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code clean
This commit is contained in:
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parent
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@ -11,7 +11,7 @@ repos:
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rev: 5.0.4
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hooks:
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- id: flake8
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args: ["--max-line-length=88", "--extend-ignore=E203,E266,E501,F401,E402,F403,F841,W503, F722, F821"]
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args: ["--max-line-length=88", "--extend-ignore=E203,E266,E501,F401,E402,F403,F841,W503"]
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#exclude:
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# What are we ignoring here?
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@ -1,3 +1,19 @@
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#!/usr/bin/env python3
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# Modified from https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/eval/eval_infer_batch.py
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"""
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Usage:
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# docker: ghcr.io/swivid/f5-tts:main
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# pip install k2==1.24.4.dev20241030+cuda12.4.torch2.4.0 -f https://k2-fsa.github.io/k2/cuda.html
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# pip install kaldialign lhotse tensorboard bigvganinference sentencepiece sherpa-onnx
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# huggingface-cli download nvidia/bigvgan_v2_24khz_100band_256x --local-dir bigvgan_v2_24khz_100band_256x
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manifest=/path/seed_tts_eval/seedtts_testset/zh/meta.lst
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python3 f5-tts/generate_averaged_model.py \
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--epoch 56 \
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--avg 14 --decoder-dim 768 --nhead 12 --num-decoder-layers 18 \
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--exp-dir exp/f5_small
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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
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bash local/compute_wer.sh $output_dir $manifest
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"""
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import argparse
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import logging
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import math
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@ -62,7 +78,7 @@ def get_parser():
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parser.add_argument(
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"--manifest-file",
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type=str,
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default="/home/yuekaiz/seed_tts_eval/seedtts_testset/zh/meta_head.lst",
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default="/path/seed_tts_eval/seedtts_testset/zh/meta.lst",
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help="The manifest file in seed_tts_eval format",
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)
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@ -180,7 +196,6 @@ def get_inference_prompt(
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batch_accum[bucket_i] += total_mel_len
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if batch_accum[bucket_i] >= infer_batch_size:
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# print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
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prompts_all.append(
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(
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utts[bucket_i],
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@ -282,7 +297,7 @@ def main():
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model = get_model(args).eval().to(device)
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checkpoint = torch.load(args.model_path, map_location="cpu")
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if "ema_model_state_dict" in checkpoint or 'model_state_dict' in checkpoint:
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if "ema_model_state_dict" in checkpoint or "model_state_dict" in checkpoint:
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model = load_F5_TTS_pretrained_checkpoint(model, args.model_path)
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else:
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_ = load_checkpoint(
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@ -1 +0,0 @@
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../../../librispeech/ASR/zipformer/optim.py
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@ -20,8 +20,17 @@
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# limitations under the License.
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"""
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Usage:
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# docker: ghcr.io/swivid/f5-tts:main
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# pip install k2==1.24.4.dev20241030+cuda12.4.torch2.4.0 -f https://k2-fsa.github.io/k2/cuda.html
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# pip install kaldialign lhotse tensorboard bigvganinference sentencepiece
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# huggingface-cli download nvidia/bigvgan_v2_24khz_100band_256x --local-dir bigvgan_v2_24khz_100band_256x
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world_size=8
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exp_dir=exp/ft-tts
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exp_dir=exp/f5-tts-small
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python3 f5-tts/train.py --max-duration 700 --filter-min-duration 0.5 --filter-max-duration 20 \
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--num-buckets 6 --dtype "bfloat16" --save-every-n 5000 --valid-interval 10000 \
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--base-lr 7.5e-5 --warmup-steps 20000 --num-epochs 60 \
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--num-decoder-layers 18 --nhead 12 --decoder-dim 768 \
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--exp-dir ${exp_dir} --world-size ${world_size}
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"""
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import argparse
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@ -45,13 +54,10 @@ from lhotse.utils import fix_random_seed
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from model.cfm import CFM
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from model.dit import DiT
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from model.utils import convert_char_to_pinyin
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from optim import Eden, ScaledAdam
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from torch.optim.lr_scheduler import LinearLR, SequentialLR
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from torch import Tensor
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# from torch.cuda.amp import GradScaler
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from torch.amp import GradScaler
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim.lr_scheduler import LinearLR, SequentialLR
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from torch.utils.tensorboard import SummaryWriter
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from tts_datamodule import TtsDataModule
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from utils import MetricsTracker
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@ -87,12 +93,14 @@ def add_model_arguments(parser: argparse.ArgumentParser):
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default=1024,
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help="Embedding dimension in the decoder model.",
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)
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parser.add_argument(
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"--nhead",
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type=int,
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default=16,
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help="Number of attention heads in the Decoder layers.",
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)
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parser.add_argument(
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"--num-decoder-layers",
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type=int,
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@ -156,7 +164,7 @@ def get_parser():
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parser.add_argument(
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"--exp-dir",
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type=Path,
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default="exp/valle_dev",
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default="exp/f5",
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help="""The experiment dir.
