with gr.Row(equal_height=False):
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llama_use_lora = gr.Checkbox(
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|
@@ -822,10 +999,10 @@ with gr.Blocks(
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value=True,
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)
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llama_ckpt = gr.Dropdown(
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- label="Select LLAMA ckpt",
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- choices=["latest", "new"]
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+ label=i18n("Select LLAMA ckpt"),
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+ choices=[i18n("latest"), i18n("new")]
|
|
|
+ [str(p) for p in Path("results").glob("text2sem*/")],
|
|
|
- value="latest",
|
|
|
+ value=i18n("latest"),
|
|
|
interactive=True,
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
@@ -943,6 +1120,7 @@ with gr.Blocks(
|
|
|
)
|
|
|
lora_llama_config = gr.Dropdown(
|
|
|
label=i18n("LLAMA Model Config"),
|
|
|
+ info=i18n("Type the path or select from the dropdown"),
|
|
|
choices=[
|
|
|
"dual_ar_2_codebook_large",
|
|
|
"dual_ar_2_codebook_medium",
|
|
|
@@ -1004,18 +1182,39 @@ with gr.Blocks(
|
|
|
label=i18n("WebUI Port"), value="7862"
|
|
|
)
|
|
|
with gr.Row():
|
|
|
- infer_vqgan_model = gr.Dropdown(
|
|
|
- label=i18n("VQGAN Model Path"),
|
|
|
+ infer_decoder_model = gr.Dropdown(
|
|
|
+ label=i18n("Decoder Model Path"),
|
|
|
info=i18n(
|
|
|
"Type the path or select from the dropdown"
|
|
|
),
|
|
|
- value=init_vqgan_yml["ckpt_path"],
|
|
|
+ value=str(
|
|
|
+ Path("checkpoints/vits_decoder_v1.1.ckpt")
|
|
|
+ ),
|
|
|
choices=[init_vqgan_yml["ckpt_path"]]
|
|
|
+ + [str(Path("checkpoints/vits_decoder_v1.1.ckpt"))]
|
|
|
+ [
|
|
|
str(p)
|
|
|
for p in Path("results").glob(
|
|
|
"vqgan*/**/*.ckpt"
|
|
|
)
|
|
|
+ ]
|
|
|
+ + [
|
|
|
+ str(p)
|
|
|
+ for p in Path("results").glob("vits*/**/*.ckpt")
|
|
|
+ ],
|
|
|
+ allow_custom_value=True,
|
|
|
+ )
|
|
|
+ infer_decoder_config = gr.Dropdown(
|
|
|
+ label=i18n("Decoder Model Config"),
|
|
|
+ info=i18n(
|
|
|
+ "Type the path or select from the dropdown"
|
|
|
+ ),
|
|
|
+ value="vits_decoder_finetune",
|
|
|
+ choices=[
|
|
|
+ "vits_decoder_finetune",
|
|
|
+ "vits_decoder_pretrain",
|
|
|
+ "vqgan_finetune",
|
|
|
+ "vqgan_pretrain",
|
|
|
],
|
|
|
allow_custom_value=True,
|
|
|
)
|
|
|
@@ -1035,6 +1234,18 @@ with gr.Blocks(
|
|
|
],
|
|
|
allow_custom_value=True,
|
|
|
)
|
|
|
+ infer_llama_config = gr.Dropdown(
|
|
|
+ label=i18n("LLAMA Model Config"),
|
|
|
+ info=i18n(
|
|
|
