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README.md

@@ -1,13 +1,13 @@
----
-title: MIKU TTS
-emoji: ⚡
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version:  4.39.0
-app_file: app.py
-pinned: false
-duplicated_from: litagin/rvc_okiba_TTS
----
-
+---
+title: MIKU TTS
+emoji: ⚡
+colorFrom: blue
+colorTo: blue
+sdk: gradio
+sdk_version: 5.16.1
+app_file: app.py
+pinned: false
+duplicated_from: litagin/rvc_okiba_TTS
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,311 +1,316 @@
-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 huggingface_hub import snapshot_download
-
-
-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 TTS
-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]
-
-# RVC models
-model_root = snapshot_download(repo_id="NoCrypt/miku_RVC", token=os.environ["TOKEN"])
-models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
-models.sort()
-
-
-def model_data(model_name):
-    # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
-    pth_path = [
-        f"{model_root}/{model_name}/{f}"
-        for f in os.listdir(f"{model_root}/{model_name}")
-        if f.endswith(".pth")
-    ][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 = [
-        f"{model_root}/{model_name}/{f}"
-        for f in os.listdir(f"{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()
-
-
-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}, speed: {speed}")
-    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) > 1000:
-            print("Error: Text too long")
-            return (
-                f"Text characters should be at most 1000 in this huggingface space, but got {len(tts_text)} characters.",
-                None,
-                None,
-            )
-        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 >= 200:
-            print("Error: Audio too long")
-            return (
-                f"Audio should be less than 200 seconds in this huggingface space, but got {duration}s.",
-                edge_output_filename,
-                None,
-            )
-        f0_up_key = int(f0_up_key)
-
-        tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
-        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
-
-
-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.")
-
-initial_md = """
-![banner that says mikutts](https://huggingface.co/spaces/NoCrypt/mikuTTS/resolve/main/imgs/banner_mikutts.webp)
-"""
-
-app = gr.Blocks(theme='NoCrypt/miku')
-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="Tune",
-                value=6,
-            )
-        with gr.Column():
-            f0_method = gr.Radio(
-                label="Pitch extraction method (pm: very fast, low quality, rmvpe: a little slow, high quality)",
-                choices=["pm", "rmvpe"],  # harvest and crepe 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), make sure the gender matches the model",
-                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="こんにちは、私の名前は初音ミクです!")
-        with gr.Column():
-            but0 = gr.Button("Convert", variant="primary")
-            info_text = gr.Textbox(label="Output info")
-        with gr.Column():
-            with gr.Accordion("Edge Voice", open=False):
-                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=[
-                ["こんにちは、私の名前は初音ミクです!", "ja-JP-NanamiNeural-Female", 6],
-                ["Hello there. My name is Hatsune Miku!","en-CA-ClaraNeural-Female", 6],
-                ["Halo. Nama saya Hatsune Miku!","id-ID-GadisNeural-Female", 4],
-                ["Halo. Jenengku Hatsune Miku!","jv-ID-SitiNeural-Female", 10],
-            ],
-            inputs=[tts_text, tts_voice, f0_key_up],
-        )
-
-app.launch()
+import spaces # in windows env, delete related to "spaces"
+@spaces.GPU
+def gpu():
+    pass
+
+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 huggingface_hub import snapshot_download
+
+
+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 TTS
+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]
+
+# RVC models
+model_root = snapshot_download(repo_id="NoCrypt/miku_RVC", token=os.getenv("TOKEN", None))
+models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
+models.sort()
+
+
+def model_data(model_name):
+    # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
+    pth_path = [
+        f"{model_root}/{model_name}/{f}"
+        for f in os.listdir(f"{model_root}/{model_name}")
+        if f.endswith(".pth")
+    ][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 = [
+        f"{model_root}/{model_name}/{f}"
+        for f in os.listdir(f"{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()
+
+
+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}, speed: {speed}")
+    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) > 1000:
+            print("Error: Text too long")
+            return (
+                f"Text characters should be at most 1000 in this huggingface space, but got {len(tts_text)} characters.",
+                None,
+                None,
+            )
+        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 >= 200:
+            print("Error: Audio too long")
+            return (
+                f"Audio should be less than 200 seconds in this huggingface space, but got {duration}s.",
+                edge_output_filename,
+                None,
+            )
+        f0_up_key = int(f0_up_key)
+
+        tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
+        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
+
+
+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.")
+
+initial_md = """
+![banner that says mikutts](https://huggingface.co/spaces/NoCrypt/mikuTTS/resolve/main/imgs/banner_mikutts.webp)
+"""
+
+app = gr.Blocks(theme='NoCrypt/miku')
+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="Tune",
+                value=6,
+            )
+        with gr.Column():
+            f0_method = gr.Radio(
+                label="Pitch extraction method (pm: very fast, low quality, rmvpe: a little slow, high quality)",
+                choices=["pm", "rmvpe"],  # harvest and crepe 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), make sure the gender matches the model",
+                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="こんにちは、私の名前は初音ミクです!")
+        with gr.Column():
+            but0 = gr.Button("Convert", variant="primary")
+            info_text = gr.Textbox(label="Output info")
+        with gr.Column():
+            with gr.Accordion("Edge Voice", open=False):
+                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=[
+                ["こんにちは、私の名前は初音ミクです!", "ja-JP-NanamiNeural-Female", 6],
+                ["Hello there. My name is Hatsune Miku!","en-CA-ClaraNeural-Female", 6],
+                ["Halo. Nama saya Hatsune Miku!","id-ID-GadisNeural-Female", 4],
+                ["Halo. Jenengku Hatsune Miku!","jv-ID-SitiNeural-Female", 10],
+            ],
+            inputs=[tts_text, tts_voice, f0_key_up],
+        )
+
+app.launch(ssr_mode=False)

pre-requirements.txt

@@ -0,0 +1 @@
+pip==24.0

requirements.txt

@@ -1,10 +1,12 @@
-edge_tts==6.1.7
-fairseq==0.12.2
-faiss_cpu==1.7.4
-gradio==3.38.0
-librosa==0.9.1
-numpy==1.23.5
-praat-parselmouth==0.4.3
-pyworld==0.3.4
-torchcrepe==0.0.20
-huggingface_hub
+torch<2.6
+edge_tts>=6.1.7
+fairseq>=0.12.2
+faiss_cpu>=1.7.4
+#gradio==3.38.0
+librosa>=0.9.1
+numpy==1.23.5
+praat-parselmouth>=0.4.3
+pyworld>=0.3.4
+torchcrepe>=0.0.20
+huggingface_hub
+# https://github.com/facebookresearch/fairseq/issues/5511