Add api.py

api.py

@@ -0,0 +1,324 @@
+import argparse
+import os
+import signal
+import sys
+from time import time as ttime
+import torch
+import librosa
+import soundfile as sf
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.responses import StreamingResponse
+import uvicorn
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+from feature_extractor import cnhubert
+from io import BytesIO
+from module.models import SynthesizerTrn
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+from module.mel_processing import spectrogram_torch
+from my_utils import load_audio
+
+DEFAULT_PORT = 9880
+DEFAULT_CNHUBERT = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+DEFAULT_BERT = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+DEFAULT_HALF = True
+
+DEFAULT_GPT = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+DEFAULT_SOVITS = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+
+AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
+
+parser = argparse.ArgumentParser(description="GPT-SoVITS api")
+
+parser.add_argument("-g", "--gpt_path", type=str, default="", help="GPT模型路径")
+parser.add_argument("-s", "--sovits_path", type=str, default="", help="SoVITS模型路径")
+
+parser.add_argument("-dr", "--default_refer_path", type=str, default="",
+                    help="默认参考音频路径, 请求缺少参考音频时调用")
+parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
+parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
+
+parser.add_argument("-d", "--device", type=str, default=AVAILABLE_COMPUTE, help="cuda / cpu")
+parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT, help="default: 9880")
+parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
+parser.add_argument("-hp", "--half_precision", action='store_true', default=False)
+
+parser.add_argument("-hb", "--hubert_path", type=str, default=DEFAULT_CNHUBERT)
+parser.add_argument("-b", "--bert_path", type=str, default=DEFAULT_BERT)
+
+args = parser.parse_args()
+
+gpt_path = args.gpt_path
+sovits_path = args.sovits_path
+
+default_refer_path = args.default_refer_path
+default_refer_text = args.default_refer_text
+default_refer_language = args.default_refer_language
+has_preset = False
+
+device = args.device
+port = args.port
+host = args.bind_addr
+is_half = args.half_precision
+
+cnhubert_base_path = args.hubert_path
+bert_path = args.bert_path
+
+if gpt_path == "":
+    gpt_path = DEFAULT_GPT
+    print("[WARN] 未指定GPT模型路径")
+if sovits_path == "":
+    sovits_path = DEFAULT_SOVITS
+    print("[WARN] 未指定SoVITS模型路径")
+
+if default_refer_path == "" or default_refer_text == "" or default_refer_language == "":
+    default_refer_path, default_refer_text, default_refer_language = "", "", ""
+    print("[INFO] 未指定默认参考音频")
+    has_preset = False
+else:
+    print(f"[INFO] 默认参考音频路径: {default_refer_path}")
+    print(f"[INFO] 默认参考音频文本: {default_refer_text}")
+    print(f"[INFO] 默认参考音频语种: {default_refer_language}")
+    has_preset = True
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+# bert_model = AutoModelForSequenceClassification.from_pretrained(bert_path, config=bert_path+"/config.json")
+if (is_half == True):
+    bert_model = bert_model.half().to(device)
+else:
+    bert_model = bert_model.to(device)
+
+
+# bert_model=bert_model.to(device)
+def get_bert_feature(text, word2ph):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)  #####输入是long不用管精度问题，精度随bert_model
+        res = bert_model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
+    assert len(word2ph) == len(text)
+    phone_level_feature = []
+    for i in range(len(word2ph)):
+        repeat_feature = res[i].repeat(word2ph[i], 1)
+        phone_level_feature.append(repeat_feature)
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    # if(is_half==True):phone_level_feature=phone_level_feature.half()
+    return phone_level_feature.T
+
+
+n_semantic = 1024
+dict_s2 = torch.load(sovits_path, map_location="cpu")
+hps = dict_s2["config"]
+
+
+class DictToAttrRecursive:
+    def __init__(self, input_dict):
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                # 如果值是字典，递归调用构造函数
+                setattr(self, key, DictToAttrRecursive(value))
+            else:
+                setattr(self, key, value)
+
+
+hps = DictToAttrRecursive(hps)
+hps.model.semantic_frame_rate = "25hz"
+dict_s1 = torch.load(gpt_path, map_location="cpu")
+config = dict_s1["config"]
+ssl_model = cnhubert.get_model()
+if is_half:
+    ssl_model = ssl_model.half().to(device)
+else:
+    ssl_model = ssl_model.to(device)
+
+vq_model = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    n_speakers=hps.data.n_speakers,
+    **hps.model)
+if is_half:
+    vq_model = vq_model.half().to(device)
+else:
+    vq_model = vq_model.to(device)
+vq_model.eval()
+print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+hz = 50
+max_sec = config['data']['max_sec']
+t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
+t2s_model.load_state_dict(dict_s1["weight"])
+if is_half:
+    t2s_model = t2s_model.half()
+t2s_model = t2s_model.to(device)
+t2s_model.eval()
+total = sum([param.nelement() for param in t2s_model.parameters()])
+print("Number of parameter: %.2fM" % (total / 1e6))
+
+
+def get_spepc(hps, filename):
