| """Command line: python -m lgtm.cli --text "Xin chào" --lang vi --voice F1 --out out.wav [--ref ref.wav] [--backend onnx]""" |
| import argparse |
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|
| def main(): |
| ap = argparse.ArgumentParser(description="LGTM text-to-speech") |
| ap.add_argument("--text", required=True) |
| ap.add_argument("--lang", default="en", help="en es pt fr de it sv vi ja ko id") |
| ap.add_argument("--voice", default="F1", help="preset (F1-F5, M1-M5) or voice .json") |
| ap.add_argument("--ref", default=None, help="reference .wav to clone the voice from (overrides --voice)") |
| ap.add_argument("--save_voice", default=None, help="save the cloned voice to this .json") |
| ap.add_argument("--out", default="out.wav") |
| ap.add_argument("--model", default=".", help="local model dir or Hugging Face repo id") |
| ap.add_argument("--backend", choices=["torch", "onnx"], default="torch") |
| ap.add_argument("--steps", type=int, default=8) |
| ap.add_argument("--speed", type=float, default=1.05) |
| ap.add_argument("--gpu", action="store_true", help="(onnx) use CUDAExecutionProvider") |
| a = ap.parse_args() |
| if a.backend == "onnx": |
| from .onnx_inference import LGTMOnnx, save_voice_style |
| tts = LGTMOnnx.from_pretrained(a.model, use_gpu=a.gpu) |
| else: |
| from .inference import LGTMTTS, save_voice_style |
| tts = LGTMTTS.from_pretrained(a.model) |
| voice = tts.clone_voice(a.ref) if a.ref else a.voice |
| if a.ref and a.save_voice: |
| save_voice_style(a.save_voice, voice) |
| wav = tts.synthesize(a.text, lang=a.lang, voice=voice, steps=a.steps, speed=a.speed) |
| tts.save_wav(wav, a.out) |
| print(f"wrote {a.out} ({len(wav) / 44100:.2f} s)") |
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|
|
| if __name__ == "__main__": |
| main() |
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|
