Create app.py

app.py

@@ -0,0 +1,187 @@
+import os
+import json
+import argparse
+import traceback
+import logging
+import gradio as gr
+import numpy as np
+import librosa
+import torch
+import asyncio
+import edge_tts
+from datetime import datetime
+from fairseq import checkpoint_utils
+from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
+from vc_infer_pipeline import VC
+from config import (
+    is_half,
+    device
+)
+logging.getLogger("numba").setLevel(logging.WARNING)
+limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
+
+def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
+    def vc_fn(
+        input_audio,
+        f0_up_key,
+        f0_method,
+        index_rate,
+        tts_mode,
+        tts_text,
+        tts_voice
+    ):
+        try:
+            if tts_mode:
+                if len(tts_text) > 100 and limitation:
+                    return "Text is too long", None
+                if tts_text is None or tts_voice is None:
+                    return "You need to enter text and select a voice", None
+                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
+                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
+            else:
+                if args.files:
+                    audio, sr = librosa.load(input_audio, sr=16000, mono=True)
+                else:
+                    if input_audio is None:
+                        return "You need to upload an audio", None
+                    sampling_rate, audio = input_audio
+                    duration = audio.shape[0] / sampling_rate
+                    if duration > 20 and limitation:
+                        return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
+                    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+                    if len(audio.shape) > 1:
+                        audio = librosa.to_mono(audio.transpose(1, 0))
+                    if sampling_rate != 16000:
+                        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+            times = [0, 0, 0]
+            f0_up_key = int(f0_up_key)
+            audio_opt = vc.pipeline(
+                hubert_model,
+                net_g,
+                0,
+                audio,
+                times,
+                f0_up_key,
+                f0_method,
+                file_index,
+                file_big_npy,
+                index_rate,
+                if_f0,
+            )
+            print(
+                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
+            )
+            return "Success", (tgt_sr, audio_opt)
+        except:
+            info = traceback.format_exc()
+            print(info)
+            return info, (None, None)
+    return vc_fn
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(device)
+    if is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+def change_to_tts_mode(tts_mode):
+    if tts_mode:
+        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
+    else:
+        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--api', action="store_true", default=False)
+    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
+    parser.add_argument("--files", action="store_true", default=False, help="load audio from path")
+    args, unknown = parser.parse_known_args()
+    load_hubert()
+    models = []
+    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
+    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
+    with open("weights/model_info.json", "r", encoding="utf-8") as f:
+        models_info = json.load(f)
+    for name, info in models_info.items():
+        if not info['enable']:
+            continue
+        title = info['title']
+        author = info.get("author", None)
+        cover = f"weights/{name}/{info['cover']}"
+        index = f"weights/{name}/{info['feature_retrieval_library']}"
+        npy = f"weights/{name}/{info['feature_file']}"
+        cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
+        tgt_sr = cpt["config"][-1]
+        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+        if_f0 = cpt.get("f0", 1)
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+        del net_g.enc_q
+        print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
+        net_g.eval().to(device)
+        if is_half:
+            net_g = net_g.half()
+        else:
+            net_g = net_g.float()
+        vc = VC(tgt_sr, device, is_half)
+        models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
+    with gr.Blocks() as app:
+        gr.Markdown(
+            "# <center> RVC Models\n"
+            "## <center> The input audio should be clean and pure voice without background music.\n"
+            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=wafer22.Rvc-Models)\n\n"
+            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1m1g55pKTnsQS6JMcosZMlz6njPqqq5c0?usp=share_link)\n\n"
+            "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/wafer22/rvc-models?duplicate=true)\n\n"
+            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
+        )
+        with gr.Tabs():
+            for (name, title, author, cover, vc_fn) in models:
+                with gr.TabItem(name):
+                    with gr.Row():
+                        gr.Markdown(
+                            '<div align="center">'
+                            f'<div>{title}</div>\n'+
+                            (f'<div>Model author: {author}</div>' if author else "")+
+                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
+                            '</div>'
+                        )
+                    with gr.Row():
+                        with gr.Column():
+                            if args.files:
+                                vc_input = gr.Textbox(label="Input audio path")
+                            else:
+                                vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
+                            vc_transpose = gr.Number(label="Transpose", value=0)
+                            vc_f0method = gr.Radio(
+                                label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
+                                choices=["pm", "harvest"],
+                                value="pm",
+                                interactive=True,
+                            )
+                            vc_index_ratio = gr.Slider(
+                                minimum=0,
+                                maximum=1,
+                                label="Retrieval feature ratio",
+                                value=0.6,
+                                interactive=True,
+                            )
+                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
+                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
+                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
+                            vc_submit = gr.Button("Generate", variant="primary")
+                        with gr.Column():
+                            vc_output1 = gr.Textbox(label="Output Message")
+                            vc_output2 = gr.Audio(label="Output Audio")
+                vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
+                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
+        app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)