mirror of
https://github.com/k2-fsa/icefall.git
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d89f4ea149
* use piper_phonemize as text tokenizer in ljspeech recipe * modify usage of tokenizer in vits/train.py * update docs
271 lines
7.1 KiB
Python
Executable File
271 lines
7.1 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2023 Xiaomi Corporation (Author: Zengwei Yao)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This script exports a VITS model from PyTorch to ONNX.
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Export the model to ONNX:
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./vits/export-onnx.py \
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--epoch 1000 \
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--exp-dir vits/exp \
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--tokens data/tokens.txt
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It will generate two files inside vits/exp:
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- vits-epoch-1000.onnx
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- vits-epoch-1000.int8.onnx (quantizated model)
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See ./test_onnx.py for how to use the exported ONNX models.
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"""
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import argparse
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import logging
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from pathlib import Path
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from typing import Dict, Tuple
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import onnx
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import torch
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import torch.nn as nn
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from onnxruntime.quantization import QuantType, quantize_dynamic
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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=1000,
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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=str,
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default="vits/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--tokens",
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type=str,
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default="data/tokens.txt",
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help="""Path to vocabulary.""",
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)
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return parser
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def add_meta_data(filename: str, meta_data: Dict[str, str]):
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"""Add meta data to an ONNX model. It is changed in-place.
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Args:
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filename:
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Filename of the ONNX model to be changed.
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meta_data:
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Key-value pairs.
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"""
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model = onnx.load(filename)
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for key, value in meta_data.items():
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meta = model.metadata_props.add()
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meta.key = key
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meta.value = value
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onnx.save(model, filename)
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class OnnxModel(nn.Module):
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"""A wrapper for VITS generator."""
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def __init__(self, model: nn.Module):
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"""
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Args:
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model:
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A VITS generator.
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frame_shift:
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The frame shift in samples.
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"""
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super().__init__()
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self.model = model
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def forward(
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self,
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tokens: torch.Tensor,
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tokens_lens: torch.Tensor,
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noise_scale: float = 0.667,
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alpha: float = 1.0,
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noise_scale_dur: float = 0.8,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Please see the help information of VITS.inference_batch
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Args:
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tokens:
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Input text token indexes (1, T_text)
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tokens_lens:
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Number of tokens of shape (1,)
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noise_scale (float):
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Noise scale parameter for flow.
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noise_scale_dur (float):
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Noise scale parameter for duration predictor.
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alpha (float):
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Alpha parameter to control the speed of generated speech.
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Returns:
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Return a tuple containing:
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- audio, generated wavform tensor, (B, T_wav)
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"""
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audio, _, _ = self.model.inference(
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text=tokens,
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text_lengths=tokens_lens,
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noise_scale=noise_scale,
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noise_scale_dur=noise_scale_dur,
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alpha=alpha,
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)
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return audio
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def export_model_onnx(
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model: nn.Module,
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model_filename: str,
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vocab_size: int,
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opset_version: int = 11,
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) -> None:
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"""Export the given generator model to ONNX format.
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The exported model has one input:
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- tokens, a tensor of shape (1, T_text); dtype is torch.int64
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and it has one output:
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- audio, a tensor of shape (1, T'); dtype is torch.float32
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Args:
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model:
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The VITS generator.
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model_filename:
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The filename to save the exported ONNX model.
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vocab_size:
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Number of tokens used in training.
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opset_version:
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The opset version to use.
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"""
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tokens = torch.randint(low=0, high=vocab_size, size=(1, 13), dtype=torch.int64)
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tokens_lens = torch.tensor([tokens.shape[1]], dtype=torch.int64)
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noise_scale = torch.tensor([1], dtype=torch.float32)
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noise_scale_dur = torch.tensor([1], dtype=torch.float32)
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alpha = torch.tensor([1], dtype=torch.float32)
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torch.onnx.export(
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model,
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(tokens, tokens_lens, noise_scale, alpha, noise_scale_dur),
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model_filename,
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verbose=False,
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opset_version=opset_version,
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input_names=[
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"tokens",
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"tokens_lens",
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"noise_scale",
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"alpha",
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"noise_scale_dur",
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],
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output_names=["audio"],
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dynamic_axes={
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"tokens": {0: "N", 1: "T"},
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"tokens_lens": {0: "N"},
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"audio": {0: "N", 1: "T"},
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},
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)
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meta_data = {
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"model_type": "VITS",
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"version": "1",
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"model_author": "k2-fsa",
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"comment": "VITS generator",
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}
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logging.info(f"meta_data: {meta_data}")
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add_meta_data(filename=model_filename, meta_data=meta_data)
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@torch.no_grad()
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def main():
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args = get_parser().parse_args()
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args.exp_dir = Path(args.exp_dir)
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params = get_params()
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params.update(vars(args))
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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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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 = model.generator
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model.to("cpu")
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model.eval()
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model = OnnxModel(model=model)
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num_param = sum([p.numel() for p in model.parameters()])
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logging.info(f"generator parameters: {num_param}")
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suffix = f"epoch-{params.epoch}"
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opset_version = 13
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logging.info("Exporting encoder")
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model_filename = params.exp_dir / f"vits-{suffix}.onnx"
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export_model_onnx(
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model,
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model_filename,
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params.vocab_size,
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opset_version=opset_version,
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)
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logging.info(f"Exported generator to {model_filename}")
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# Generate int8 quantization models
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# See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
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logging.info("Generate int8 quantization models")
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model_filename_int8 = params.exp_dir / f"vits-{suffix}.int8.onnx"
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quantize_dynamic(
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model_input=model_filename,
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model_output=model_filename_int8,
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weight_type=QuantType.QUInt8,
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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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main()
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