mirror of
https://github.com/k2-fsa/icefall.git
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417 lines
16 KiB
Python
Executable File
417 lines
16 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 = str(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": "icefall", # must be icefall for models from icefall
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"language": "English",
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"voice": "en-us", # Choose your language appropriately
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"has_espeak": 1,
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"n_speakers": 1,
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"sample_rate": 22050, # Must match the real sample rate
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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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"""
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Supported languages.
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LJSpeech is using "en-us" from the second column.
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Pty Language Age/Gender VoiceName File Other Languages
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5 af --/M Afrikaans gmw/af
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5 am --/M Amharic sem/am
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5 an --/M Aragonese roa/an
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5 ar --/M Arabic sem/ar
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5 as --/M Assamese inc/as
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5 az --/M Azerbaijani trk/az
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5 ba --/M Bashkir trk/ba
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5 be --/M Belarusian zle/be
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5 bg --/M Bulgarian zls/bg
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5 bn --/M Bengali inc/bn
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5 bpy --/M Bishnupriya_Manipuri inc/bpy
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5 bs --/M Bosnian zls/bs
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5 ca --/M Catalan roa/ca
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5 chr-US-Qaaa-x-west --/M Cherokee_ iro/chr
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5 cmn --/M Chinese_(Mandarin,_latin_as_English) sit/cmn (zh-cmn 5)(zh 5)
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5 cmn-latn-pinyin --/M Chinese_(Mandarin,_latin_as_Pinyin) sit/cmn-Latn-pinyin (zh-cmn 5)(zh 5)
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5 cs --/M Czech zlw/cs
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5 cv --/M Chuvash trk/cv
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5 cy --/M Welsh cel/cy
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5 da --/M Danish gmq/da
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5 de --/M German gmw/de
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5 el --/M Greek grk/el
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5 en-029 --/M English_(Caribbean) gmw/en-029 (en 10)
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2 en-gb --/M English_(Great_Britain) gmw/en (en 2)
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5 en-gb-scotland --/M English_(Scotland) gmw/en-GB-scotland (en 4)
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5 en-gb-x-gbclan --/M English_(Lancaster) gmw/en-GB-x-gbclan (en-gb 3)(en 5)
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5 en-gb-x-gbcwmd --/M English_(West_Midlands) gmw/en-GB-x-gbcwmd (en-gb 9)(en 9)
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5 en-gb-x-rp --/M English_(Received_Pronunciation) gmw/en-GB-x-rp (en-gb 4)(en 5)
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2 en-us --/M English_(America) gmw/en-US (en 3)
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5 en-us-nyc --/M English_(America,_New_York_City) gmw/en-US-nyc
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5 eo --/M Esperanto art/eo
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5 es --/M Spanish_(Spain) roa/es
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5 es-419 --/M Spanish_(Latin_America) roa/es-419 (es-mx 6)
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5 et --/M Estonian urj/et
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5 eu --/M Basque eu
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5 fa --/M Persian ira/fa
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5 fa-latn --/M Persian_(Pinglish) ira/fa-Latn
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5 fi --/M Finnish urj/fi
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5 fr-be --/M French_(Belgium) roa/fr-BE (fr 8)
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5 fr-ch --/M French_(Switzerland) roa/fr-CH (fr 8)
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5 fr-fr --/M French_(France) roa/fr (fr 5)
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5 ga --/M Gaelic_(Irish) cel/ga
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5 gd --/M Gaelic_(Scottish) cel/gd
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5 gn --/M Guarani sai/gn
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5 grc --/M Greek_(Ancient) grk/grc
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5 gu --/M Gujarati inc/gu
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5 hak --/M Hakka_Chinese sit/hak
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5 haw --/M Hawaiian map/haw
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5 he --/M Hebrew sem/he
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5 hi --/M Hindi inc/hi
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5 hr --/M Croatian zls/hr (hbs 5)
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5 ht --/M Haitian_Creole roa/ht
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5 hu --/M Hungarian urj/hu
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5 hy --/M Armenian_(East_Armenia) ine/hy (hy-arevela 5)
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5 hyw --/M Armenian_(West_Armenia) ine/hyw (hy-arevmda 5)(hy 8)
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5 ia --/M Interlingua art/ia
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5 id --/M Indonesian poz/id
