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| ../../../icefall/shared/ | ||
| ../../../icefall/shared |
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egs/wenetspeech4tts/TTS/f5-tts/generate_averaged_model.py
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| #!/usr/bin/env python3 | ||
| # | ||
| # Copyright 2021-2022 Xiaomi Corporation (Author: Yifan Yang) | ||
| # Copyright 2024 Yuekai Zhang | ||
| # | ||
| # See ../../../../LICENSE for clarification regarding multiple authors | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| """ | ||
| Usage: | ||
| (1) use the checkpoint exp_dir/epoch-xxx.pt | ||
| python3 bin/generate_averaged_model.py \ | ||
| --epoch 40 \ | ||
| --avg 5 \ | ||
| --exp-dir ${exp_dir} | ||
| It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`. | ||
| You can later load it by `torch.load("epoch-28-avg-15.pt")`. | ||
| """ | ||
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| import argparse | ||
| from pathlib import Path | ||
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| import k2 | ||
| import torch | ||
| from train import add_model_arguments, get_model | ||
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| from icefall.checkpoint import ( | ||
| average_checkpoints, | ||
| average_checkpoints_with_averaged_model, | ||
| find_checkpoints, | ||
| ) | ||
| from icefall.utils import AttributeDict | ||
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| def get_parser(): | ||
| parser = argparse.ArgumentParser( | ||
| formatter_class=argparse.ArgumentDefaultsHelpFormatter | ||
| ) | ||
|
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| parser.add_argument( | ||
| "--epoch", | ||
| type=int, | ||
| default=30, | ||
| help="""It specifies the checkpoint to use for decoding. | ||
| Note: Epoch counts from 1. | ||
| You can specify --avg to use more checkpoints for model averaging.""", | ||
| ) | ||
|
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| parser.add_argument( | ||
| "--iter", | ||
| type=int, | ||
| default=0, | ||
| help="""If positive, --epoch is ignored and it | ||
| will use the checkpoint exp_dir/checkpoint-iter.pt. | ||
| You can specify --avg to use more checkpoints for model averaging. | ||
| """, | ||
| ) | ||
|
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| parser.add_argument( | ||
| "--avg", | ||
| type=int, | ||
| default=9, | ||
| help="Number of checkpoints to average. Automatically select " | ||
| "consecutive checkpoints before the checkpoint specified by " | ||
| "'--epoch' and '--iter'", | ||
| ) | ||
|
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| parser.add_argument( | ||
| "--exp-dir", | ||
| type=str, | ||
| default="zipformer/exp", | ||
| help="The experiment dir", | ||
| ) | ||
| add_model_arguments(parser) | ||
| return parser | ||
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| @torch.no_grad() | ||
| def main(): | ||
| parser = get_parser() | ||
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| args = parser.parse_args() | ||
| args.exp_dir = Path(args.exp_dir) | ||
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| params = AttributeDict() | ||
| params.update(vars(args)) | ||
|
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| if params.iter > 0: | ||
| params.suffix = f"checkpoint-{params.iter}-avg-{params.avg}" | ||
| else: | ||
| params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" | ||
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| print("Script started") | ||
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| device = torch.device("cpu") | ||
| print(f"Device: {device}") | ||
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| print("About to create model") | ||
| filename = f"{params.exp_dir}/epoch-{params.epoch}.pt" | ||
| checkpoint = torch.load(filename, map_location=device) | ||
| args = AttributeDict(checkpoint) | ||
| model = get_model(args) | ||
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| if params.iter > 0: | ||
| # TODO FIX ME | ||
| filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ | ||
| : params.avg + 1 | ||
| ] | ||
| if len(filenames) == 0: | ||
| raise ValueError( | ||
| f"No checkpoints found for --iter {params.iter}, --avg {params.avg}" | ||
| ) | ||
| elif len(filenames) < params.avg + 1: | ||
| raise ValueError( | ||
| f"Not enough checkpoints ({len(filenames)}) found for" | ||
| f" --iter {params.iter}, --avg {params.avg}" | ||
| ) | ||
| filename_start = filenames[-1] | ||
| filename_end = filenames[0] | ||
| print( | ||
| "Calculating the averaged model over iteration checkpoints" | ||
| f" from {filename_start} (excluded) to {filename_end}" | ||
| ) | ||
| model.to(device) | ||
| model.load_state_dict( | ||
| average_checkpoints_with_averaged_model( | ||
| filename_start=filename_start, | ||
| filename_end=filename_end, | ||
| device=device, | ||
| ) | ||
| ) | ||
| filename = params.exp_dir / f"checkpoint-{params.iter}-avg-{params.avg}.pt" | ||
| torch.save({"model": model.state_dict()}, filename) | ||
| else: | ||
| assert params.avg > 0, params.avg | ||
| start = params.epoch - params.avg | ||
| assert start >= 1, start | ||
| filename_start = f"{params.exp_dir}/epoch-{start}.pt" | ||
| filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" | ||
| print( | ||
| f"Calculating the averaged model over epoch range from " | ||
| f"{start} (excluded) to {params.epoch}" | ||
| ) | ||
| filenames = [ | ||
| f"{params.exp_dir}/epoch-{i}.pt" for i in range(start, params.epoch + 1) | ||
| ] | ||
| model.to(device) | ||
| model.load_state_dict(average_checkpoints(filenames, device=device)) | ||
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| filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt" | ||
| checkpoint["model"] = model.state_dict() | ||
| torch.save(checkpoint, filename) | ||
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| num_param = sum([p.numel() for p in model.parameters()]) | ||
| print(f"Number of model parameters: {num_param}") | ||
|
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| print("Done!") | ||
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| if __name__ == "__main__": | ||
| main() |
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