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https://github.com/k2-fsa/icefall.git
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minor adjustment
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zr_jin
parent
fb34991566
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2
.github/scripts/ljspeech/TTS/run-matcha.sh
vendored
2
.github/scripts/ljspeech/TTS/run-matcha.sh
vendored
@ -56,7 +56,7 @@ function infer() {
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
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./matcha/synth.py \
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./matcha/infer.py \
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--epoch 1 \
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--exp-dir ./matcha/exp \
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--tokens data/tokens.txt \
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@ -65,6 +65,28 @@ def get_parser():
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help="""Path to vocabulary.""",
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)
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# The following arguments are used for inference on single text
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parser.add_argument(
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"--input-text",
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type=str,
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required=False,
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help="The text to generate speech for",
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)
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parser.add_argument(
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"--output-wav",
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type=str,
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required=False,
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help="The filename of the wave to save the generated speech",
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)
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parser.add_argument(
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"--sampling-rate",
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type=int,
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default=22050,
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help="The sampling rate of the generated speech (default: 22050 for LJSpeech)",
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)
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return parser
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@ -103,7 +125,7 @@ def process_text(text: str, tokenizer: Tokenizer, device: str = "cpu") -> dict:
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return {"x_orig": text, "x": x, "x_lengths": x_lengths}
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def synthesise(
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def synthesize(
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model: nn.Module,
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tokenizer: Tokenizer,
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n_timesteps: int,
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@ -169,7 +191,7 @@ def infer_dataset(
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cut_ids = [cut.id for cut in batch["cut"]]
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for i in range(batch_size):
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output = synthesise(
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output = synthesize(
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model=model,
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tokenizer=tokenizer,
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n_timesteps=params.n_timesteps,
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@ -271,15 +293,35 @@ def main():
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denoiser = Denoiser(vocoder, mode="zeros")
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denoiser.to(device)
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infer_dataset(
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dl=test_dl,
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params=params,
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model=model,
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vocoder=vocoder,
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denoiser=denoiser,
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tokenizer=tokenizer,
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)
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if params.input_text is not None and params.output_wav is not None:
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logging.info("Synthesizing a single text")
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output = synthesize(
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model=model,
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tokenizer=tokenizer,
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n_timesteps=params.n_timesteps,
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text=params.input_text,
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length_scale=params.length_scale,
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temperature=params.temperature,
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device=device,
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)
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output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
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sf.write(
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file=params.output_wav,
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data=output["waveform"],
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samplerate=params.sampling_rate,
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subtype="PCM_16",
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)
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else:
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logging.info("Decoding the test set")
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infer_dataset(
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dl=test_dl,
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params=params,
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model=model,
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vocoder=vocoder,
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denoiser=denoiser,
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tokenizer=tokenizer,
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)
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if __name__ == "__main__":
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main()
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@ -1,166 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
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import argparse
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import json
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import logging
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from pathlib import Path
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import soundfile as sf
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import torch
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from hifigan.denoiser import Denoiser
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from infer import load_vocoder, synthesise, to_waveform
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from tokenizer import Tokenizer
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from train import get_model, get_params
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from icefall.checkpoint import load_checkpoint
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=4000,
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help="""It specifies the checkpoint to use for decoding.
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Note: Epoch counts from 1.
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""",
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)
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parser.add_argument(
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"--exp-dir",
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type=Path,
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default="matcha/exp",
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help="""The experiment dir.
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It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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""",
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)
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parser.add_argument(
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"--vocoder",
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type=Path,
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default="./generator_v1",
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help="Path to the vocoder",
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)
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parser.add_argument(
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"--tokens",
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type=Path,
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default="data/tokens.txt",
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)
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parser.add_argument(
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"--cmvn",
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type=str,
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default="data/fbank/cmvn.json",
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help="""Path to vocabulary.""",
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)
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parser.add_argument(
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"--input-text",
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type=str,
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required=True,
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help="The text to generate speech for",
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)
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parser.add_argument(
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"--output-wav",
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type=str,
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required=True,
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help="The filename of the wave to save the generated speech",
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)
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parser.add_argument(
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"--sampling-rate",
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type=int,
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default=22050,
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help="The sampling rate of the generated speech (default: 22050 for LJSpeech)",
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)
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return parser
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@torch.inference_mode()
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def main():
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parser = get_parser()
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args = parser.parse_args()
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params = get_params()
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params.update(vars(args))
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logging.info("Infer started")
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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tokenizer = Tokenizer(params.tokens)
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params.blank_id = tokenizer.pad_id
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params.vocab_size = tokenizer.vocab_size
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params.model_args.n_vocab = params.vocab_size
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with open(params.cmvn) as f:
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stats = json.load(f)
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params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
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params.data_args.data_statistics.mel_std = stats["fbank_std"]
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params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
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params.model_args.data_statistics.mel_std = stats["fbank_std"]
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# Number of ODE Solver steps
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params.n_timesteps = 2
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# Changes to the speaking rate
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params.length_scale = 1.0
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# Sampling temperature
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params.temperature = 0.667
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logging.info(params)
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logging.info("About to create model")
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model = get_model(params)
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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model.to(device)
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model.eval()
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if not Path(params.vocoder).is_file():
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raise ValueError(f"{params.vocoder} does not exist")
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vocoder = load_vocoder(params.vocoder)
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vocoder.to(device)
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denoiser = Denoiser(vocoder, mode="zeros")
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denoiser.to(device)
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output = synthesise(
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model=model,
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tokenizer=tokenizer,
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n_timesteps=params.n_timesteps,
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text=params.input_text,
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length_scale=params.length_scale,
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temperature=params.temperature,
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device=device,
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)
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output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
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sf.write(
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file=params.output_wav,
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data=output["waveform"],
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samplerate=params.sampling_rate,
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subtype="PCM_16",
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)
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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main()
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