Update app.py

app.py

@@ -1,100 +1,102 @@
-import gradio as gr
-from rvc_infer import download_online_model, infer_audio
-import os
-import re
-import random
-from scipy.io.wavfile import write
-from scipy.io.wavfile import read
-import numpy as np
-import yt_dlp
-import subprocess
-
-def download_model(url, dir_name):
-    output_models = download_online_model(url, dir_name)
-    return output_models
-
-
-def download_audio(url):
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'outtmpl': 'ytdl/%(title)s.%(ext)s',
-        'postprocessors': [{
-            'key': 'FFmpegExtractAudio',
-            'preferredcodec': 'wav',
-            'preferredquality': '192',
-        }],
-    }
-
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        info_dict = ydl.extract_info(url, download=True)
-        file_path = ydl.prepare_filename(info_dict).rsplit('.', 1)[0] + '.wav'
-        sample_rate, audio_data = read(file_path)
-        audio_array = np.asarray(audio_data, dtype=np.int16)
-
-        return sample_rate, audio_array
-
-
-CSS = """
-"""
-
-with gr.Blocks(theme="Hev832/Applio", fill_width=True, css=CSS) as demo:
-    with gr.Tab("Inferenece"):
-        gr.Markdown("in progress")
-        model_name = gr.Textbox(label="Model Name #", lines=1, value="")
-        input_audio = gr.Audio(label="Input Audio #", type="filepath")
-        f0_change = gr.Slider(label="f0 change #", minimum=0, maximum=10, step=1, value=0)
-        f0_method = gr.Dropdown(label="f0 method #", choices=["rmvpe+"], value="rmvpe+")
-        min_pitch = gr.Textbox(label="min pitch #", lines=1, value="50")
-        max_pitch = gr.Textbox(label="max pitch #", lines=1, value="1100")
-        crepe_hop_length = gr.Slider(label="crepe_hop_length #", minimum=0, maximum=256, step=1, value=128)
-        index_rate = gr.Slider(label="index_rate #", minimum=0, maximum=1.0, step=0.01, value=0.75)
-        filter_radius = gr.Slider(label="filter_radius #", minimum=0, maximum=10.0, step=0.01, value=3)
-        rms_mix_rate = gr.Slider(label="rms_mix_rate #", minimum=0, maximum=1.0, step=0.01, value=0.25)
-        protect = gr.Slider(label="protect #", minimum=0, maximum=1.0, step=0.01, value=0.33)
-        split_infer = gr.Checkbox(label="split_infer #", value=False)
-        min_silence = gr.Slider(label="min_silence #", minimum=0, maximum=1000, step=1, value=500)
-        silence_threshold = gr.Slider(label="silence_threshold #", minimum=-1000, maximum=1000, step=1, value=-50)
-        seek_step = gr.Slider(label="seek_step #", minimum=0, maximum=100, step=1, value=0)
-        keep_silence = gr.Slider(label="keep_silence #", minimum=-1000, maximum=1000, step=1, value=100)
-        do_formant = gr.Checkbox(label="do_formant #", value=False)
-        quefrency = gr.Slider(label="quefrency #", minimum=0, maximum=100, step=1, value=0)
-        timbre = gr.Slider(label="timbre #", minimum=0, maximum=100, step=1, value=1)
-        f0_autotune = gr.Checkbox(label="f0_autotune #", value=False)
-        audio_format = gr.Dropdown(label="audio_format #", choices=["wav"], value="wav")
-        resample_sr = gr.Slider(label="resample_sr #", minimum=0, maximum=100, step=1, value=0)
-        hubert_model_path = gr.Textbox(label="hubert_model_pathe #", lines=1, value="hubert_base.pt")
-        rmvpe_model_path = gr.Textbox(label="rmvpe_model_path #", lines=1, value="rmvpe.pt")
-        fcpe_model_path = gr.Textbox(label="fcpe_model_path #", lines=1, value="fcpe.pt")
-        submit_inference = gr.Button('Inference #', variant='primary')
-        result_audio = gr.Audio("Output Audio #", type="filepath")
-
-    with gr.Tab("Download Model"):
-        gr.Markdown("## Download Model for infernece")
-        url_input = gr.Textbox(label="Model URL", placeholder="Enter the URL of the model")
-        dir_name_input = gr.Textbox(label="Directory Name", placeholder="Enter the directory name")
-        output = gr.Textbox(label="Output Models")
-        download_button = gr.Button("Download Model")
-        download_button.click(download_model, inputs=[url_input, dir_name_input], outputs=output)
-
-        with gr.Tab(" Credits"):
-            gr.Markdown(
-                """
-                this project made by [Blane187](https://huggingface.co/Blane187) with Improvements by [John6666](https://huggingfce.co/John6666)
-                """)
-
-
-    gr.on(
-        triggers=[submit_inference.click],
-        fn=infer_audio,
-        inputs=[model_name, input_audio, 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, do_formant, quefrency, timbre, f0_autotune, audio_format, resample_sr,
-                hubert_model_path, rmvpe_model_path, fcpe_model_path],
-        outputs=[result_audio],
-        queue=True,
-        show_api=True,
-        show_progress="full",
-    )
-
-demo.queue()
-demo.launch(debug=True,show_api=False)
+import gradio as gr
+from rvc_infer import download_online_model, infer_audio
