mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
API修复优化 (#1503)
* model control * Mix timbre * Fix some detail problems * Optimize detail * Add int32 * Add example * Add aac pcm32 support
This commit is contained in:
KamioRinn
GitHub
parent
94fe9b3963
commit
f4e812ae16
184
api.py
184
api.py
@ -20,6 +20,7 @@
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`-hp` - `覆盖 config.py 使用半精度`
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`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
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·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
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·-st` - `返回的音频数据类型, 默认int16, "int16", "int32"`
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·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`
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`-hb` - `cnhubert路径`
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@ -74,7 +75,7 @@ RESP:
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手动指定当次推理所使用的参考音频，并提供参数:
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GET:
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`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&top_k=20&top_p=0.6&temperature=0.6&speed=1`
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`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&top_k=20&top_p=0.6&temperature=0.6&speed=1&inp_refs="456.wav"&inp_refs="789.wav"`
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POST:
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```json
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{
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@ -86,7 +87,8 @@ POST:
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"top_k": 20,
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"top_p": 0.6,
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"temperature": 0.6,
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"speed": 1
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"speed": 1,
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"inp_refs": ["456.wav","789.wav"]
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}
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```
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@ -153,7 +155,7 @@ from time import time as ttime
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import torch
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import librosa
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import soundfile as sf
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from fastapi import FastAPI, Request, HTTPException
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from fastapi import FastAPI, Request, Query, HTTPException
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from fastapi.responses import StreamingResponse, JSONResponse
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import uvicorn
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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@ -195,8 +197,24 @@ def is_full(*items): # 任意一项为空返回False
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return True
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def change_sovits_weights(sovits_path):
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global vq_model, hps
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class Speaker:
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def __init__(self, name, gpt, sovits, phones = None, bert = None, prompt = None):
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self.name = name
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self.sovits = sovits
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self.gpt = gpt
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self.phones = phones
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self.bert = bert
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self.prompt = prompt
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speaker_list = {}
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class Sovits:
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def __init__(self, vq_model, hps):
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self.vq_model = vq_model
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self.hps = hps
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def get_sovits_weights(sovits_path):
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dict_s2 = torch.load(sovits_path, map_location="cpu")
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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@ -205,7 +223,7 @@ def change_sovits_weights(sovits_path):
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hps.model.version = "v1"
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else:
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hps.model.version = "v2"
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print("sovits版本:",hps.model.version)
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logger.info(f"模型版本: {hps.model.version}")
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model_params_dict = vars(hps.model)
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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@ -222,10 +240,17 @@ def change_sovits_weights(sovits_path):
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vq_model.eval()
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vq_model.load_state_dict(dict_s2["weight"], strict=False)
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sovits = Sovits(vq_model, hps)
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return sovits
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def change_gpt_weights(gpt_path):
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global hz, max_sec, t2s_model, config
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hz = 50
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class Gpt:
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def __init__(self, max_sec, t2s_model):
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self.max_sec = max_sec
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self.t2s_model = t2s_model
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global hz
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hz = 50
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def get_gpt_weights(gpt_path):
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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config = dict_s1["config"]
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max_sec = config["data"]["max_sec"]
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@ -238,6 +263,19 @@ def change_gpt_weights(gpt_path):
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total = sum([param.nelement() for param in t2s_model.parameters()])
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logger.info("Number of parameter: %.2fM" % (total / 1e6))
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gpt = Gpt(max_sec, t2s_model)
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return gpt
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def change_gpt_sovits_weights(gpt_path,sovits_path):
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try:
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gpt = get_gpt_weights(gpt_path)
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sovits = get_sovits_weights(sovits_path)
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except Exception as e:
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return JSONResponse({"code": 400, "message": str(e)}, status_code=400)
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speaker_list["default"] = Speaker(name="default", gpt=gpt, sovits=sovits)
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return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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@ -289,14 +327,14 @@ def get_phones_and_bert(text,language,version,final=False):
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if language == "zh":
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if re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.text_normalize(formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return get_phones_and_bert(formattext,"zh",version)
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else:
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phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
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bert = get_bert_feature(norm_text, word2ph).to(device)
