Create app.py

app.py

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+import os
+import torch
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import langid
+from openvoice.api import BaseSpeakerTTS, ToneColorConverter
+import openvoice.se_extractor as se_extractor
+
+# Constants
+CKPT_BASE_PATH = "checkpoints"
+EN_SUFFIX = f"{CKPT_BASE_PATH}/base_speakers/EN"
+CONVERTER_SUFFIX = f"{CKPT_BASE_PATH}/converter"
+OUTPUT_DIR = "outputs/"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# Download necessary files
+def download_from_hf_hub(filename, local_dir="./"):
+    os.makedirs(local_dir, exist_ok=True)
+    hf_hub_download(repo_id="myshell-ai/OpenVoice", filename=filename, local_dir=local_dir)
+
+for file in [f"{CONVERTER_SUFFIX}/checkpoint.pth", f"{CONVERTER_SUFFIX}/config.json",
+             f"{EN_SUFFIX}/checkpoint.pth", f"{EN_SUFFIX}/config.json",
+             f"{EN_SUFFIX}/en_default_se.pth", f"{EN_SUFFIX}/en_style_se.pth"]:
+    download_from_hf_hub(file)
+
+# Initialize models
+pt_device = "cpu"
+en_base_speaker_tts = BaseSpeakerTTS(f"{EN_SUFFIX}/config.json", device=pt_device)
+en_base_speaker_tts.load_ckpt(f"{EN_SUFFIX}/checkpoint.pth")
+
+tone_color_converter = ToneColorConverter(f"{CONVERTER_SUFFIX}/config.json", device=pt_device)
+tone_color_converter.load_ckpt(f"{CONVERTER_SUFFIX}/checkpoint.pth")
+
+en_source_default_se = torch.load(f"{EN_SUFFIX}/en_default_se.pth")
+en_source_style_se = torch.load(f"{EN_SUFFIX}/en_style_se.pth")
+
+# Main prediction function
+def predict(prompt, style, audio_file_pth, tau):
+    if len(prompt) < 2 or len(prompt) > 200:
+        return "Text should be between 2 and 200 characters.", None
+
+    try:
+        target_se, _ = se_extractor.get_se(audio_file_pth, tone_color_converter, target_dir=OUTPUT_DIR, vad=True)
+    except Exception as e:
+        return f"Error getting target tone color: {str(e)}", None
+
+    src_path = f"{OUTPUT_DIR}/tmp.wav"
+    en_base_speaker_tts.tts(prompt, src_path, speaker=style, language="English")
+
+    save_path = f"{OUTPUT_DIR}/output.wav"
+    tone_color_converter.convert(
+        audio_src_path=src_path,
+        src_se=en_source_style_se if style != "default" else en_source_default_se,
+        tgt_se=target_se,
+        output_path=save_path,
+        tau=tau
+    )
+
+    return "Voice cloning completed successfully.", save_path
+
+# Gradio interface
+def create_demo():
+    with gr.Blocks() as demo:
+        gr.Markdown("# OpenVoice: Instant Voice Cloning with fine-tuning")
+        
+        with gr.Row():
+            input_text = gr.Textbox(label="Text to speak", placeholder="Enter text here (2-200 characters)")
+            style = gr.Dropdown(
+                label="Style",
+                choices=["default", "whispering", "cheerful", "terrified", "angry", "sad", "friendly"],
+                value="default"
+            )
+        
+        with gr.Row():
+            reference_audio = gr.Audio(label="Reference Audio", type="filepath")
+            tau_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Tau (Voice similarity)", info="Higher values make the output more similar to the reference voice")
+
+        submit_button = gr.Button("Generate Voice")
+        
+        output_text = gr.Textbox(label="Status")
+        output_audio = gr.Audio(label="Generated Audio")
+
+        submit_button.click(
+            predict,
+            inputs=[input_text, style, reference_audio, tau_slider],
+            outputs=[output_text, output_audio]
+        )
+
+    return demo
+
+# Launch the demo
+if __name__ == "__main__":
+    demo = create_demo()
+    demo.launch()