app.py

from flask import Flask, request, jsonify, send_file
import tempfile
import logging
import json

from torch import no_grad, LongTensor
import soundfile as sf
import utils
import ONNXVITS_infer

app = Flask(__name__)
logging.getLogger('numba').setLevel(logging.WARNING)

TRILINGUAL = {
    "title": "Trilingual",
    "model_path": "./pretrained_models/G_trilingual.pth",
    "config_path": "./configs/uma_trilingual.json",
    "onnx_dir": "./ONNX_net/G_trilingual/"
}

JAPANESE = {
    "title": "Japanese",
    "model_path": "./pretrained_models/G_jp.pth",
    "config_path": "./configs/uma87.json",
    "onnx_dir": "./ONNX_net/G_jp/"
}

models_tts = []
models_info = [
    TRILINGUAL,
    JAPANESE
]
MODEL = { "japanese": JAPANESE, "trilingual": TRILINGUAL }

def load_models():
    for info in models_info:
        hps = utils.get_hparams_from_file(info['config_path'])
        model = ONNXVITS_infer.SynthesizerTrn(
            len(hps.symbols),
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            ONNX_dir=info["onnx_dir"],
            **hps.model
        )
        utils.load_checkpoint(info['model_path'], model, None)
        model.eval()
        models_tts.append({
            "name": info["title"],
            "model": model,
            "hps": hps,
            "speaker_ids": hps.speakers
        })

load_models()

def get_text(text, hps, is_symbol):
    from text import text_to_sequence
    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
    if hps.data.add_blank:
        from commons import intersperse
        text_norm = intersperse(text_norm, 0)
    return LongTensor(text_norm)

def tts_process(text, speaker, speed, model_data, is_symbol):
    model = model_data["model"]
    hps = model_data["hps"]
    speaker_id = model_data["speaker_ids"][speaker]
    stn_tst = get_text(text, hps, is_symbol)
    with no_grad():
        x_tst = stn_tst.unsqueeze(0)
        x_tst_lengths = LongTensor([stn_tst.size(0)])
        sid = LongTensor([speaker_id])
        audio = model.infer(
            x_tst, x_tst_lengths, sid=sid, 
            noise_scale=0.667, noise_scale_w=0.8, 
            length_scale=1.0 / speed
        )[0][0, 0].data.cpu().float().numpy()
    return audio, hps.data.sampling_rate

def read_json(path):
    with open(path, "r") as f:
        return json.loads(f.read())


def get_model_data(model):
    return next((m for m in models_tts if m["name"].lower() == model.lower()), None)
   
@app.route("/")
def index():
    return jsonify({"status": "OK" })

@app.route("/<model>/speakers", methods=["GET"])
def speakers(model):
    global MODEL
    model = model.lower()
    model_info = MODEL.get(model, None)
    
    if model_info is None:
        return jsonify({ "error": f"Model not found for `{model}`"}), 404
        
    config = read_json(model_info["config_path"])
    return jsonify({"model_name": model_info["title"], "speakers": config["speakers"] })

@app.route("/<model>/generate", methods=["POST", "GET"])
def generate(model):
    data = request.json if request.method == "POST" else request.args
    text = data.get("text")
    speaker = data.get("speaker")
    speed = float(data.get("speed", 1.0))
    is_symbol = data.get("is_symbol", False)
    speaker_id = data.get("speaker_id")
    
    if not text:
        return jsonify({"error": "Missing parameter 'text'"}), 400

    model_data = get_model_data(model)
    if not model_data:
        return jsonify({"error": "Model not found"}), 404
        
    speaker_ids = { str(id): speaker for speaker, id in model_data["speaker_ids"].items() }
    
    if not speaker:
        if speaker_id is not None:
            speaker = speaker_ids.get(str(speaker_id), None)       
            if not speaker:
                return jsonify({"error": f"Speaker ID `{speaker_id}` not found"}), 404
        else:
            return jsonify({"error": "Missing 'speaker' or 'speaker_id'"}), 400

    if speaker not in model_data["speaker_ids"]:
        return jsonify({"error": f"Speaker `{speaker}` not found"}), 404

    try:
        audio, sampling_rate = tts_process(text, speaker, speed, model_data, is_symbol)
        temp_wav = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
        sf.write(temp_wav.name, audio, sampling_rate, format="wav")
        temp_wav.close()
        return send_file(temp_wav.name, as_attachment=True, download_name="output.wav")
    except Exception as e:
        print(e)
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=True)