Create app.py

app.py

@@ -0,0 +1,59 @@
+import torch
+
+from transformers import pipeline
+
+import numpy as np
+import gradio as gr
+
+set_seed(0)
+
+def _grab_best_device(use_gpu=True):
+    if torch.cuda.device_count() > 0 and use_gpu:
+        device = "cuda"
+    else:
+        device = "cpu"
+    return device
+
+device = _grab_best_device()
+
+HUB_PATH = "ylacombe/vits_vctk_welsh_male"
+pipe = pipeline(HUB_PATH, device=0)
+
+title = "# 🐶 VITS"
+
+description = """
+
+"""
+
+num_speakers = pipe.model.config.num_speakers
+
+# Inference
+def generate_audio(text, spkr_id):
+
+    forward_params = {"spkr_id": spkr_id}
+    output = pipeline(text, forward_params=forward_params)
+    
+    return (output["sampling_rate"], output["audio"].squeeze())
+
+
+# Gradio blocks demo    
+with gr.Blocks() as demo_blocks:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    with gr.Row():
+        with gr.Column():
+            inp_text = gr.Textbox(label="Input Text", info="What would you like bark to synthesise?")
+            spkr = gr.Dropdown(
+                    [i for i in range(num_speakers)],
+                    value=None, 
+                    label="Speaker ID", 
+                    info="Default: Unconditional Generation"
+                    )
+            btn = gr.Button("Generate Audio!")
+    
+        with gr.Column():
+            out_audio_vocos = gr.Audio(type="numpy", autoplay=False, label="Generated Audio", show_label=True)
+    
+    btn.click(generate_audio, [inp_text, spk], [out_audio_vocos])
+
+demo_blocks.queue().launch(debug=True)