Add option to convert weights from the pytorch format.

f5_tts_mlx/cfm.py

@@ -9,6 +9,7 @@
 
 from __future__ import annotations
 from datetime import datetime
+import os
 from pathlib import Path
 from random import random
 from typing import Callable, Literal
@@ -377,7 +378,7 @@ def fn(t, x):
         return out, trajectory
 
     @classmethod
-    def from_pretrained(cls, hf_model_name_or_path: str) -> F5TTS:
+    def from_pretrained(cls, hf_model_name_or_path: str, convert_weights = False) -> F5TTS:
         path = fetch_from_hub(hf_model_name_or_path)
 
         if path is None:
@@ -435,6 +436,40 @@ def from_pretrained(cls, hf_model_name_or_path: str) -> F5TTS:
         )
 
         weights = mx.load(model_path.as_posix(), format="safetensors")
+
+        if convert_weights:
+            new_weights = {}
+            for k, v in weights.items():
+                k = k.replace('ema_model.', '')
+
+                # rename layers
+                if len(k) < 1 or 'mel_spec.' in k or k in ('initted', 'step'):
+                    continue
+                elif '.to_out' in k:
+                    k = k.replace('.to_out', '.to_out.layers')
+                elif '.text_blocks' in k:
+                    k = k.replace('.text_blocks', '.text_blocks.layers')
+                elif '.ff.ff.0.0' in k:
+                    k = k.replace('.ff.ff.0.0', '.ff.ff.layers.0.layers.0')
+                elif '.ff.ff.2' in k:
+                    k = k.replace('.ff.ff.2', '.ff.ff.layers.2')
+                elif '.time_mlp' in k:
+                    k = k.replace('.time_mlp', '.time_mlp.layers')
+                elif '.conv1d' in k:
+                    k = k.replace('.conv1d', '.conv1d.layers')
+
+                # reshape weights
+                if '.dwconv.weight' in k:
+                    v = v.swapaxes(1, 2)
+                elif '.conv1d.layers.0.weight' in k:
+                    v = v.swapaxes(1, 2)
+                elif '.conv1d.layers.2.weight' in k:
+                    v = v.swapaxes(1, 2)
+
+                new_weights[k] = v
+
+            weights = new_weights
+
         f5tts.load_weights(list(weights.items()))
         mx.eval(f5tts.parameters())