+import os
+import re
+import random
+from scipy.io.wavfile import write
+from scipy.io.wavfile import read
+import numpy as np
+import yt_dlp
+import subprocess
+
+def download_model(url, dir_name):
+    output_models = download_online_model(url, dir_name)
+    return output_models
+
+
+def download_audio(url):
+    ydl_opts = {
+        'format': 'bestaudio/best',
+        'outtmpl': 'ytdl/%(title)s.%(ext)s',
+        'postprocessors': [{
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'wav',
+            'preferredquality': '192',
+        }],
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info_dict = ydl.extract_info(url, download=True)
+        file_path = ydl.prepare_filename(info_dict).rsplit('.', 1)[0] + '.wav'
+        sample_rate, audio_data = read(file_path)
+        audio_array = np.asarray(audio_data, dtype=np.int16)
+
+        return sample_rate, audio_array
+
+
+CSS = """
+"""
+
+with gr.Blocks(theme="Hev832/Applio", fill_width=True, css=CSS) as demo:
+    with gr.Tab("Inferenece"):
+        gr.Markdown("in progress")
+        model_name = gr.Textbox(label="Model Name #", lines=1, value="")
+        input_audio = gr.Audio(label="Input Audio #", type="filepath")
+        with gr.Accordion("Settings", open=False):
+            f0_change = gr.Slider(label="f0 change #", minimum=0, maximum=10, step=1, value=0)
+            f0_method = gr.Dropdown(label="f0 method #", choices=["rmvpe+"], value="rmvpe+")
+            min_pitch = gr.Textbox(label="min pitch #", lines=1, value="50")
+            max_pitch = gr.Textbox(label="max pitch #", lines=1, value="1100")
+            crepe_hop_length = gr.Slider(label="crepe_hop_length #", minimum=0, maximum=256, step=1, value=128)
+            index_rate = gr.Slider(label="index_rate #", minimum=0, maximum=1.0, step=0.01, value=0.75)
+            filter_radius = gr.Slider(label="filter_radius #", minimum=0, maximum=10.0, step=0.01, value=3)
+            rms_mix_rate = gr.Slider(label="rms_mix_rate #", minimum=0, maximum=1.0, step=0.01, value=0.25)
+            protect = gr.Slider(label="protect #", minimum=0, maximum=1.0, step=0.01, value=0.33)
+        with gr.Accordion("Advanced Settings", open=False):
+            split_infer = gr.Checkbox(label="split_infer #", value=False)
+            min_silence = gr.Slider(label="min_silence #", minimum=0, maximum=1000, step=1, value=500)
+            silence_threshold = gr.Slider(label="silence_threshold #", minimum=-1000, maximum=1000, step=1, value=-50)
+            seek_step = gr.Slider(label="seek_step #", minimum=0, maximum=100, step=1, value=0)
+            keep_silence = gr.Slider(label="keep_silence #", minimum=-1000, maximum=1000, step=1, value=100)
+            do_formant = gr.Checkbox(label="do_formant #", value=False)
+            quefrency = gr.Slider(label="quefrency #", minimum=0, maximum=100, step=1, value=0)
+            timbre = gr.Slider(label="timbre #", minimum=0, maximum=100, step=1, value=1)
+            f0_autotune = gr.Checkbox(label="f0_autotune #", value=False)
+            audio_format = gr.Dropdown(label="audio_format #", choices=["wav"], value="wav")
+            resample_sr = gr.Slider(label="resample_sr #", minimum=0, maximum=100, step=1, value=0)
+            hubert_model_path = gr.Textbox(label="hubert_model_pathe #", lines=1, value="hubert_base.pt")
+            rmvpe_model_path = gr.Textbox(label="rmvpe_model_path #", lines=1, value="rmvpe.pt")
+            fcpe_model_path = gr.Textbox(label="fcpe_model_path #", lines=1, value="fcpe.pt")
+            submit_inference = gr.Button('Inference #', variant='primary')
+        result_audio = gr.Audio("Output Audio #", type="filepath")
+
+    with gr.Tab("Download Model"):
+        gr.Markdown("## Download Model for infernece")
+        url_input = gr.Textbox(label="Model URL", placeholder="Enter the URL of the model")
+        dir_name_input = gr.Textbox(label="Directory Name", placeholder="Enter the directory name")
+        output = gr.Textbox(label="Output Models")
+        download_button = gr.Button("Download Model")
+        download_button.click(download_model, inputs=[url_input, dir_name_input], outputs=output)
+
+        with gr.Tab(" Credits"):
+            gr.Markdown(
+                """
+                this project made by [Blane187](https://huggingface.co/Blane187) with Improvements by [John6666](https://huggingfce.co/John6666)
+                """)
+
+
+    gr.on(
+        triggers=[submit_inference.click],
+        fn=infer_audio,
+        inputs=[model_name, input_audio, 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, do_formant, quefrency, timbre, f0_autotune, audio_format, resample_sr,
+                hubert_model_path, rmvpe_model_path, fcpe_model_path],
+        outputs=[result_audio],
+        queue=True,
+        show_api=True,
+        show_progress="full",
+    )
+
+demo.queue()
+demo.launch(debug=True,show_api=False)