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/runtime/triton_trtllm/client_grpc.py

@@ -220,8 +220,8 @@ def get_args():
     return parser.parse_args()
 
 
-def load_audio(wav_path, target_sample_rate=16000):
-    assert target_sample_rate == 16000, "hard coding in server"
+def load_audio(wav_path, target_sample_rate=24000):
+    assert target_sample_rate == 24000, "hard coding in server"
     if isinstance(wav_path, dict):
         waveform = wav_path["array"]
         sample_rate = wav_path["sampling_rate"]
@@ -244,7 +244,7 @@ async def send(
     model_name: str,
     padding_duration: int = None,
     audio_save_dir: str = "./",
-    save_sample_rate: int = 16000,
+    save_sample_rate: int = 24000,
 ):
     total_duration = 0.0
     latency_data = []
@@ -254,7 +254,7 @@ async def send(
     for i, item in enumerate(manifest_item_list):
         if i % log_interval == 0:
             print(f"{name}: {i}/{len(manifest_item_list)}")
-        waveform, sample_rate = load_audio(item["audio_filepath"], target_sample_rate=16000)
+        waveform, sample_rate = load_audio(item["audio_filepath"], target_sample_rate=24000)
         duration = len(waveform) / sample_rate
         lengths = np.array([[len(waveform)]], dtype=np.int32)
 
@@ -417,7 +417,7 @@ async def main():
                 model_name=args.model_name,
                 audio_save_dir=args.log_dir,
                 padding_duration=1,
-                save_sample_rate=24000 if args.model_name == "f5_tts" else 16000,
+                save_sample_rate=24000,
             )
         )
         tasks.append(task)

src/f5_tts/runtime/triton_trtllm/client_http.py

@@ -82,7 +82,7 @@ def prepare_request(
     samples,
     reference_text,
     target_text,
-    sample_rate=16000,
+    sample_rate=24000,
     audio_save_dir: str = "./",
 ):
     assert len(samples.shape) == 1, "samples should be 1D"
@@ -106,8 +106,8 @@ def prepare_request(
     return data
 
 
-def load_audio(wav_path, target_sample_rate=16000):
-    assert target_sample_rate == 16000, "hard coding in server"
+def load_audio(wav_path, target_sample_rate=24000):
+    assert target_sample_rate == 24000, "hard coding in server"
     if isinstance(wav_path, dict):
         samples = wav_path["array"]
         sample_rate = wav_path["sampling_rate"]
@@ -129,7 +129,7 @@ if __name__ == "__main__":
 
     url = f"{server_url}/v2/models/{args.model_name}/infer"
     samples, sr = load_audio(args.reference_audio)
-    assert sr == 16000, "sample rate hardcoded in server"
+    assert sr == 24000, "sample rate hardcoded in server"
 
     samples = np.array(samples, dtype=np.float32)
     data = prepare_request(samples, args.reference_text, args.target_text)

src/f5_tts/runtime/triton_trtllm/model_repo_f5_tts/f5_tts/config.pbtxt

@@ -33,7 +33,7 @@ parameters [
   },
   {
    key: "reference_audio_sample_rate", 
-   value: {string_value:"16000"}
+   value: {string_value:"24000"}
   },
   {
    key: "vocoder",