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86 lines
2.1 KiB
Python
86 lines
2.1 KiB
Python
#!/usr/bin/env python3
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import argparse
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import json
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import time
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import sys
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import torch
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_NOISE_SCALE = 0.667
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_LENGTH_SCALE = 1.0
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_NOISE_W = 0.8
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-m", "--model", required=True, help="Path to Torchscript file (.ts)"
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)
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parser.add_argument("-c", "--config", help="Path to model config file (.json)")
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args = parser.parse_args()
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if not args.config:
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args.config = f"{args.model}.json"
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with open(args.config, "r", encoding="utf-8") as config_file:
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config = json.load(config_file)
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sample_rate = config["audio"]["sample_rate"]
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utterances = [json.loads(line) for line in sys.stdin]
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start_time = time.monotonic_ns()
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model = torch.jit.load(args.model)
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end_time = time.monotonic_ns()
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model.eval()
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load_sec = (end_time - start_time) / 1e9
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synthesize_rtf = []
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for utterance in utterances:
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phoneme_ids = utterance["phoneme_ids"]
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speaker_id = utterance.get("speaker_id")
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synthesize_rtf.append(
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synthesize(
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model,
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phoneme_ids,
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speaker_id,
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sample_rate,
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)
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)
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json.dump(
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{"load_sec": load_sec, "synthesize_rtf": synthesize_rtf},
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sys.stdout,
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)
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def synthesize(model, phoneme_ids, speaker_id, sample_rate) -> float:
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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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sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None
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start_time = time.monotonic_ns()
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audio = (
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model(
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text,
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text_lengths,
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sid,
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torch.FloatTensor([_NOISE_SCALE]),
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torch.FloatTensor([_LENGTH_SCALE]),
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torch.FloatTensor([_NOISE_W]),
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)[0]
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.detach()
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.numpy()
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.squeeze()
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)
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end_time = time.monotonic_ns()
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audio_sec = (len(audio) / 2) / sample_rate
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infer_sec = (end_time - start_time) / 1e9
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return infer_sec / audio_sec
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if __name__ == "__main__":
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main()
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