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It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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@ -169,7 +177,7 @@ def get_parser():
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default="f5-tts/vocab.txt",
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help="Path to the unique text tokens file",
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)
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# /home/yuekaiz//HF/F5-TTS/F5TTS_Base_bigvgan/model_1250000.pt
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parser.add_argument(
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"--pretrained-model-path",
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type=str,
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@ -180,15 +188,9 @@ def get_parser():
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parser.add_argument(
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"--optimizer-name",
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type=str,
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default="ScaledAdam",
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default="AdamW",
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help="The optimizer.",
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)
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parser.add_argument(
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"--scheduler-name",
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type=str,
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default="Eden",
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help="The scheduler.",
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)
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parser.add_argument(
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"--base-lr", type=float, default=0.05, help="The base learning rate."
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)
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@ -203,7 +205,7 @@ def get_parser():
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parser.add_argument(
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"--decay-steps",
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type=int,
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default=None,
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default=1000000,
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help="""Number of steps that affects how rapidly the learning rate
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decreases. We suggest not to change this.""",
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)
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@ -286,6 +288,7 @@ def get_parser():
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default=0.0,
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help="Keep only utterances with duration > this.",
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)
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parser.add_argument(
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"--filter-max-duration",
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type=float,
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@ -293,13 +296,6 @@ def get_parser():
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help="Keep only utterances with duration < this.",
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)
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parser.add_argument(
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"--visualize",
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type=str2bool,
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default=False,
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help="visualize model results in eval step.",
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)
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parser.add_argument(
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"--oom-check",
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type=str2bool,
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@ -383,6 +379,7 @@ def get_tokenizer(vocab_file_path: str):
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def get_model(params):
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vocab_char_map, vocab_size = get_tokenizer(params.tokens)
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# bigvgan 100 dim features
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n_mel_channels = 100
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n_fft = 1024
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sampling_rate = 24_000
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@ -421,7 +418,6 @@ def get_model(params):
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def load_F5_TTS_pretrained_checkpoint(
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model, ckpt_path, device: str = "cpu", dtype=torch.float32
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):
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# model = model.to(dtype)
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checkpoint = torch.load(ckpt_path, map_location=device, weights_only=True)
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if "ema_model_state_dict" in checkpoint:
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checkpoint["model_state_dict"] = {
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@ -641,14 +637,6 @@ def compute_validation_loss(
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params.best_valid_epoch = params.cur_epoch
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params.best_valid_loss = loss_value
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# if params.visualize:
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# output_dir = Path(f"{params.exp_dir}/eval/step-{params.batch_idx_train:06d}")
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# output_dir.mkdir(parents=True, exist_ok=True)
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# if isinstance(model, DDP):
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# model.module.visualize(predicts, batch, output_dir=output_dir)
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# else:
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# model.visualize(predicts, batch, output_dir=output_dir)
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return tot_loss
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@ -744,11 +732,11 @@ def train_one_epoch(
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scaler.scale(loss).backward()
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if params.batch_idx_train >= params.accumulate_grad_steps:
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if params.batch_idx_train % params.accumulate_grad_steps == 0:
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if params.optimizer_name not in ["ScaledAdam", "Eve"]:
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# Unscales the gradients of optimizer's assigned params in-place
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scaler.unscale_(optimizer)
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# Since the gradients of optimizer's assigned params are unscaled, clips as usual:
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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# Unscales the gradients of optimizer's assigned params in-place
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scaler.unscale_(optimizer)
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# Since the gradients of optimizer's assigned params are unscaled, clips as usual:
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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scaler.step(optimizer)
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scaler.update()
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@ -757,10 +745,7 @@ def train_one_epoch(
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# optimizer.step()
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for k in range(params.accumulate_grad_steps):
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if isinstance(scheduler, Eden):
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scheduler.step_batch(params.batch_idx_train)
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else:
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scheduler.step()
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scheduler.step()
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set_batch_count(model, params.batch_idx_train)
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except: # noqa
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@ -940,16 +925,18 @@ def run(rank, world_size, args):
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logging.info(f"Device: {device}")
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tokenizer = get_tokenizer(params.tokens)
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print("the class type of tokenizer is: ", type(tokenizer))
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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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if params.pretrained_model_path:
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checkpoint = torch.load(params.pretrained_model_path, map_location="cpu")
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if "ema_model_state_dict" in checkpoint or 'model_state_dict' in checkpoint:
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model = load_F5_TTS_pretrained_checkpoint(model, params.pretrained_model_path)
