Update: VoxCPM1.5 and fine-tuning supprt

src/voxcpm/training/tracker.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import contextlib
+import time
+from pathlib import Path
+from typing import Dict, Optional
+
+
+class TrainingTracker:
+    """
+    Lightweight tracker inspired by the minimcpm-audio training workflow.
+
+    It keeps track of the current global step, prints rank-aware messages,
+    optionally writes to TensorBoard via a provided writer, and stores progress
+    in a logfile for later inspection.
+    """
+
+    def __init__(
+        self,
+        *,
+        writer=None,
+        log_file: Optional[str] = None,
+        rank: int = 0,
+    ):
+        self.writer = writer
+        self.log_file = Path(log_file) if log_file else None
+        if self.log_file:
+            self.log_file.parent.mkdir(parents=True, exist_ok=True)
+        self.rank = rank
+        self.step = 0
+        # Record the time of the last log to calculate the interval
+        self._last_log_time: float | None = None
+
+    # ------------------------------------------------------------------ #
+    # Logging helpers
+    # ------------------------------------------------------------------ #
+    def print(self, message: str):
+        if self.rank == 0:
+            print(message, flush=True)
+            if self.log_file:
+                with self.log_file.open("a", encoding="utf-8") as f:
+                    f.write(message + "\n")
+
+    def log_metrics(self, metrics: Dict[str, float], split: str):
+        if self.rank == 0:
+            now = time.time()
+            dt_str = ""
+            if self._last_log_time is not None:
+                dt = now - self._last_log_time
+                dt_str = f", log interval: {dt:.2f}s"
+            self._last_log_time = now
+
+            formatted = ", ".join(f"{k}: {v:.6f}" for k, v in metrics.items())
+            self.print(f"[{split}] step {self.step}: {formatted}{dt_str}")
+        if self.writer is not None:
+            for key, value in metrics.items():
+                if isinstance(value, (int, float)):
+                    self.writer.add_scalar(f"{split}/{key}", value, self.step)
+
+    def done(self, split: str, message: str):
+        self.print(f"[{split}] {message}")
+
+    # ------------------------------------------------------------------ #
+    # State dict
+    # ------------------------------------------------------------------ #
+    def state_dict(self):
+        return {"step": self.step}
+
+    def load_state_dict(self, state):
+        self.step = int(state.get("step", 0))
+
+    # ------------------------------------------------------------------ #
+    # Context manager compatibility (for parity with minicpm-audio code)
+    # ------------------------------------------------------------------ #
+    @contextlib.contextmanager
+    def live(self):
+        yield
+