+    audio = load_audio(filename, int(hps.data.sampling_rate))
+    audio = torch.FloatTensor(audio)
+    audio_norm = audio
+    audio_norm = audio_norm.unsqueeze(0)
+    spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
+                             hps.data.win_length, center=False)
+    return spec
+
+
+dict_language = {
+    "中文": "zh",
+    "英文": "en",
+    "日文": "ja",
+    "ZH": "zh",
+    "EN": "en",
+    "JA": "ja",
+    "zh": "zh",
+    "en": "en",
+    "ja": "ja"
+}
+
+
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
+    t0 = ttime()
+    prompt_text = prompt_text.strip("\n")
+    prompt_language, text = prompt_language, text.strip("\n")
+    with torch.no_grad():
+        wav16k, sr = librosa.load(ref_wav_path, sr=16000)  # 派蒙
+        wav16k = torch.from_numpy(wav16k)
+        if (is_half == True):
+            wav16k = wav16k.half().to(device)
+        else:
+            wav16k = wav16k.to(device)
+        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
+        codes = vq_model.extract_latent(ssl_content)
+        prompt_semantic = codes[0, 0]
+    t1 = ttime()
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
+    phones1 = cleaned_text_to_sequence(phones1)
+    texts = text.split("\n")
+    audio_opt = []
+    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
+    for text in texts:
+        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
+        phones2 = cleaned_text_to_sequence(phones2)
+        if (prompt_language == "zh"):
+            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
+        else:
+            bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
+                device)
+        if (text_language == "zh"):
+            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
+        else:
+            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
+        bert = torch.cat([bert1, bert2], 1)
+
+        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+        prompt = prompt_semantic.unsqueeze(0).to(device)
+        t2 = ttime()
+        with torch.no_grad():
+            # pred_semantic = t2s_model.model.infer(
+            pred_semantic, idx = t2s_model.model.infer_panel(
+                all_phoneme_ids,
+                all_phoneme_len,
+                prompt,
+                bert,
+                # prompt_phone_len=ph_offset,
+                top_k=config['inference']['top_k'],
+                early_stop_num=hz * max_sec)
+        t3 = ttime()
+        # print(pred_semantic.shape,idx)
+        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
+        refer = get_spepc(hps, ref_wav_path)  # .to(device)
+        if (is_half == True):
+            refer = refer.half().to(device)
+        else:
+            refer = refer.to(device)
+        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
+        audio = \
+            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
+                            refer).detach().cpu().numpy()[
+                0, 0]  ###试试重建不带上prompt部分
+        audio_opt.append(audio)
+        audio_opt.append(zero_wav)
+        t4 = ttime()
+    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
+
+
+def restart():
+    python = sys.executable
+    os.execl(python, python, *sys.argv)
+
+
+def handle(command, refer_wav_path, prompt_text, prompt_language, text, text_language):
+    if command == "/restart":
+        restart()
+    elif command == "/exit":
+        os.kill(os.getpid(), signal.SIGTERM)
+        exit(0)
+
+    if (
+            refer_wav_path == "" or refer_wav_path is None
+            or prompt_text == "" or prompt_text is None
+            or prompt_language == "" or prompt_language is None
+    ):
+        refer_wav_path, prompt_text, prompt_language = (
+            default_refer_path,
+            default_refer_text,
+            default_refer_language,
+        )
+        if not has_preset:
+            raise HTTPException(status_code=400, detail="未指定参考音频且接口无预设")
+
+    with torch.no_grad():
+        gen = get_tts_wav(
+            refer_wav_path, prompt_text, prompt_language, text, text_language
+        )
+        sampling_rate, audio_data = next(gen)
+
+    wav = BytesIO()
+    sf.write(wav, audio_data, sampling_rate, format="wav")
+    wav.seek(0)
+
+    torch.cuda.empty_cache()
+    return StreamingResponse(wav, media_type="audio/wav")
+
+
+app = FastAPI()
+
+
+@app.post("/")
+async def tts_endpoint(request: Request):
+    json_post_raw = await request.json()
+    return handle(
+        json_post_raw.get("command"),
+        json_post_raw.get("refer_wav_path"),
+        json_post_raw.get("prompt_text"),
+        json_post_raw.get("prompt_language"),
+        json_post_raw.get("text"),
+        json_post_raw.get("text_language"),
+    )
+
+
+@app.get("/")
+async def tts_endpoint(
+        command: str = None,
+        refer_wav_path: str = None,
+        prompt_text: str = None,
+        prompt_language: str = None,
+        text: str = None,
+        text_language: str = None,
+):
+    return handle(command, refer_wav_path, prompt_text, prompt_language, text, text_language)
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host=host, port=port, workers=1)