Sync from GitHub repo

This Space is synced from the GitHub repo: https://github.com/SWivid/F5-TTS. Please submit contributions to the Space there

src/f5_tts/api.py

@@ -119,7 +119,7 @@ class F5TTS:
         seed_everything(seed)
         self.seed = seed
 
-        ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text, device=self.device)
+        ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text)
 
         wav, sr, spec = infer_process(
             ref_file,

src/f5_tts/infer/infer_cli.py

@@ -162,6 +162,11 @@ parser.add_argument(
     type=float,
     help=f"Fix the total duration (ref and gen audios) in seconds, default {fix_duration}",
 )
+parser.add_argument(
+    "--device",
+    type=str,
+    help="Specify the device to run on",
+)
 args = parser.parse_args()
 
 
@@ -202,6 +207,7 @@ cfg_strength = args.cfg_strength or config.get("cfg_strength", cfg_strength)
 sway_sampling_coef = args.sway_sampling_coef or config.get("sway_sampling_coef", sway_sampling_coef)
 speed = args.speed or config.get("speed", speed)
 fix_duration = args.fix_duration or config.get("fix_duration", fix_duration)
+device = args.device
 
 
 # patches for pip pkg user
@@ -239,7 +245,9 @@ if vocoder_name == "vocos":
 elif vocoder_name == "bigvgan":
     vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
 
-vocoder = load_vocoder(vocoder_name=vocoder_name, is_local=load_vocoder_from_local, local_path=vocoder_local_path)
+vocoder = load_vocoder(
+    vocoder_name=vocoder_name, is_local=load_vocoder_from_local, local_path=vocoder_local_path, device=device
+)
 
 
 # load TTS model
@@ -270,7 +278,9 @@ if not ckpt_file:
     ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}"))
 
 print(f"Using {model}...")
-ema_model = load_model(model_cls, model_arc, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file)
+ema_model = load_model(
+    model_cls, model_arc, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file, device=device
+)
 
 
 # inference process
@@ -326,6 +336,7 @@ def main():
             sway_sampling_coef=sway_sampling_coef,
             speed=speed,
             fix_duration=fix_duration,
+            device=device,
         )
         generated_audio_segments.append(audio_segment)
 

src/f5_tts/infer/utils_infer.py

@@ -149,7 +149,7 @@ def initialize_asr_pipeline(device: str = device, dtype=None):
         dtype = (
             torch.float16
             if "cuda" in device
-            and torch.cuda.get_device_properties(device).major >= 6
+            and torch.cuda.get_device_properties(device).major >= 7
             and not torch.cuda.get_device_name().endswith("[ZLUDA]")
             else torch.float32
         )
@@ -186,7 +186,7 @@ def load_checkpoint(model, ckpt_path, device: str, dtype=None, use_ema=True):
         dtype = (
             torch.float16
             if "cuda" in device
-            and torch.cuda.get_device_properties(device).major >= 6
+            and torch.cuda.get_device_properties(device).major >= 7
             and not torch.cuda.get_device_name().endswith("[ZLUDA]")
             else torch.float32
         )
@@ -289,7 +289,7 @@ def remove_silence_edges(audio, silence_threshold=-42):
 # preprocess reference audio and text
 
 
-def preprocess_ref_audio_text(ref_audio_orig, ref_text, clip_short=True, show_info=print, device=device):
+def preprocess_ref_audio_text(ref_audio_orig, ref_text, clip_short=True, show_info=print):
     show_info("Converting audio...")
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
         aseg = AudioSegment.from_file(ref_audio_orig)