Create server.py

server.py

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+from onnx_modules.V230_OnnxInference import OnnxInferenceSession
+import numpy as np
+import torch
+from scipy.io.wavfile import write
+from text import cleaned_text_to_sequence, get_bert
+from text.cleaner import clean_text
+import utils
+import commons
+import uuid
+from flask import Flask, request, jsonify, render_template_string
+from flask_cors import CORS
+import gradio as gr
+import os
+from threading import Thread
+
+hps = utils.get_hparams_from_file('onnx/BangDreamApi.json')
+device = 'cpu'
+
+BandList = {
+        "PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
+        "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
+        "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
+        "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
+        "Roselia":["友希那","紗夜","リサ","燐子","あこ"],
+        "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
+        "Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
+        "MyGo":["燈","愛音","そよ","立希","楽奈"],
+        "AveMujica":["祥子","睦","海鈴","にゃむ","初華"],
+        "圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"],
+        "凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"],
+        "弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"],
+        "西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"]
+}
+
+Session = OnnxInferenceSession(
+        {
+        "enc" : "onnx/BangDreamApi/BangDreamApi_enc_p.onnx",
+        "emb_g" : "onnx/BangDreamApi/BangDreamApi_emb.onnx",
+        "dp" : "onnx/BangDreamApi/BangDreamApi_dp.onnx",
+        "sdp" : "onnx/BangDreamApi/BangDreamApi_sdp.onnx",
+        "flow" : "onnx/BangDreamApi/BangDreamApi_flow.onnx",
+        "dec" : "onnx/BangDreamApi/BangDreamApi_dec.onnx"
+        },
+        Providers = ["CPUExecutionProvider"]
+    )
+
+def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
+    style_text = None if style_text == "" else style_text
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if True:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    bert_ori = get_bert(
+        norm_text, word2ph, language_str, device, style_text, style_weight
+    )
+    del word2ph
+    assert bert_ori.shape[-1] == len(phone), phone
+
+    if language_str == "ZH":
+        bert = bert_ori
+        ja_bert = torch.randn(1024, len(phone))
+        en_bert = torch.randn(1024, len(phone))
+    elif language_str == "JP":
+        bert = torch.randn(1024, len(phone))
+        ja_bert = bert_ori
+        en_bert = torch.randn(1024, len(phone))
+    elif language_str == "EN":
+        bert = torch.randn(1024, len(phone))
+        ja_bert = torch.randn(1024, len(phone))
+        en_bert = bert_ori
+    else:
+        raise ValueError("language_str should be ZH, JP or EN")
+
+    assert bert.shape[-1] == len(
+        phone
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, en_bert, phone, tone, language
+
+
+def infer(
+    text,
+    sid,
+    style_text=None,
+    style_weight=0.7,
+    sdp_ratio=0.5,
+    noise_scale=0.6,
+    noise_scale_w=0.667,
+    length_scale=1,
+    unique_filename = 'temp.wav'
+):
+    language= 'JP' if is_japanese(text) else 'ZH'
+    bert, ja_bert, en_bert, phones, tone, language = get_text(
+        text,
+        language,
+        hps, 
+        device,
+        style_text=style_text,
+        style_weight=style_weight,
+    )
+    with torch.no_grad():
+        x_tst = phones.unsqueeze(0).to(device).numpy()
+        language = np.zeros_like(x_tst)
+        tone = np.zeros_like(x_tst)
+        bert = bert.to(device).transpose(0, 1).numpy()
+        ja_bert = ja_bert.to(device).transpose(0, 1).numpy()
+        en_bert = en_bert.to(device).transpose(0, 1).numpy()
+        del phones
+        sid = np.array([hps.spk2id[sid]])
+        audio = Session(
+                x_tst,
+                tone,
+                language,
+                bert,
+                ja_bert,
+                en_bert,
+                sid,
+                seed=114514,
+                seq_noise_scale=noise_scale_w,
+                sdp_noise_scale=noise_scale,
+                length_scale=length_scale,
+                sdp_ratio=sdp_ratio,
+            )
+        del x_tst, tone, language, bert, ja_bert, en_bert, sid
+        write(unique_filename, 44100, audio)
+        return (44100,gr.processing_utils.convert_to_16_bit_wav(audio))
+
+def is_japanese(string):
+        for ch in string:
+            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
+                return True
+        return False
+
+
+Flaskapp = Flask(__name__)
+CORS(Flaskapp)
+@Flaskapp.route('/')
+
+def tts():
+    global last_text, last_model
+    speaker = request.args.get('speaker')
+    sdp_ratio = float(request.args.get('sdp_ratio', 0.2))
+    noise_scale = float(request.args.get('noise_scale', 0.6))
+    noise_scale_w = float(request.args.get('noise_scale_w', 0.8))
+    length_scale = float(request.args.get('length_scale', 1))
+    style_weight = float(request.args.get('style_weight', 0.7))
+    style_text = request.args.get('style_text', 'happy')
+    text = request.args.get('text')
+    is_chat = request.args.get('is_chat', 'false').lower() == 'true'
+    #model = request.args.get('model',modelPaths[-1])
+    
+    if not speaker or not text:
+        return render_template_string("""
+            <!DOCTYPE html>
+            <html>
+            <head>
+                <title>TTS API Documentation</title>
+            </head>
+            <body>
+                <iframe src="https://mahiruoshi-bangdream-bert-vits2.hf.space" style="width:100%; height:100vh; border:none;"></iframe>
+            </body>
+            </html>
+        """)
+    '''
+    if model != last_model:
+        unique_filename  = loadmodel(model)
+        last_model = model
+    '''
+    if is_chat and text == last_text:
+        # Generate 1 second of silence and return
+        unique_filename = 'blank.wav'
+        silence = np.zeros(44100, dtype=np.int16)
+        write(unique_filename , 44100, silence)
+    else:
+        last_text = text
+        unique_filename = f"temp{uuid.uuid4()}.wav"
+        infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale,sid = speaker, style_text=style_text, style_weight=style_weight,unique_filename=unique_filename)
+    with open(unique_filename ,'rb') as bit:
+        wav_bytes = bit.read()
+    os.remove(unique_filename)
+    headers = {
+            'Content-Type': 'audio/wav',
+            'Text': unique_filename .encode('utf-8')}
+    return wav_bytes, 200, headers
+
+if __name__ == "__main__":
+    speaker_ids = hps.spk2id
+    speakers = list(speaker_ids.keys())
+    last_text = ""
+    Flaskapp.run(host="0.0.0.0", port=5000,debug=True)