+ "Type the path or select from the dropdown"
|
|
|
+ ),
|
|
|
+ choices=[
|
|
|
+ "dual_ar_2_codebook_large",
|
|
|
+ "dual_ar_2_codebook_medium",
|
|
|
+ ],
|
|
|
+ value="dual_ar_2_codebook_large",
|
|
|
+ allow_custom_value=True,
|
|
|
+ )
|
|
|
with gr.Row():
|
|
|
infer_compile = gr.Radio(
|
|
|
label=i18n("Compile Model"),
|
|
|
@@ -1052,15 +1263,6 @@ with gr.Blocks(
|
|
|
),
|
|
|
interactive=is_module_installed("triton"),
|
|
|
)
|
|
|
- infer_llama_config = gr.Dropdown(
|
|
|
- label=i18n("LLAMA Model Config"),
|
|
|
- choices=[
|
|
|
- "dual_ar_2_codebook_large",
|
|
|
- "dual_ar_2_codebook_medium",
|
|
|
- ],
|
|
|
- value="dual_ar_2_codebook_large",
|
|
|
- allow_custom_value=True,
|
|
|
- )
|
|
|
|
|
|
with gr.Row():
|
|
|
infer_checkbox = gr.Checkbox(
|
|
|
@@ -1140,6 +1342,8 @@ with gr.Blocks(
|
|
|
inputs=[
|
|
|
train_box,
|
|
|
model_type_radio,
|
|
|
+ min_duration,
|
|
|
+ max_duration,
|
|
|
# vq-gan config
|
|
|
vqgan_ckpt,
|
|
|
vqgan_lr_slider,
|
|
|
@@ -1149,6 +1353,15 @@ with gr.Blocks(
|
|
|
vqgan_data_val_batch_size_slider,
|
|
|
vqgan_precision_dropdown,
|
|
|
vqgan_check_interval_slider,
|
|
|
+ # vits config
|
|
|
+ vits_ckpt,
|
|
|
+ vits_lr_slider,
|
|
|
+ vits_maxsteps_slider,
|
|
|
+ vits_data_num_workers_slider,
|
|
|
+ vits_data_batch_size_slider,
|
|
|
+ vits_data_val_batch_size_slider,
|
|
|
+ vits_precision_dropdown,
|
|
|
+ vits_check_interval_slider,
|
|
|
# llama config
|
|
|
llama_ckpt,
|
|
|
llama_base_config,
|
|
|
@@ -1171,8 +1384,8 @@ with gr.Blocks(
|
|
|
outputs=[train_error],
|
|
|
)
|
|
|
tb_dir.change(fn=fresh_tb_dir, inputs=[], outputs=[tb_dir])
|
|
|
- infer_vqgan_model.change(
|
|
|
- fn=fresh_vqgan_model, inputs=[], outputs=[infer_vqgan_model]
|
|
|
+ infer_decoder_model.change(
|
|
|
+ fn=fresh_decoder_model, inputs=[], outputs=[infer_decoder_model]
|
|
|
)
|
|
|
infer_llama_model.change(
|
|
|
fn=fresh_llama_model, inputs=[], outputs=[infer_llama_model]
|
|
|
@@ -1187,6 +1400,7 @@ with gr.Blocks(
|
|
|
fn=new_explorer, inputs=[train_box, tree_slider], outputs=[file_markdown]
|
|
|
)
|
|
|
vqgan_ckpt.change(fn=fresh_vqgan_ckpt, inputs=[], outputs=[vqgan_ckpt])
|
|
|
+ vits_ckpt.change(fn=fresh_vits_ckpt, inputs=[], outputs=[vits_ckpt])
|
|
|
llama_ckpt.change(fn=fresh_llama_ckpt, inputs=[], outputs=[llama_ckpt])
|
|
|
llama_lora_merge_btn.click(
|
|
|
fn=llama_lora_merge,
|
|
|
@@ -1199,7 +1413,8 @@ with gr.Blocks(
|
|
|
infer_checkbox,
|
|
|
infer_host_textbox,
|
|
|
infer_port_textbox,
|
|
|
- infer_vqgan_model,
|
|
|
+ infer_decoder_model,
|
|
|
+ infer_decoder_config,
|
|
|
infer_llama_model,
|
|
|
infer_llama_config,
|
|
|
infer_compile,
|
spicysama