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5 io --/M Ido art/io
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5 is --/M Icelandic gmq/is
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5 it --/M Italian roa/it
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5 ja --/M Japanese jpx/ja
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5 jbo --/M Lojban art/jbo
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5 ka --/M Georgian ccs/ka
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5 kk --/M Kazakh trk/kk
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5 kl --/M Greenlandic esx/kl
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5 kn --/M Kannada dra/kn
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5 ko --/M Korean ko
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5 kok --/M Konkani inc/kok
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5 ku --/M Kurdish ira/ku
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5 ky --/M Kyrgyz trk/ky
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5 la --/M Latin itc/la
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5 lb --/M Luxembourgish gmw/lb
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5 lfn --/M Lingua_Franca_Nova art/lfn
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5 lt --/M Lithuanian bat/lt
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5 ltg --/M Latgalian bat/ltg
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5 lv --/M Latvian bat/lv
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5 mi --/M Māori poz/mi
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5 mk --/M Macedonian zls/mk
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5 ml --/M Malayalam dra/ml
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5 mr --/M Marathi inc/mr
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5 ms --/M Malay poz/ms
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5 mt --/M Maltese sem/mt
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5 mto --/M Totontepec_Mixe miz/mto
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5 my --/M Myanmar_(Burmese) sit/my
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5 nb --/M Norwegian_Bokmål gmq/nb (no 5)
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5 nci --/M Nahuatl_(Classical) azc/nci
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5 ne --/M Nepali inc/ne
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5 nl --/M Dutch gmw/nl
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5 nog --/M Nogai trk/nog
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5 om --/M Oromo cus/om
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5 or --/M Oriya inc/or
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5 pa --/M Punjabi inc/pa
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5 pap --/M Papiamento roa/pap
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5 piqd --/M Klingon art/piqd
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5 pl --/M Polish zlw/pl
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5 pt --/M Portuguese_(Portugal) roa/pt (pt-pt 5)
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5 pt-br --/M Portuguese_(Brazil) roa/pt-BR (pt 6)
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5 py --/M Pyash art/py
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5 qdb --/M Lang_Belta art/qdb
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5 qu --/M Quechua qu
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5 quc --/M K'iche' myn/quc
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5 qya --/M Quenya art/qya
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5 ro --/M Romanian roa/ro
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5 ru --/M Russian zle/ru
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5 ru-cl --/M Russian_(Classic) zle/ru-cl
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2 ru-lv --/M Russian_(Latvia) zle/ru-LV
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5 sd --/M Sindhi inc/sd
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5 shn --/M Shan_(Tai_Yai) tai/shn
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5 si --/M Sinhala inc/si
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5 sjn --/M Sindarin art/sjn
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5 sk --/M Slovak zlw/sk
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5 sl --/M Slovenian zls/sl
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5 smj --/M Lule_Saami urj/smj
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5 sq --/M Albanian ine/sq
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5 sr --/M Serbian zls/sr
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5 sv --/M Swedish gmq/sv
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5 sw --/M Swahili bnt/sw
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5 ta --/M Tamil dra/ta
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5 te --/M Telugu dra/te
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5 th --/M Thai tai/th
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5 tk --/M Turkmen trk/tk
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5 tn --/M Setswana bnt/tn
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5 tr --/M Turkish trk/tr
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5 tt --/M Tatar trk/tt
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5 ug --/M Uyghur trk/ug
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5 uk --/M Ukrainian zle/uk
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5 ur --/M Urdu inc/ur
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5 uz --/M Uzbek trk/uz
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5 vi --/M Vietnamese_(Northern) aav/vi
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5 vi-vn-x-central --/M Vietnamese_(Central) aav/vi-VN-x-central
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5 vi-vn-x-south --/M Vietnamese_(Southern) aav/vi-VN-x-south
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5 yue --/M Chinese_(Cantonese) sit/yue (zh-yue 5)(zh 8)
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5 yue --/M Chinese_(Cantonese,_latin_as_Jyutping) sit/yue-Latn-jyutping (zh-yue 5)(zh 8)
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"""
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