Create app.py

app.py

@@ -0,0 +1,106 @@
+import os
+import gradio as gr
+from lib.infer import infer_audio
+from google.colab import files
+from pydub import AudioSegment
+import zipfile
+import shutil
+import urllib.request
+import gdown
+
+main_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+os.chdir(main_dir)
+
+def upload_audio(model_name, sound_path, f0_change, f0_method, min_pitch, max_pitch,
+                 crepe_hop_length, index_rate, filter_radius, rms_mix_rate,
+                 protect, split_infer, min_silence, silence_threshold, 
+                 seek_step, keep_silence, formant_shift, quefrency, timbre, 
+                 f0_autotune, output_format):
+    
+    if not sound_path:
+        uploaded_audio = files.upload()
+        assert len(uploaded_audio) == 1, "Please only input audio one at a time"
+        sound_path = os.path.join(os.getcwd(), list(uploaded_audio.keys())[0])
+    
+    inferred_audio = infer_audio(
+        model_name,
+        sound_path,
+        f0_change,
+        f0_method,
+        min_pitch,
+        max_pitch,
+        crepe_hop_length,
+        index_rate,
+        filter_radius,
+        rms_mix_rate,
+        protect,
+        split_infer,
+        min_silence,
+        silence_threshold,
+        seek_step,
+        keep_silence,
+        formant_shift,
+        quefrency,
+        timbre,
+        f0_autotune,
+        output_format
+    )
+    
+    return AudioSegment.from_file(inferred_audio)
+
+def download_model(url, dir_name):
+    models_dir = "models"
+    extraction_folder = os.path.join(models_dir, dir_name)
+
+    if os.path.exists(extraction_folder):
+        return f'Voice model directory {dir_name} already exists! Choose a different name.'
+
+    if 'pixeldrain.com' in url:
+        zip_name = url.split('/')[-1]
+        url = f'https://pixeldrain.com/api/file/{zip_name}'
+    elif 'drive.google.com' in url:
+        zip_name = dir_name + ".zip"
+        gdown.download(url, output=zip_name, use_cookies=True, quiet=True, fuzzy=True)
+    else:
+        zip_name = url.split('/')[-1]
+        urllib.request.urlretrieve(url, zip_name)
+
+    with zipfile.ZipFile(zip_name, 'r') as zip_ref:
+        zip_ref.extractall(extraction_folder)
+    
+    os.remove(zip_name)
+    
+    return f'{dir_name} model successfully downloaded!'
+
+with gr.Blocks() as app:
+    gr.Markdown("## Inference")
+    
+    with gr.Row():
+        model_name = gr.Textbox(label="Model Name")
+        sound_path = gr.Textbox(label="Audio Path")
+        
+    with gr.Row():
+        f0_change = gr.Slider(minimum=-12, maximum=12, label="F0 Change (semitones)", value=0)
+        f0_method = gr.Dropdown(choices=["crepe", "harvest", "mangio-crepe", "rmvpe", "rmvpe+", "fcpe", "fcpe_legacy", "hybrid[mangio-crepe+rmvpe]", "hybrid[mangio-crepe+fcpe]", "hybrid[rmvpe+fcpe]", "hybrid[mangio-crepe+rmvpe+fcpe]"], label="F0 Method", value="fcpe")
+
+    # Add more settings as required
+    # Example for output format
+    output_format = gr.Dropdown(choices=["wav", "flac", "mp3"], label="Output Format", value="wav")
+    
+    submit_button = gr.Button("Infer")
+    
+    output_audio = gr.Audio(label="Inferred Audio Output")
+
+    submit_button.click(upload_audio, inputs=[model_name, sound_path, f0_change, f0_method, output_format], outputs=output_audio)
+
+    gr.Markdown("## Download Models")
+    
+    url = gr.Textbox(label="Model Download URL")
+    dir_name = gr.Textbox(label="Desired Model Name")
+    download_button = gr.Button("Download Model")
+    download_output = gr.Markdown("")
+    
+    download_button.click(download_model, inputs=[url, dir_name], outputs=download_output)
+
+app.launch()