Merge pull request SWivid#196 from thunn/add_pre_commit_tooling

add and run pre-commit with ruff

.github/workflows/pre-commit.yaml

@@ -0,0 +1,14 @@
+name: pre-commit
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v3
+      - uses: pre-commit/[email protected]

.pre-commit-config.yaml

@@ -0,0 +1,14 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.7.0
+    hooks:
+      # Run the linter.
+      - id: ruff
+        args: [--fix]
+      # Run the formatter.
+      - id: ruff-format
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+      - id: check-yaml

README.md

@@ -43,6 +43,26 @@ pip install -r requirements.txt
 docker build -t f5tts:v1 .
 ```
 
+### Development
+
+When making a pull request, please use pre-commit to ensure code quality:
+
+```bash
+pip install pre-commit
+pre-commit install
+```
+
+This will run linters and formatters automatically before each commit.
+
+Manually run using: 
+
+```bash
+pre-commit run --all-files
+```
+
+Note: Some model components have linting exceptions for E722 to accommodate tensor notation
+
+
 ## Prepare Dataset
 
 Example data processing scripts for Emilia and Wenetspeech4TTS, and you may tailor your own one along with a Dataset class in `model/dataset.py`.

finetune-cli.py

@@ -1,67 +1,89 @@
 import argparse
-from model import CFM, UNetT, DiT, MMDiT, Trainer
+from model import CFM, UNetT, DiT, Trainer
 from model.utils import get_tokenizer
 from model.dataset import load_dataset
 from cached_path import cached_path
-import shutil,os
+import shutil
+import os
+
 # -------------------------- Dataset Settings --------------------------- #
 target_sample_rate = 24000
 n_mel_channels = 100
 hop_length = 256
 
-tokenizer = "pinyin"  # 'pinyin', 'char', or 'custom'
-tokenizer_path = None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
 
 # -------------------------- Argument Parsing --------------------------- #
 def parse_args():
-    parser = argparse.ArgumentParser(description='Train CFM Model')
-
-    parser.add_argument('--exp_name', type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"],help='Experiment name')
-    parser.add_argument('--dataset_name', type=str, default="Emilia_ZH_EN", help='Name of the dataset to use')
-    parser.add_argument('--learning_rate', type=float, default=1e-4, help='Learning rate for training')
-    parser.add_argument('--batch_size_per_gpu', type=int, default=256, help='Batch size per GPU')
-    parser.add_argument('--batch_size_type', type=str, default="frame", choices=["frame", "sample"],help='Batch size type')
-    parser.add_argument('--max_samples', type=int, default=16, help='Max sequences per batch')
-    parser.add_argument('--grad_accumulation_steps', type=int, default=1,help='Gradient accumulation steps')
-    parser.add_argument('--max_grad_norm', type=float, default=1.0, help='Max gradient norm for clipping')
-    parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
-    parser.add_argument('--num_warmup_updates', type=int, default=5, help='Warmup steps')
-    parser.add_argument('--save_per_updates', type=int, default=10, help='Save checkpoint every X steps')
-    parser.add_argument('--last_per_steps', type=int, default=10, help='Save last checkpoint every X steps')
-    parser.add_argument('--finetune', type=bool, default=True, help='Use Finetune')
-
+    parser = argparse.ArgumentParser(description="Train CFM Model")
+
+    parser.add_argument(
+        "--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
+    )
+    parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
+    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate for training")
+    parser.add_argument("--batch_size_per_gpu", type=int, default=256, help="Batch size per GPU")
+    parser.add_argument(
+        "--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
+    )
+    parser.add_argument("--max_samples", type=int, default=16, help="Max sequences per batch")
+    parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
+    parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
+    parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs")
+    parser.add_argument("--num_warmup_updates", type=int, default=5, help="Warmup steps")
+    parser.add_argument("--save_per_updates", type=int, default=10, help="Save checkpoint every X steps")
+    parser.add_argument("--last_per_steps", type=int, default=10, help="Save last checkpoint every X steps")
+    parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
+
+    parser.add_argument(
+        "--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
+    )
+    parser.add_argument(
+        "--tokenizer_path",
+        type=str,
+        default=None,
+        help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
+    )
+
     return parser.parse_args()
 
+
 # -------------------------- Training Settings -------------------------- #
 
+
 def main():
     args = parse_args()
-
 
     # Model parameters based on experiment name
     if args.exp_name == "F5TTS_Base":
         wandb_resume_id = None
         model_cls = DiT
         model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
         if args.finetune:
-           ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+            ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
     elif args.exp_name == "E2TTS_Base":
         wandb_resume_id = None
         model_cls = UNetT
         model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
         if args.finetune:
-           ckpt_path = str(cached_path(f"hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
-           
+            ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+
     if args.finetune:
-       path_ckpt = os.path.join("ckpts",args.dataset_name)
-       if os.path.isdir(path_ckpt)==False:
-          os.makedirs(path_ckpt,exist_ok=True)
-          shutil.copy2(ckpt_path,os.path.join(path_ckpt,os.path.basename(ckpt_path)))
-
-    checkpoint_path=os.path.join("ckpts",args.dataset_name)
-
-    # Use the dataset_name provided in the command line
-    tokenizer_path = args.dataset_name if tokenizer != "custom" else tokenizer_path
+        path_ckpt = os.path.join("ckpts", args.dataset_name)
+        if not os.path.isdir(path_ckpt):
+            os.makedirs(path_ckpt, exist_ok=True)
+            shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path)))
+
+    checkpoint_path = os.path.join("ckpts", args.dataset_name)
+
+    # Use the tokenizer and tokenizer_path provided in the command line arguments
+    tokenizer = args.tokenizer
+    if tokenizer == "custom":
+        if not args.tokenizer_path:
+            raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
+        tokenizer_path = args.tokenizer_path
+    else:
+        tokenizer_path = args.dataset_name
+
     vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
 
     mel_spec_kwargs = dict(
@@ -71,11 +93,7 @@ def main():
     )
 
     e2tts = CFM(
-        transformer=model_cls(
-            **model_cfg,
-            text_num_embeds=vocab_size,
-            mel_dim=n_mel_channels
-        ),
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
         mel_spec_kwargs=mel_spec_kwargs,
         vocab_char_map=vocab_char_map,
     )
@@ -99,10 +117,11 @@ def main():
     )
 
     train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
-    trainer.train(train_dataset,
-                  resumable_with_seed=666  # seed for shuffling dataset
-                  )
+    trainer.train(
+        train_dataset,
+        resumable_with_seed=666,  # seed for shuffling dataset
+    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()