Upload app.py

app.py

@@ -0,0 +1,319 @@
+import asyncio
+import datetime
+import logging
+import os
+import time
+import traceback
+
+import edge_tts
+import gradio as gr
+import librosa
+import torch
+from fairseq import checkpoint_utils
+
+from config import Config
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from rmvpe import RMVPE
+from vc_infer_pipeline import VC
+
+logging.getLogger("fairseq").setLevel(logging.WARNING)
+logging.getLogger("numba").setLevel(logging.WARNING)
+logging.getLogger("markdown_it").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+
+limitation = os.getenv("SYSTEM") == "spaces"
+
+config = Config()
+
+edge_output_filename = "edge_output.mp3"
+tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
+tts_voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
+
+model_root = "weights"
+models = [
+    d for d in os.listdir(model_root) if os.path.isdir(os.path.join(model_root, d))
+]
+if len(models) == 0:
+    raise ValueError("No model found in `weights` folder")
+models.sort()
+
+
+def model_data(model_name):
+    # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
+    pth_files = [
+        os.path.join(model_root, model_name, f)
+        for f in os.listdir(os.path.join(model_root, model_name))
+        if f.endswith(".pth")
+    ]
+    if len(pth_files) == 0:
+        raise ValueError(f"No pth file found in {model_root}/{model_name}")
+    pth_path = pth_files[0]
+    print(f"Loading {pth_path}")
+    cpt = torch.load(pth_path, 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)
+    version = cpt.get("version", "v1")
+    if version == "v1":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif version == "v2":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+    else:
+        raise ValueError("Unknown version")
+    del net_g.enc_q
+    net_g.load_state_dict(cpt["weight"], strict=False)
+    print("Model loaded")
+    net_g.eval().to(config.device)
+    if config.is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, config)
+    # n_spk = cpt["config"][-3]
+
+    index_files = [
+        os.path.join(model_root, model_name, f)
+        for f in os.listdir(os.path.join(model_root, model_name))
+        if f.endswith(".index")
+    ]
+    if len(index_files) == 0:
+        print("No index file found")
+        index_file = ""
+    else:
+        index_file = index_files[0]
+        print(f"Index file found: {index_file}")
+
+    return tgt_sr, net_g, vc, version, index_file, if_f0
+
+
+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(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    return hubert_model.eval()
+
+
+print("Loading hubert model...")
+hubert_model = load_hubert()
+print("Hubert model loaded.")
+
+print("Loading rmvpe model...")
+rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
+print("rmvpe model loaded.")
+
+
+def tts(
+    model_name,
+    speed,
+    tts_text,
+    tts_voice,
+    f0_up_key,
+    f0_method,
+    index_rate,
+    protect,
+    filter_radius=3,
+    resample_sr=0,
+    rms_mix_rate=0.25,
+):
+    print("------------------")
+    print(datetime.datetime.now())
+    print("tts_text:")
+    print(tts_text)
+    print(f"tts_voice: {tts_voice}")
+    print(f"Model name: {model_name}")
+    print(f"F0: {f0_method}, Key: {f0_up_key}, Index: {index_rate}, Protect: {protect}")
+    try:
+        if limitation and len(tts_text) > 280:
+            print("Error: Text too long")
+            return (
+                f"Text characters should be at most 280 in this huggingface space, but got {len(tts_text)} characters.",
+                None,
+                None,
+            )
+        tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
+        t0 = time.time()
+        if speed >= 0:
+            speed_str = f"+{speed}%"
+        else:
+            speed_str = f"{speed}%"
+        asyncio.run(
+            edge_tts.Communicate(
+                tts_text, "-".join(tts_voice.split("-")[:-1]), rate=speed_str
+            ).save(edge_output_filename)
+        )
+        t1 = time.time()
+        edge_time = t1 - t0
+        audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True)
+        duration = len(audio) / sr
+        print(f"Audio duration: {duration}s")
+        if limitation and duration >= 20:
+            print("Error: Audio too long")
+            return (
+                f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.",
+                edge_output_filename,
+                None,
+            )
+
+        f0_up_key = int(f0_up_key)
+
+        if not hubert_model:
+            load_hubert()
+        if f0_method == "rmvpe":
+            vc.model_rmvpe = rmvpe_model
+        times = [0, 0, 0]
+        audio_opt = vc.pipeline(
+            hubert_model,
+            net_g,
+            0,
+            audio,
+            edge_output_filename,
+            times,
+            f0_up_key,
+            f0_method,
+            index_file,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            None,
+        )
+        if tgt_sr != resample_sr >= 16000:
+            tgt_sr = resample_sr
+        info = f"Success. Time: edge-tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s"
+        print(info)
+        return (
+            info,
+            edge_output_filename,
+            (tgt_sr, audio_opt),
+        )
+    except EOFError:
+        info = (
+            "It seems that the edge-tts output is not valid. "
+            "This may occur when the input text and the speaker do not match. "
+            "For example, maybe you entered Japanese (without alphabets) text but chose non-Japanese speaker?"