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elif language == "yue" and re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.text_normalize(formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return get_phones_and_bert(formattext,"yue",version)
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else:
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phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
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@ -375,8 +413,11 @@ class DictToAttrRecursive(dict):
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.sampling_rate))
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audio,_ = librosa.load(filename, int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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maxx=audio.abs().max()
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if(maxx>1):
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audio/=min(2,maxx)
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
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@ -448,22 +489,32 @@ def pack_raw(audio_bytes, data, rate):
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def pack_wav(audio_bytes, rate):
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data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
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wav_bytes = BytesIO()
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sf.write(wav_bytes, data, rate, format='wav')
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if is_int32:
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data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int32)
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wav_bytes = BytesIO()
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sf.write(wav_bytes, data, rate, format='WAV', subtype='PCM_32')
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else:
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data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
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wav_bytes = BytesIO()
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sf.write(wav_bytes, data, rate, format='WAV')
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return wav_bytes
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def pack_aac(audio_bytes, data, rate):
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if is_int32:
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pcm = 's32le'
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bit_rate = '256k'
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else:
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pcm = 's16le'
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bit_rate = '128k'
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process = subprocess.Popen([
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'ffmpeg',
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'-f', 's16le', # 输入16位有符号小端整数PCM
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'-f', pcm, # 输入16位有符号小端整数PCM
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'-ar', str(rate), # 设置采样率
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'-ac', '1', # 单声道
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'-i', 'pipe:0', # 从管道读取输入
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'-c:a', 'aac', # 音频编码器为AAC
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'-b:a', '192k', # 比特率
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'-b:a', bit_rate, # 比特率
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'-vn', # 不包含视频
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'-f', 'adts', # 输出AAC数据流格式
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'pipe:1' # 将输出写入管道
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@ -504,10 +555,21 @@ def only_punc(text):
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return not any(t.isalnum() or t.isalpha() for t in text)
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, top_k= 20, top_p = 0.6, temperature = 0.6, speed = 1):
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splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, top_k= 15, top_p = 0.6, temperature = 0.6, speed = 1, inp_refs = None, spk = "default"):
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infer_sovits = speaker_list[spk].sovits
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vq_model = infer_sovits.vq_model
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hps = infer_sovits.hps
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infer_gpt = speaker_list[spk].gpt
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t2s_model = infer_gpt.t2s_model
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max_sec = infer_gpt.max_sec
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
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prompt_language, text = prompt_language, text.strip("\n")
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dtype = torch.float16 if is_half == True else torch.float32
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zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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@ -523,6 +585,19 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
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codes = vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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prompt = prompt_semantic.unsqueeze(0).to(device)
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refers=[]
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if(inp_refs):
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for path in inp_refs:
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try:
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refer = get_spepc(hps, path).to(dtype).to(device)
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refers.append(refer)
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except Exception as e:
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logger.error(e)
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if(len(refers)==0):
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refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
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t1 = ttime()
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version = vq_model.version
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os.environ['version'] = version
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@ -538,16 +613,15 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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continue
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audio_opt = []
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if (text[-1] not in splits): text += "。" if text_language != "en" else "."
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phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
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prompt = prompt_semantic.unsqueeze(0).to(device)
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t2 = ttime()
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with torch.no_grad():
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# pred_semantic = t2s_model.model.infer(
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pred_semantic, idx = t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_len,
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@ -558,23 +632,22 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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top_p = top_p,
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temperature = temperature,
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early_stop_num=hz * max_sec)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
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t3 = ttime()
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# print(pred_semantic.shape,idx)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
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refer = get_spepc(hps, ref_wav_path) # .to(device)
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if (is_half == True):
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refer = refer.half().to(device)
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else:
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refer = refer.to(device)
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# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
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audio = \
