Added intel XPU support

README.md

@@ -32,6 +32,10 @@ pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https:/
 
 # AMD GPU: install pytorch with your ROCm version, e.g.
 pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
+
+# intel GPU: install pytorch with your XPU version, e.g.
+# Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit must be installed
+pip install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu
 ```
 
 Then you can choose from a few options below:

src/f5_tts/api.py

@@ -47,7 +47,15 @@ class F5TTS:
         else:
             import torch
 
-            self.device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+            self.device = (
+                "cuda"
+                if torch.cuda.is_available()
+                else "xpu"
+                if torch.xpu.is_available()
+                else "mps"
+                if torch.backends.mps.is_available()
+                else "cpu"
+            )
 
         # Load models
         self.load_vocoder_model(vocoder_name, local_path=local_path, hf_cache_dir=hf_cache_dir)

src/f5_tts/eval/eval_utmos.py

@@ -13,7 +13,7 @@ def main():
     parser.add_argument("--ext", type=str, default="wav", help="Audio extension.")
     args = parser.parse_args()
 
-    device = "cuda" if torch.cuda.is_available() else "cpu"
+    device = "cuda" if torch.cuda.is_available() else "xpu" if torch.xpu.is_available() else "cpu"
 
     predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
     predictor = predictor.to(device)

src/f5_tts/infer/speech_edit.py

@@ -10,7 +10,15 @@ from f5_tts.infer.utils_infer import load_checkpoint, load_vocoder, save_spectro
 from f5_tts.model import CFM, DiT, UNetT
 from f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
 
-device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
 
 
 # --------------------- Dataset Settings -------------------- #

src/f5_tts/infer/utils_infer.py

@@ -33,7 +33,15 @@ from f5_tts.model.utils import (
 
 _ref_audio_cache = {}
 
-device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
 
 # -----------------------------------------
 

src/f5_tts/socket_server.py

@@ -17,7 +17,13 @@ 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 "mps" if torch.backends.mps.is_available() else "cpu"
+            "cuda"
+            if torch.cuda.is_available()
+            else "xpu"
+            if torch.xpu.is_available()
+            else "mps"
+            if torch.backends.mps.is_available()
+            else "cpu"
         )
 
         # Load the model using the provided checkpoint and vocab files

src/f5_tts/train/finetune_gradio.py

@@ -46,7 +46,15 @@ path_data = str(files("f5_tts").joinpath("../../data"))
 path_project_ckpts = str(files("f5_tts").joinpath("../../ckpts"))
 file_train = str(files("f5_tts").joinpath("train/finetune_cli.py"))
 
-device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
 
 
 # Save settings from a JSON file
@@ -889,6 +897,13 @@ def calculate_train(
             gpu_properties = torch.cuda.get_device_properties(i)
             total_memory += gpu_properties.total_memory / (1024**3)  # in GB
 
+    elif torch.xpu.is_available():
+        gpu_count = torch.xpu.device_count()
+        total_memory = 0
+        for i in range(gpu_count):
+            gpu_properties = torch.xpu.get_device_properties(i)
+            total_memory += gpu_properties.total_memory / (1024**3)
+
     elif torch.backends.mps.is_available():
         gpu_count = 1
         total_memory = psutil.virtual_memory().available / (1024**3)
@@ -1284,7 +1299,21 @@ def get_gpu_stats():
                 f"Allocated GPU memory (GPU {i}): {allocated_memory:.2f} MB\n"
                 f"Reserved GPU memory (GPU {i}): {reserved_memory:.2f} MB\n\n"
             )
+    elif torch.xpu.is_available():
+        gpu_count = torch.xpu.device_count()
+        for i in range(gpu_count):
+            gpu_name = torch.xpu.get_device_name(i)
+            gpu_properties = torch.xpu.get_device_properties(i)
+            total_memory = gpu_properties.total_memory / (1024**3)  # in GB
+            allocated_memory = torch.xpu.memory_allocated(i) / (1024**2)  # in MB
+            reserved_memory = torch.xpu.memory_reserved(i) / (1024**2)  # in MB
 
+            gpu_stats += (
+                f"GPU {i} Name: {gpu_name}\n"
+                f"Total GPU memory (GPU {i}): {total_memory:.2f} GB\n"
+                f"Allocated GPU memory (GPU {i}): {allocated_memory:.2f} MB\n"
+                f"Reserved GPU memory (GPU {i}): {reserved_memory:.2f} MB\n\n"
+            )
     elif torch.backends.mps.is_available():
         gpu_count = 1
         gpu_stats += "MPS GPU\n"