97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import argparse
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import io
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from pathlib import Path
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import soundfile as sf
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import torch
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import uvicorn
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from torch import no_grad, LongTensor
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import commons
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import utils
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from models import SynthesizerTrn
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from text import text_to_sequence
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app = FastAPI()
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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language_marks = {
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"日本語": "[JA]",
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"简体中文": "[ZH]",
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"English": "[EN]",
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"Mix": "",
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}
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def get_text(text: str, is_symbol: bool):
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text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = LongTensor(text_norm)
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return text_norm
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def tts_fn(text: str, speaker: str, language: str, speed: float):
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if language is not None:
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text = language_marks[language] + text + language_marks[language]
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speaker_id = speaker_ids[speaker]
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stn_tst = get_text(text, False)
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with no_grad():
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x_tst = stn_tst.unsqueeze(0).to(device)
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x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
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sid = LongTensor([speaker_id]).to(device)
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audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
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length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
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del stn_tst, x_tst, x_tst_lengths, sid
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return audio
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@app.get("/tts/options")
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async def get_options():
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return {"speakers": list(speaker_ids.keys()), "languages": list(language_marks.keys())}
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@app.post("/tts")
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async def generate(request: Request):
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data = await request.json()
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text = data.get('text')
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speaker = data.get('speaker')
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language = data.get('language', '日本語')
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speed = data.get('speed', 1.0)
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if not text or not speaker or language not in language_marks:
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raise HTTPException(status_code=400, detail="Invalid input parameters")
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audio = tts_fn(text, speaker, language, speed)
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audio_io = io.BytesIO()
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sf.write(audio_io, audio, hps.data.sampling_rate, format='OGG')
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audio_io.seek(0)
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return StreamingResponse(audio_io, media_type='audio/ogg',
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headers={'Content-Disposition': 'attachment; filename="output.ogg"'})
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-d", default="./OUTPUT_MODEL",
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help="directory to your fine-tuned model (contains G_latest.pth and config.json)")
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args = parser.parse_args()
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d_config = Path(args.d) / "config.json"
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d_model = Path(args.d) / "G_latest.pth"
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hps = utils.get_hparams_from_file(d_config)
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model = SynthesizerTrn(
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len(hps.symbols),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model).to(device)
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_ = model.eval()
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utils.load_checkpoint(d_model, model, None)
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speaker_ids = hps.speakers
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uvicorn.run(app, host='0.0.0.0', port=27519)
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