mirror of
https://github.com/k2-fsa/icefall.git
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139 lines
4.1 KiB
Python
Executable File
139 lines
4.1 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 is used to test the exported onnx model by vits/export-onnx.py
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Use the onnx model to generate a wav:
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./vits/test_onnx.py \
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--model-filename vits/exp/vits-epoch-1000.onnx \
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--tokens data/tokens.txt
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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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import onnxruntime as ort
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import torch
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import torchaudio
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from tokenizer import Tokenizer
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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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"--model-filename",
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type=str,
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required=True,
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help="Path to the onnx model.",
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)
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parser.add_argument(
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"--speakers",
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type=Path,
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default=Path("data/speakers.txt"),
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help="Path to speakers.txt file.",
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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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class OnnxModel:
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def __init__(self, model_filename: str):
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session_opts = ort.SessionOptions()
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session_opts.inter_op_num_threads = 1
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session_opts.intra_op_num_threads = 4
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self.session_opts = session_opts
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self.model = ort.InferenceSession(
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model_filename,
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sess_options=self.session_opts,
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providers=["CPUExecutionProvider"],
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)
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logging.info(f"{self.model.get_modelmeta().custom_metadata_map}")
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def __call__(
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self, tokens: torch.Tensor, tokens_lens: torch.Tensor, speaker: torch.Tensor
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) -> torch.Tensor:
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"""
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Args:
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tokens:
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A 1-D tensor of shape (1, T)
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Returns:
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A tensor of shape (1, T')
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"""
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noise_scale = torch.tensor([0.667], dtype=torch.float32)
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noise_scale_dur = torch.tensor([0.8], dtype=torch.float32)
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alpha = torch.tensor([1.0], dtype=torch.float32)
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out = self.model.run(
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[
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self.model.get_outputs()[0].name,
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],
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{
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self.model.get_inputs()[0].name: tokens.numpy(),
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self.model.get_inputs()[1].name: tokens_lens.numpy(),
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self.model.get_inputs()[2].name: noise_scale.numpy(),
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self.model.get_inputs()[3].name: alpha.numpy(),
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self.model.get_inputs()[4].name: noise_scale_dur.numpy(),
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self.model.get_inputs()[5].name: speaker.numpy(),
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},
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)[0]
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return torch.from_numpy(out)
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def main():
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args = get_parser().parse_args()
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tokenizer = Tokenizer(args.tokens)
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with open(args.speakers) as f:
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speaker_map = {line.strip(): i for i, line in enumerate(f)}
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args.num_spks = len(speaker_map)
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logging.info("About to create onnx model")
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model = OnnxModel(args.model_filename)
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text = "I went there to see the land, the people and how their system works, end quote."
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tokens = tokenizer.texts_to_token_ids([text])
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tokens = torch.tensor(tokens) # (1, T)
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tokens_lens = torch.tensor([tokens.shape[1]], dtype=torch.int64) # (1, T)
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speaker = torch.tensor([1], dtype=torch.int64) # (1, )
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audio = model(tokens, tokens_lens, speaker) # (1, T')
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torchaudio.save(str("test_onnx.wav"), audio, sample_rate=22050)
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logging.info("Saved to test_onnx.wav")
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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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