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if "ema_model_state_dict" in checkpoint or "model_state_dict" in checkpoint:
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model = load_F5_TTS_pretrained_checkpoint(
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model, params.pretrained_model_path
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)
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else:
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_ = load_checkpoint(
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params.pretrained_model_path,
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@ -984,27 +971,24 @@ def run(rank, world_size, args):
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model_parameters = model.parameters()
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if params.optimizer_name == "ScaledAdam":
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optimizer = ScaledAdam(
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model_parameters,
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lr=params.base_lr,
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clipping_scale=2.0,
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)
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elif params.optimizer_name == "AdamW":
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optimizer = torch.optim.AdamW(
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model_parameters,
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lr=params.base_lr,
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betas=(0.9, 0.95),
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weight_decay=1e-2,
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eps=1e-8,
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)
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else:
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raise NotImplementedError()
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optimizer = torch.optim.AdamW(
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model_parameters,
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lr=params.base_lr,
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betas=(0.9, 0.95),
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weight_decay=1e-2,
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eps=1e-8,
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)
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warmup_scheduler = LinearLR(optimizer, start_factor=1e-8, end_factor=1.0, total_iters=params.warmup_steps)
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decay_scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=1e-8, total_iters=params.decay_steps)
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warmup_scheduler = LinearLR(
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optimizer, start_factor=1e-8, end_factor=1.0, total_iters=params.warmup_steps
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)
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decay_scheduler = LinearLR(
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optimizer, start_factor=1.0, end_factor=1e-8, total_iters=params.decay_steps
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)
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scheduler = SequentialLR(
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optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[params.warmup_steps]
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optimizer,
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schedulers=[warmup_scheduler, decay_scheduler],
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milestones=[params.warmup_steps],
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)
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optimizer.zero_grad()
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@ -1062,8 +1046,6 @@ def run(rank, world_size, args):
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scaler.load_state_dict(checkpoints["grad_scaler"])
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for epoch in range(params.start_epoch, params.num_epochs + 1):
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if isinstance(scheduler, Eden):
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scheduler.step_epoch(epoch - 1)
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fix_random_seed(params.seed + epoch - 1)
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train_dl.sampler.set_epoch(epoch - 1)
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@ -1140,7 +1122,6 @@ def scan_pessimistic_batches_for_oom(
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"Sanity check -- see if any of the batches in epoch 1 would cause OOM."
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)
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batches, crit_values = find_pessimistic_batches(train_dl.sampler)
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print(23333)
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dtype = torch.float32
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if params.dtype in ["bfloat16", "bf16"]:
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dtype = torch.bfloat16
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@ -24,8 +24,6 @@ from pathlib import Path
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from typing import Any, Dict, Optional
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import torch
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# from fbank import MatchaFbank, MatchaFbankConfig
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from lhotse import CutSet, load_manifest_lazy
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from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures; SpeechSynthesisDataset,
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CutConcatenate,
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@ -185,22 +183,6 @@ class TtsDataModule:
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raise NotImplementedError(
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"On-the-fly feature extraction is not implemented yet."
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)
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# sampling_rate = 22050
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# config = MatchaFbankConfig(
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# n_fft=1024,
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# n_mels=80,
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# sampling_rate=sampling_rate,
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# hop_length=256,
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# win_length=1024,
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# f_min=0,
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# f_max=8000,
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# )
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# train = SpeechSynthesisDataset(
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# return_text=True,
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# return_tokens=False,
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# feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
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# return_cuts=self.args.return_cuts,
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# )
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if self.args.bucketing_sampler:
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logging.info("Using DynamicBucketingSampler.")
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@ -249,22 +231,6 @@ class TtsDataModule:
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raise NotImplementedError(
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"On-the-fly feature extraction is not implemented yet."
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)
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# sampling_rate = 22050
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# config = MatchaFbankConfig(
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# n_fft=1024,
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# n_mels=80,
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# sampling_rate=sampling_rate,
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# hop_length=256,
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# win_length=1024,
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# f_min=0,
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# f_max=8000,
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# )
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# validate = SpeechSynthesisDataset(
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# return_text=True,
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# return_tokens=False,
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# feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
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# return_cuts=self.args.return_cuts,
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# )
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else:
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validate = SpeechSynthesisDataset(
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return_text=True,
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@ -296,22 +262,6 @@ class TtsDataModule:
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raise NotImplementedError(
|
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"On-the-fly feature extraction is not implemented yet."