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vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
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refer,speed=speed).detach().cpu().numpy()[
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refers,speed=speed).detach().cpu().numpy()[
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0, 0] ###试试重建不带上prompt部分
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max_audio=np.abs(audio).max()
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if max_audio>1:
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audio/=max_audio
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audio_opt.append(audio)
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audio_opt.append(zero_wav)
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t4 = ttime()
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audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),hps.data.sampling_rate)
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if is_int32:
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audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 2147483647).astype(np.int32),hps.data.sampling_rate)
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else:
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audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),hps.data.sampling_rate)
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# logger.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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if stream_mode == "normal":
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audio_bytes, audio_chunk = read_clean_buffer(audio_bytes)
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@ -615,7 +688,7 @@ def handle_change(path, text, language):
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return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
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def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc, top_k, top_p, temperature, speed):
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def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc, top_k, top_p, temperature, speed, inp_refs):
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if (
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refer_wav_path == "" or refer_wav_path is None
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or prompt_text == "" or prompt_text is None
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@ -634,7 +707,7 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cu
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else:
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text = cut_text(text,cut_punc)
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return StreamingResponse(get_tts_wav(refer_wav_path, prompt_text, prompt_language, text, text_language, top_k, top_p, temperature, speed), media_type="audio/"+media_type)
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return StreamingResponse(get_tts_wav(refer_wav_path, prompt_text, prompt_language, text, text_language, top_k, top_p, temperature, speed, inp_refs), media_type="audio/"+media_type)
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@ -691,6 +764,7 @@ parser.add_argument("-hp", "--half_precision", action="store_true", default=Fals
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# 此时 full_precision==True, half_precision==False
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parser.add_argument("-sm", "--stream_mode", type=str, default="close", help="流式返回模式, close / normal / keepalive")
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parser.add_argument("-mt", "--media_type", type=str, default="wav", help="音频编码格式, wav / ogg / aac")
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parser.add_argument("-st", "--sub_type", type=str, default="int16", help="音频数据类型, int16 / int32")
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parser.add_argument("-cp", "--cut_punc", type=str, default="", help="文本切分符号设定, 符号范围,.;?!、，。？！；：…")
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# 切割常用分句符为 `python ./api.py -cp ".?!。？！"`
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parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
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@ -752,6 +826,14 @@ else:
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media_type = "ogg"
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logger.info(f"编码格式: {media_type}")
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# 音频数据类型
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if args.sub_type.lower() == 'int32':
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is_int32 = True
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logger.info(f"数据类型: int32")
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else:
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is_int32 = False
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logger.info(f"数据类型: int16")
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# 初始化模型
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cnhubert.cnhubert_base_path = cnhubert_base_path
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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@ -763,9 +845,7 @@ if is_half:
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else:
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bert_model = bert_model.to(device)
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ssl_model = ssl_model.to(device)
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change_sovits_weights(sovits_path)
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change_gpt_weights(gpt_path)
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change_gpt_sovits_weights(gpt_path = gpt_path, sovits_path = sovits_path)
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@ -777,14 +857,18 @@ app = FastAPI()
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@app.post("/set_model")
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async def set_model(request: Request):
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json_post_raw = await request.json()
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global gpt_path
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gpt_path=json_post_raw.get("gpt_model_path")
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global sovits_path
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sovits_path=json_post_raw.get("sovits_model_path")
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logger.info("gptpath"+gpt_path+";vitspath"+sovits_path)
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change_sovits_weights(sovits_path)
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change_gpt_weights(gpt_path)
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return "ok"
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return change_gpt_sovits_weights(
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gpt_path = json_post_raw.get("gpt_model_path"),
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sovits_path = json_post_raw.get("sovits_model_path")
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)
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@app.get("/set_model")
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||||
async def set_model(
|
||||
gpt_model_path: str = None,
|
||||
sovits_model_path: str = None,
|
||||
):
|
||||
return change_gpt_sovits_weights(gpt_path = gpt_model_path, sovits_path = sovits_model_path)
|
||||
|
||||
|
||||
@app.post("/control")
|
||||
@ -827,10 +911,11 @@ async def tts_endpoint(request: Request):
|
||||
json_post_raw.get("text"),
|
||||
json_post_raw.get("text_language"),
|
||||
json_post_raw.get("cut_punc"),
|
||||
json_post_raw.get("top_k", 10),
|
||||
json_post_raw.get("top_k", 15),
|
||||
json_post_raw.get("top_p", 1.0),
|
||||
json_post_raw.get("temperature", 1.0),
|
||||
json_post_raw.get("speed", 1.0)
|
||||
json_post_raw.get("speed", 1.0),
|
||||
json_post_raw.get("inp_refs", [])
|
||||
)
|
||||
|
||||
|
||||
@ -842,12 +927,13 @@ async def tts_endpoint(
|
||||
text: str = None,
|
||||
text_language: str = None,
|
||||
cut_punc: str = None,
|
||||
top_k: int = 10,
|
||||
top_k: int = 15,
|
||||
top_p: float = 1.0,
|
||||
temperature: float = 1.0,
|
||||
speed: float = 1.0
|
||||
speed: float = 1.0,
|
||||
inp_refs: list = Query(default=[])
|
||||
):
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc, top_k, top_p, temperature, speed)
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc, top_k, top_p, temperature, speed, inp_refs)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user