sbv2-v2/server.py

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)


@app.route("/")
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"
            )