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pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "f5-tts"
-version = "0.3.1"
+version = "0.3.2"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}

src/f5_tts/socket_server.py

@@ -1,13 +1,14 @@
+import argparse
+import gc
 import socket
 import struct
 import torch
 import torchaudio
-from threading import Thread
-
-
-import gc
 import traceback
+from importlib.resources import files
+from threading import Thread
 
+from cached_path import cached_path
 
 from infer.utils_infer import infer_batch_process, preprocess_ref_audio_text, load_vocoder, load_model
 from model.backbones.dit import DiT
@@ -15,7 +16,9 @@ from model.backbones.dit import DiT
 
 class TTSStreamingProcessor:
     def __init__(self, ckpt_file, vocab_file, ref_audio, ref_text, device=None, dtype=torch.float32):
-        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.device = device or (
+            "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+        )
 
         # Load the model using the provided checkpoint and vocab files
         self.model = load_model(
@@ -137,23 +140,51 @@ def start_server(host, port, processor):
 
 
 if __name__ == "__main__":
-    try:
-        # Load the model and vocoder using the provided files
-        ckpt_file = ""  # pointing your checkpoint "ckpts/model/model_1096.pt"
-        vocab_file = ""  # Add vocab file path if needed
-        ref_audio = ""  # add ref audio"./tests/ref_audio/reference.wav"
-        ref_text = ""
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--host", default="0.0.0.0")
+    parser.add_argument("--port", default=9998)
+
+    parser.add_argument(
+        "--ckpt_file",
+        default=str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors")),
+        help="Path to the model checkpoint file",
+    )
+    parser.add_argument(
+        "--vocab_file",
+        default="",
+        help="Path to the vocab file if customized",
+    )
+
+    parser.add_argument(
+        "--ref_audio",
+        default=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+        help="Reference audio to provide model with speaker characteristics",
+    )
+    parser.add_argument(
+        "--ref_text",
+        default="",
+        help="Reference audio subtitle, leave empty to auto-transcribe",
+    )
+
+    parser.add_argument("--device", default=None, help="Device to run the model on")
+    parser.add_argument("--dtype", default=torch.float32, help="Data type to use for model inference")
+
+    args = parser.parse_args()
 
+    try:
         # Initialize the processor with the model and vocoder
         processor = TTSStreamingProcessor(
-            ckpt_file=ckpt_file,
-            vocab_file=vocab_file,
-            ref_audio=ref_audio,
-            ref_text=ref_text,
-            dtype=torch.float32,
+            ckpt_file=args.ckpt_file,
+            vocab_file=args.vocab_file,
+            ref_audio=args.ref_audio,
+            ref_text=args.ref_text,
+            device=args.device,
+            dtype=args.dtype,
         )
 
         # Start the server
-        start_server("0.0.0.0", 9998, processor)
+        start_server(args.host, args.port, processor)
+
     except KeyboardInterrupt:
         gc.collect()