user-friendly wandb support

README.md

@@ -67,6 +67,27 @@ An initial guidance on Finetuning [#57](https://github.com/SWivid/F5-TTS/discuss
 
 Gradio UI finetuning with `finetune_gradio.py` see [#143](https://github.com/SWivid/F5-TTS/discussions/143).
 
+## Wandb Logging
+
+By default, the training script does NOT use logging (assuming you didn't manually log in using `wandb login`).
+
+To turn on wandb logging, you can either:
+
+1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login)
+2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows:
+
+On Mac & Linux:
+
+```
+export WANDB_API_KEY=<YOUR WANDB API KEY>
+```
+
+On Windows:
+
+```
+set WANDB_API_KEY=<YOUR WANDB API KEY>
+```
+
 ## Inference
 
 The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), or automatically downloaded with `inference-cli` and `gradio_app`.

model/trainer.py

@@ -50,31 +50,35 @@ class Trainer:
         
         ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters = True)
 
+        logger = "wandb" if wandb.api.api_key else None
+        print(f"Using logger: {logger}")
+
         self.accelerator = Accelerator(
-            log_with = "wandb",
+            log_with = logger,
             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:
-            init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name}}
-        self.accelerator.init_trackers(
-            project_name = wandb_project, 
-            init_kwargs=init_kwargs,
-            config={"epochs": epochs,
-                    "learning_rate": learning_rate,
-                    "num_warmup_updates": num_warmup_updates, 
-                    "batch_size": batch_size,
-                    "batch_size_type": batch_size_type,
-                    "max_samples": max_samples,
-                    "grad_accumulation_steps": grad_accumulation_steps,
-                    "max_grad_norm": max_grad_norm,
-                    "gpus": self.accelerator.num_processes,
-                    "noise_scheduler": noise_scheduler}
-            )
+
+        if logger == "wandb":
+            if exists(wandb_resume_id):
+                init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name, 'id': wandb_resume_id}}
+            else:
+                init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name}}
+            self.accelerator.init_trackers(
+                project_name = wandb_project,
+                init_kwargs=init_kwargs,
+                config={"epochs": epochs,
+                        "learning_rate": learning_rate,
+                        "num_warmup_updates": num_warmup_updates,
+                        "batch_size": batch_size,
+                        "batch_size_type": batch_size_type,
+                        "max_samples": max_samples,
+                        "grad_accumulation_steps": grad_accumulation_steps,
+                        "max_grad_norm": max_grad_norm,
+                        "gpus": self.accelerator.num_processes,
+                        "noise_scheduler": noise_scheduler}
+                )
 
         self.model = model