Create app.py

app.py

@@ -0,0 +1,90 @@
+from pathlib import Path
+import torch
+from so_vits_svc_fork.hparams import HParams
+import json
+import gradio as gr
+import librosa
+import numpy as np
+
+##########################################################
+# REPLACE THESE VALUES TO CHANGE THE MODEL REPO/CKPT NAME
+##########################################################
+repo_id = "dog/theovon"
+ckpt_name = None # or specify a ckpt. ex. "G_1257.pth"
+##########################################################
+
+# Figure out the latest generator by taking highest value one.
+# Ex. if the repo has: G_0.pth, G_100.pth, G_200.pth, we'd use G_200.pth
+if ckpt_name is None:
+    latest_id = sorted(
+        [
+            int(Path(x).stem.split("_")[1])
+            for x in list_repo_files(repo_id)
+            if x.startswith("G_") and x.endswith(".pth")
+        ]
+    )[-1]
+    ckpt_name = f"G_{latest_id}.pth"
+
+generator_path = hf_hub_download(repo_id, ckpt_name)
+config_path = hf_hub_download(repo_id, "config.json")
+hparams = HParams(**json.loads(Path(config_path).read_text()))
+speakers = list(hparams.spk.keys())
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = Svc(net_g_path=generator_path, config_path=config_path, device=device, cluster_model_path=None)
+
+
+def predict(
+    speaker,
+    audio,
+    transpose: int = 0,
+    auto_predict_f0: bool = False,
+    cluster_infer_ratio: float = 0,
+    noise_scale: float = 0.4,
+    f0_method: str = "crepe",
+    db_thresh: int = -40,
+    pad_seconds: float = 0.5,
+    chunk_seconds: float = 0.5,
+    absolute_thresh: bool = False,
+):
+    audio, _ = librosa.load(audio, sr=model.target_sample)
+    audio = model.infer_silence(
+        audio.astype(np.float32),
+        speaker=speaker,
+        transpose=transpose,
+        auto_predict_f0=auto_predict_f0,
+        cluster_infer_ratio=cluster_infer_ratio,
+        noise_scale=noise_scale,
+        f0_method=f0_method,
+        db_thresh=db_thresh,
+        pad_seconds=pad_seconds,
+        chunk_seconds=chunk_seconds,
+        absolute_thresh=absolute_thresh,
+    )
+    return model.target_sample, audio
+
+
+interface = gr.Interface(
+    predict,
+    inputs=[
+        gr.Dropdown(speakers, value=speakers[0], label="Target Speaker"),
+        gr.Audio(type="filepath", source="microphone", label="Source Audio"),
+        gr.Slider(-12, 12, value=0, step=1, label="Transpose (Semitones)"),
+        gr.Checkbox(False, label="Auto Predict F0"),
+        gr.Slider(0.0, 1.0, value=0.0, step=0.1, label='cluster infer ratio'),
+        gr.Slider(0.0, 1.0, value=0.4, step=0.1, label="noise scale"),
+        gr.Dropdown(choices=["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"], value='crepe', label="f0 method"),
+    ],
+    outputs="audio",
+    title="Voice Cloning",
+    description=f"""
+This app uses models trained with so-vits-svc-fork to clone your voice. Model currently being used is https://hf.co/{repo_id}. To change the model being served, duplicate the space and update the repo_id in `app.py`.
+""".strip(),
+    article="""
+<p style='text-align: center'>
+    <a href='https://github.com/voicepaw/so-vits-svc-fork' target='_blank'>Github Repo</a>
+</p>
+"""
+)
+
+if __name__ == '__main__':
+    interface.launch()