mirror of https://github.com/rhasspy/piper
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
104 lines
2.5 KiB
Python
104 lines
2.5 KiB
Python
#!/usr/bin/env python3
|
|
import argparse
|
|
import logging
|
|
import json
|
|
import time
|
|
import statistics
|
|
import sys
|
|
|
|
import torch
|
|
|
|
_NOISE_SCALE = 0.667
|
|
_LENGTH_SCALE = 1.0
|
|
_NOISE_W = 0.8
|
|
|
|
_LOGGER = logging.getLogger(__name__)
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"-m", "--model", required=True, help="Path to Torchscript file (.ts)"
|
|
)
|
|
parser.add_argument("-c", "--config", help="Path to model config file (.json)")
|
|
args = parser.parse_args()
|
|
logging.basicConfig(level=logging.DEBUG)
|
|
|
|
if not args.config:
|
|
args.config = f"{args.model}.json"
|
|
|
|
with open(args.config, "r", encoding="utf-8") as config_file:
|
|
config = json.load(config_file)
|
|
|
|
sample_rate = config["audio"]["sample_rate"]
|
|
utterances = [json.loads(line) for line in sys.stdin]
|
|
|
|
start_time = time.monotonic_ns()
|
|
model = torch.jit.load(args.model)
|
|
end_time = time.monotonic_ns()
|
|
|
|
model.eval()
|
|
|
|
load_sec = (end_time - start_time) / 1e9
|
|
synthesize_rtf = []
|
|
for utterance in utterances:
|
|
phoneme_ids = utterance["phoneme_ids"]
|
|
speaker_id = utterance.get("speaker_id")
|
|
synthesize_rtf.append(
|
|
synthesize(
|
|
model,
|
|
phoneme_ids,
|
|
speaker_id,
|
|
sample_rate,
|
|
)
|
|
)
|
|
|
|
json.dump(
|
|
{
|
|
"load_sec": load_sec,
|
|
"rtf_mean": statistics.mean(synthesize_rtf),
|
|
"rtf_stdev": statistics.stdev(synthesize_rtf),
|
|
"synthesize_rtf": synthesize_rtf,
|
|
},
|
|
sys.stdout,
|
|
)
|
|
|
|
|
|
def synthesize(model, phoneme_ids, speaker_id, sample_rate) -> float:
|
|
text = torch.LongTensor(phoneme_ids).unsqueeze(0)
|
|
text_lengths = torch.LongTensor([len(phoneme_ids)])
|
|
sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None
|
|
|
|
start_time = time.monotonic_ns()
|
|
audio = (
|
|
model(
|
|
text,
|
|
text_lengths,
|
|
sid,
|
|
torch.FloatTensor([_NOISE_SCALE]),
|
|
torch.FloatTensor([_LENGTH_SCALE]),
|
|
torch.FloatTensor([_NOISE_W]),
|
|
)[0]
|
|
.detach()
|
|
.numpy()
|
|
.squeeze()
|
|
)
|
|
end_time = time.monotonic_ns()
|
|
|
|
audio_sec = len(audio) / sample_rate
|
|
infer_sec = (end_time - start_time) / 1e9
|
|
rtf = infer_sec / audio_sec
|
|
|
|
_LOGGER.debug(
|
|
"Real-time factor: %s (infer=%s sec, audio=%s sec)",
|
|
rtf,
|
|
infer_sec,
|
|
audio_sec,
|
|
)
|
|
|
|
return rtf
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|