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80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
#!/usr/bin/env python3
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import argparse
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import json
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import logging
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import sys
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import time
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from pathlib import Path
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import torch
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from .vits.lightning import VitsModel
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from .vits.utils import audio_float_to_int16
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from .vits.wavfile import write as write_wav
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_LOGGER = logging.getLogger("piper_train.infer")
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def main():
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"""Main entry point"""
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logging.basicConfig(level=logging.DEBUG)
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parser = argparse.ArgumentParser(prog="piper_train.infer")
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parser.add_argument(
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"--checkpoint", required=True, help="Path to model checkpoint (.ckpt)"
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)
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parser.add_argument("--output-dir", required=True, help="Path to write WAV files")
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parser.add_argument("--sample-rate", type=int, default=22050)
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args = parser.parse_args()
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args.output_dir = Path(args.output_dir)
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args.output_dir.mkdir(parents=True, exist_ok=True)
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model = VitsModel.load_from_checkpoint(args.checkpoint, dataset=None)
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# Inference only
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model.eval()
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with torch.no_grad():
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model.model_g.dec.remove_weight_norm()
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for i, line in enumerate(sys.stdin):
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line = line.strip()
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if not line:
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continue
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utt = json.loads(line)
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utt_id = str(i)
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phoneme_ids = utt["phoneme_ids"]
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speaker_id = utt.get("speaker_id")
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text = torch.LongTensor(phoneme_ids).unsqueeze(0)
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text_lengths = torch.LongTensor([len(phoneme_ids)])
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scales = [0.667, 1.0, 0.8]
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sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None
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start_time = time.perf_counter()
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audio = model(text, text_lengths, scales, sid=sid).detach().numpy()
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audio = audio_float_to_int16(audio)
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end_time = time.perf_counter()
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audio_duration_sec = audio.shape[-1] / args.sample_rate
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infer_sec = end_time - start_time
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real_time_factor = (
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infer_sec / audio_duration_sec if audio_duration_sec > 0 else 0.0
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)
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_LOGGER.debug(
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"Real-time factor for %s: %0.2f (infer=%0.2f sec, audio=%0.2f sec)",
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i + 1,
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real_time_factor,
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infer_sec,
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audio_duration_sec,
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)
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output_path = args.output_dir / f"{utt_id}.wav"
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write_wav(str(output_path), args.sample_rate, audio)
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if __name__ == "__main__":
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main()
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