42 lines
1.8 KiB
Python
42 lines
1.8 KiB
Python
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import argparse, os
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import numpy as np
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import soundfile as sf
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from kokoro import KPipeline
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("text", help="Japanese text")
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ap.add_argument("--voice", default="jf_alpha", help="e.g. jf_alpha, jf_tebukuro, jm_kumo ...")
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ap.add_argument("--speed", type=float, default=1.0, help="1.0 = normal, >1 faster, <1 slower")
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ap.add_argument("--out", default="out.wav")
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args = ap.parse_args()
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# Japanese pipeline
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pipeline = KPipeline(lang_code="j") # Japanese [oai_citation:2‡Hugging Face](https://huggingface.co/hexgrad/Kokoro-82M/blob/938257c07e326d534677886ca13829b39347fff7/README.md)
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# Split at Japanese punctuation to avoid “rushing” long passages
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# (Kokoro voices often behave best around moderate chunk sizes.) [oai_citation:3‡Hugging Face](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md)
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split_pattern = r"(?<=[。！？\n])\s*"
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audio_parts = []
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generator = pipeline(args.text, voice=args.voice, speed=args.speed, split_pattern=split_pattern)
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for _, _, audio in generator:
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# audio can be a torch.Tensor or already a numpy array depending on device/backend
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if hasattr(audio, "detach"): # torch.Tensor
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audio = audio.detach()
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if hasattr(audio, "cpu"): # move to CPU if needed
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audio = audio.cpu()
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if hasattr(audio, "numpy"): # torch -> numpy
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audio = audio.numpy()
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audio_parts.append(np.asarray(audio, dtype=np.float32))
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if not audio_parts:
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raise SystemExit("No audio generated (empty input?)")
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audio_all = np.concatenate(audio_parts, axis=0)
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sf.write(args.out, audio_all, 24000)
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print(f"Wrote: {args.out} (24kHz)")
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if __name__ == "__main__":
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main()
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