Update inference-cli.py add load vocos from local path

inference-cli.py

@@ -1,26 +1,24 @@
+import argparse
+import codecs
 import re
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tomli
 import torch
 import torchaudio
-import numpy as np
-import tempfile
+import tqdm
+from cached_path import cached_path
 from einops import rearrange
-from vocos import Vocos
 from pydub import AudioSegment, silence
-from model import CFM, UNetT, DiT, MMDiT
-from cached_path import cached_path
-from model.utils import (
-    load_checkpoint,
-    get_tokenizer,
-    convert_char_to_pinyin,
-    save_spectrogram,
-)
 from transformers import pipeline
-import soundfile as sf
-import tomli
-import argparse
-import tqdm
-from pathlib import Path
-import codecs
+from vocos import Vocos
+
+from model import CFM, DiT, MMDiT, UNetT
+from model.utils import (convert_char_to_pinyin, get_tokenizer,
+                         load_checkpoint, save_spectrogram)
 
 parser = argparse.ArgumentParser(
     prog="python3 inference-cli.py",
@@ -73,6 +71,11 @@ parser.add_argument(
     "--remove_silence",
     help="Remove silence.",
 )
+parser.add_argument(
+    "--load_vocoder_from_local",
+    action="store_true",
+    help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
+)
 args = parser.parse_args()
 
 config = tomli.load(open(args.config, "rb"))
@@ -88,6 +91,7 @@ model = args.model if args.model else config["model"]
 remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
 wave_path = Path(output_dir)/"out.wav"
 spectrogram_path = Path(output_dir)/"out.png"
+vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
 
 SPLIT_WORDS = [
     "but", "however", "nevertheless", "yet", "still",
@@ -105,7 +109,16 @@ device = (
     if torch.cuda.is_available()
     else "mps" if torch.backends.mps.is_available() else "cpu"
 )
-vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+if args.load_vocoder_from_local:
+    print(f"Load vocos from local path {vocos_local_path}")
+    vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
+    state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", map_location=device)
+    vocos.load_state_dict(state_dict)
+    vocos.eval()
+else:
+    print("Donwload Vocos from huggingface charactr/vocos-mel-24khz")
+    vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
 
 print(f"Using {device} device")
 
@@ -124,8 +137,9 @@ speed = 1.0
 fix_duration = None
 
 def load_model(repo_name, exp_name, model_cls, model_cfg, ckpt_step):
-    ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
-    # ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors
+    ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors
+    if not Path(ckpt_path).exists():
+        ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
     vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin")
     model = CFM(
         transformer=model_cls(
@@ -385,4 +399,4 @@ def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, custom_spli
     return infer_batch((audio, sr), ref_text, gen_text_batches, model, remove_silence)
     
 
-infer(ref_audio, ref_text, gen_text, model, remove_silence, ",".join(SPLIT_WORDS))
+infer(ref_audio, ref_text, gen_text, model, remove_silence, ",".join(SPLIT_WORDS))