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| from flask import Flask, request, Response | |
| from io import BytesIO | |
| import torch | |
| from av import open as avopen | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import cleaned_text_to_sequence, get_bert | |
| from text.cleaner import clean_text | |
| from scipy.io import wavfile | |
| # Flask Init | |
| app = Flask(__name__) | |
| app.config["JSON_AS_ASCII"] = False | |
| def get_text(text, language_str, hps): | |
| norm_text, phone, tone, word2ph = clean_text(text, language_str) | |
| phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) | |
| if hps.data.add_blank: | |
| 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 = get_bert(norm_text, word2ph, language_str) | |
| del word2ph | |
| assert bert.shape[-1] == len(phone), phone | |
| if language_str == "ZH": | |
| bert = bert | |
| ja_bert = torch.zeros(768, len(phone)) | |
| elif language_str == "JA": | |
| ja_bert = bert | |
| bert = torch.zeros(1024, len(phone)) | |
| else: | |
| bert = torch.zeros(1024, len(phone)) | |
| ja_bert = torch.zeros(768, len(phone)) | |
| 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, phone, tone, language | |
| def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language): | |
| bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps) | |
| with torch.no_grad(): | |
| x_tst = phones.to(dev).unsqueeze(0) | |
| tones = tones.to(dev).unsqueeze(0) | |
| lang_ids = lang_ids.to(dev).unsqueeze(0) | |
| bert = bert.to(dev).unsqueeze(0) | |
| ja_bert = ja_bert.to(device).unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev) | |
| speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev) | |
| audio = ( | |
| net_g.infer( | |
| x_tst, | |
| x_tst_lengths, | |
| speakers, | |
| tones, | |
| lang_ids, | |
| bert, | |
| ja_bert, | |
| sdp_ratio=sdp_ratio, | |
| noise_scale=noise_scale, | |
| noise_scale_w=noise_scale_w, | |
| length_scale=length_scale, | |
| )[0][0, 0] | |
| .data.cpu() | |
| .float() | |
| .numpy() | |
| ) | |
| return audio | |
| def replace_punctuation(text, i=2): | |
| punctuation = "，。？！" | |
| for char in punctuation: | |
| text = text.replace(char, char * i) | |
| return text | |
| def wav2(i, o, format): | |
| inp = avopen(i, "rb") | |
| out = avopen(o, "wb", format=format) | |
| if format == "ogg": | |
| format = "libvorbis" | |
| ostream = out.add_stream(format) | |
| for frame in inp.decode(audio=0): | |
| for p in ostream.encode(frame): | |
| out.mux(p) | |
| for p in ostream.encode(None): | |
| out.mux(p) | |
| out.close() | |
| inp.close() | |
| # Load Generator | |
| hps = utils.get_hparams_from_file("./configs/config.json") | |
| dev = "cuda" | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model, | |
| ).to(dev) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint("logs/G_649000.pth", net_g, None, skip_optimizer=True) | |
| def main(): | |
| try: | |
| speaker = request.args.get("speaker") | |
| text = request.args.get("text").replace("/n", "") | |
| sdp_ratio = float(request.args.get("sdp_ratio", 0.2)) | |
| noise = float(request.args.get("noise", 0.5)) | |
| noisew = float(request.args.get("noisew", 0.6)) | |
| length = float(request.args.get("length", 1.2)) | |
| language = request.args.get("language") | |
| if length >= 2: | |
| return "Too big length" | |
| if len(text) >= 250: | |
| return "Too long text" | |
| fmt = request.args.get("format", "wav") | |
| if None in (speaker, text): | |
| return "Missing Parameter" | |
| if fmt not in ("mp3", "wav", "ogg"): | |
| return "Invalid Format" | |
| if language not in ("JA", "ZH"): | |
| return "Invalid language" | |
| except: | |
| return "Invalid Parameter" | |
| with torch.no_grad(): | |
| audio = infer( | |
| text, | |
| sdp_ratio=sdp_ratio, | |
| noise_scale=noise, | |
| noise_scale_w=noisew, | |
| length_scale=length, | |
| sid=speaker, | |
| language=language, | |
| ) | |
| with BytesIO() as wav: | |
| wavfile.write(wav, hps.data.sampling_rate, audio) | |
| torch.cuda.empty_cache() | |
| if fmt == "wav": | |
| return Response(wav.getvalue(), mimetype="audio/wav") | |
| wav.seek(0, 0) | |
| with BytesIO() as ofp: | |
| wav2(wav, ofp, fmt) | |
| return Response( | |
| ofp.getvalue(), mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg" | |
| ) | |