+        )
+        print(info)
+        return info, None, None
+    except:
+        info = traceback.format_exc()
+        print(info)
+        return info, None, None
+
+
+initial_md = """
+# RVC text-to-speech webui
+
+This is a text-to-speech webui of RVC models.
+
+Input text ➡[(edge-tts)](https://github.com/rany2/edge-tts)➡ Speech mp3 file ➡[(RVC)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)➡ Final output
+"""
+
+app = gr.Blocks(theme="Hev832/emerald", title="RVC-TTS")
+with app:
+    gr.Markdown(initial_md)
+    with gr.Row():
+        with gr.Column():
+            model_name = gr.Dropdown(label="Model", choices=models, value=models[0])
+            f0_key_up = gr.Number(
+                label="Transpose (the best value depends on the models and speakers)",
+                value=0,
+            )
+        with gr.Column():
+            f0_method = gr.Radio(
+                label="Pitch extraction method (Rmvpe is default)",
+                choices=["rmvpe", "crepe"],  # harvest is too slow
+                value="rmvpe",
+                interactive=True,
+            )
+            index_rate = gr.Slider(
+                minimum=0,
+                maximum=1,
+                label="Index rate",
+                value=1,
+                interactive=True,
+            )
+            protect0 = gr.Slider(
+                minimum=0,
+                maximum=0.5,
+                label="Protect",
+                value=0.33,
+                step=0.01,
+                interactive=True,
+            )
+    with gr.Row():
+        with gr.Column():
+            tts_voice = gr.Dropdown(
+                label="Edge-tts speaker (format: language-Country-Name-Gender)",
+                choices=tts_voices,
+                allow_custom_value=False,
+                value="ja-JP-NanamiNeural-Female",
+            )
+            speed = gr.Slider(
+                minimum=-100,
+                maximum=100,
+                label="Speech speed (%)",
+                value=0,
+                step=10,
+                interactive=True,
+            )
+            tts_text = gr.Textbox(label="Input Text", value="これは日本語テキストから音声への変換デモです。")
+        with gr.Column():
+            but0 = gr.Button("Convert", variant="primary")
+            info_text = gr.Textbox(label="Output info")
+        with gr.Column():
+            edge_tts_output = gr.Audio(label="Edge Voice", type="filepath")
+            tts_output = gr.Audio(label="Result")
+        but0.click(
+            tts,
+            [
+                model_name,
+                speed,
+                tts_text,
+                tts_voice,
+                f0_key_up,
+                f0_method,
+                index_rate,
+                protect0,
+            ],
+            [info_text, edge_tts_output, tts_output],
+        )
+    with gr.Row():
+        examples = gr.Examples(
+            examples_per_page=100,
+            examples=[
+                ["これは日本語テキストから音声への変換デモです。", "ja-JP-NanamiNeural-Female"],
+                [
+                    "This is an English text to speech conversation demo.",
+                    "en-US-AriaNeural-Female",
+                ],
+            ],
+            inputs=[tts_text, tts_voice],
+        )
+
+
+app.launch()