diff --git a/egs/wenetspeech4tts/TTS/README.md b/egs/wenetspeech4tts/TTS/README.md index f35bb51c7f..6b7509aca7 100644 --- a/egs/wenetspeech4tts/TTS/README.md +++ b/egs/wenetspeech4tts/TTS/README.md @@ -68,5 +68,54 @@ python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg}_top_p_${top_ --text-extractor pypinyin_initials_finals --top-p ${top_p} ``` +# [F5-TTS](https://arxiv.org/abs/2410.06885) + +./f5-tts contains the code for training F5-TTS model. + +Generated samples and training logs of wenetspeech basic 7k hours data can be found [here](https://huggingface.co/yuekai/f5-tts-small-wenetspeech4tts-basic/tensorboard). + +Preparation: + +``` +bash prepare.sh --stage 5 --stop_stage 6 +``` + +The training command is given below: + +``` +# docker: ghcr.io/swivid/f5-tts:main +# pip install k2==1.24.4.dev20241030+cuda12.4.torch2.4.0 -f https://k2-fsa.github.io/k2/cuda.html +# pip install kaldialign lhotse tensorboard bigvganinference sentencepiece + +world_size=8 +exp_dir=exp/f5-tts-small +python3 f5-tts/train.py --max-duration 700 --filter-min-duration 0.5 --filter-max-duration 20 \ + --num-buckets 6 --dtype "bfloat16" --save-every-n 5000 --valid-interval 10000 \ + --base-lr 7.5e-5 --warmup-steps 20000 --num-epochs 60 \ + --num-decoder-layers 18 --nhead 12 --decoder-dim 768 \ + --exp-dir ${exp_dir} --world-size ${world_size} +``` + +To inference, use: +``` +huggingface-cli login +huggingface-cli download --local-dir seed_tts_eval yuekai/seed_tts_eval --repo-type dataset +huggingface-cli download --local-dir ${exp_dir} yuekai/f5-tts-small-wenetspeech4tts-basic +huggingface-cli download nvidia/bigvgan_v2_24khz_100band_256x --local-dir bigvgan_v2_24khz_100band_256x + +manifest=./seed_tts_eval/seedtts_testset/zh/meta.lst +model_path=f5-tts-small-wenetspeech4tts-basic/epoch-56-avg-14.pt +# skip +python3 f5-tts/generate_averaged_model.py \ + --epoch 56 \ + --avg 14 --decoder-dim 768 --nhead 12 --num-decoder-layers 18 \ + --exp-dir exp/f5_small + + +accelerate launch f5-tts/infer.py --nfe 16 --model-path $model_path --manifest-file $manifest --output-dir $output_dir --decoder-dim 768 --nhead 12 --num-decoder-layers 18 +bash local/compute_wer.sh $output_dir $manifest +``` + # Credits -- [vall-e](https://github.com/lifeiteng/vall-e) +- [VALL-E](https://github.com/lifeiteng/vall-e) +- [F5-TTS](https://github.com/SWivid/F5-TTS) diff --git a/egs/wenetspeech4tts/TTS/f5-tts/README.md b/egs/wenetspeech4tts/TTS/f5-tts/README.md new file mode 100644 index 0000000000..e4a7e2a7ca --- /dev/null +++ b/egs/wenetspeech4tts/TTS/f5-tts/README.md @@ -0,0 +1,3 @@ +# Introduction +Files in this folder are copied from +https://github.com/SWivid/F5-TTS/tree/main/src/f5_tts/model diff --git a/egs/wenetspeech4tts/TTS/f5-tts/generate_averaged_model.py b/egs/wenetspeech4tts/TTS/f5-tts/generate_averaged_model.py new file mode 100644 index 0000000000..f023585535 --- /dev/null +++ b/egs/wenetspeech4tts/TTS/f5-tts/generate_averaged_model.py @@ -0,0 +1,173 @@ +#!/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")`. +""" + + +import argparse +from pathlib import Path + +import k2 +import torch +from train import add_model_arguments, get_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, +) +from icefall.utils import AttributeDict + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + 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.""", + ) + + 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. + """, + ) + + 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'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="The experiment dir", + ) + add_model_arguments(parser) + return parser + + +@torch.no_grad() +def main(): + parser = get_parser() + + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = AttributeDict() + params.update(vars(args)) + + if params.iter > 0: + params.suffix = f"checkpoint-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + print("Script started") + + device = torch.device("cpu") + print(f"Device: {device}") + + 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) + + 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)) + + filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt" + checkpoint["model"] = model.state_dict() + torch.save(checkpoint, filename) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + print("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/wenetspeech4tts/TTS/f5-tts/train.py b/egs/wenetspeech4tts/TTS/f5-tts/train.py index c1153360c2..37dcf531e5 100755 --- a/egs/wenetspeech4tts/TTS/f5-tts/train.py +++ b/egs/wenetspeech4tts/TTS/f5-tts/train.py @@ -23,7 +23,7 @@ # docker: ghcr.io/swivid/f5-tts:main # pip install k2==1.24.4.dev20241030+cuda12.4.torch2.4.0 -f https://k2-fsa.github.io/k2/cuda.html # pip install kaldialign lhotse tensorboard bigvganinference sentencepiece -# huggingface-cli download nvidia/bigvgan_v2_24khz_100band_256x --local-dir bigvgan_v2_24khz_100band_256x + world_size=8 exp_dir=exp/f5-tts-small python3 f5-tts/train.py --max-duration 700 --filter-min-duration 0.5 --filter-max-duration 20 \ diff --git a/egs/wenetspeech4tts/TTS/f5-tts/vocab.txt b/egs/wenetspeech4tts/TTS/f5-tts/vocab.txt new file mode 100644 index 0000000000..93f8b48b2c --- /dev/null +++ b/egs/wenetspeech4tts/TTS/f5-tts/vocab.txt @@ -0,0 +1,2545 @@ + +! +" +# +$ +% +& +' +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +: +; += +> +? +@ +A +B +C +D +E +F +G +H +I +J +K +L +M +N +O +P +Q +R +S +T +U +V +W +X +Y +Z +[ +\ +] +_ +a +a1 +ai1 +ai2 +ai3 +ai4 +an1 +an3 +an4 +ang1 +ang2 +ang4 +ao1 +ao2 +ao3 +ao4 +b +ba +ba1 +ba2 +ba3 +ba4 +bai1 +bai2 +bai3 +bai4 +ban1 +ban2 +ban3 +ban4 +bang1 +bang2 +bang3 +bang4 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