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)
|
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# sampling_rate = 22050
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# config = MatchaFbankConfig(
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# n_fft=1024,
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# n_mels=80,
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# sampling_rate=sampling_rate,
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# hop_length=256,
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# win_length=1024,
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# f_min=0,
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# f_max=8000,
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# )
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# test = SpeechSynthesisDataset(
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# return_text=True,
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# return_tokens=False,
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# feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
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# return_cuts=self.args.return_cuts,
|
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# )
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else:
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test = SpeechSynthesisDataset(
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return_text=True,
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@ -1,15 +0,0 @@
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export PYTHONPATH=$PYTHONPATH:/home/yuekaiz/icefall_matcha
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#bigvganinference
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model_path=/home/yuekaiz/HF/F5-TTS/F5TTS_Base_bigvgan/model_1250000.pt
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manifest=/home/yuekaiz/HF/valle_wenetspeech4tts_demo/wenetspeech4tts.txt
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manifest=/home/yuekaiz/seed_tts_eval/seedtts_testset/zh/meta_head.lst
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# get wenetspeech4tts
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manifest_base_stem=$(basename $manifest)
|
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mainfest_base_stem=${manifest_base_stem%.*}
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output_dir=./results/f5-tts-pretrained/$mainfest_base_stem
|
||||
|
||||
|
||||
pip install sherpa-onnx bigvganinference lhotse kaldialign sentencepiece
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accelerate launch f5-tts/infer.py --nfe 16 --model-path $model_path --manifest-file $manifest --output-dir $output_dir || exit 1
|
||||
|
||||
bash local/compute_wer.sh $output_dir $manifest
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||||
@ -1,6 +1,5 @@
|
||||
wav_dir=$1
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||||
wav_files=$(ls $wav_dir/*.wav)
|
||||
# wav_files=$(echo $wav_files | cut -d " " -f 1)
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# if wav_files is empty, then exit
|
||||
if [ -z "$wav_files" ]; then
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exit 1
|
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|
||||
@ -1,5 +1,4 @@
|
||||
#!/usr/bin/env bash
|
||||
export PYTHONPATH=$PYTHONPATH:/home/yuekaiz/icefall_matcha
|
||||
set -eou pipefail
|
||||
|
||||
|
||||
@ -7,8 +6,8 @@ set -eou pipefail
|
||||
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
|
||||
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
|
||||
stage=7
|
||||
stop_stage=7
|
||||
stage=1
|
||||
stop_stage=4
|
||||
|
||||
dl_dir=$PWD/download
|
||||
|
||||
@ -101,23 +100,10 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
python3 ./local/display_manifest_statistics.py --manifest-dir ${audio_feats_dir}
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: build monotonic_align lib (used by matcha recipes)"
|
||||
for recipe in matcha; do
|
||||
if [ ! -d $recipe/monotonic_align/build ]; then
|
||||
cd $recipe/monotonic_align
|
||||
python3 setup.py build_ext --inplace
|
||||
cd ../../
|
||||
else
|
||||
log "monotonic_align lib for $recipe already built"
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
subset="Basic"
|
||||
prefix="wenetspeech4tts"
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 6: Generate fbank (used by ./matcha)"
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: Generate fbank (used by ./f5-tts)"
|
||||
mkdir -p data/fbank
|
||||
if [ ! -e data/fbank/.${prefix}.done ]; then
|
||||
./local/compute_mel_feat.py --dataset-parts $subset --split 100
|
||||
@ -125,8 +111,8 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
|
||||
log "Stage 7: Split the ${prefix} cuts into train, valid and test sets (used by ./matcha)"
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 7: Split the ${prefix} cuts into train, valid and test sets (used by ./f5-tts)"
|
||||
if [ ! -f data/fbank/${prefix}_cuts_${subset}.jsonl.gz ]; then
|
||||
echo "Combining ${prefix} cuts"
|
||||
pieces=$(find data/fbank/ -name "${prefix}_cuts_${subset}.*.jsonl.gz")
|
||||
@ -135,17 +121,17 @@ if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
|
||||
if [ ! -e data/fbank/.${prefix}_split.done ]; then
|
||||
echo "Splitting ${prefix} cuts into train, valid and test sets"
|
||||
|
||||
# lhotse subset --last 800 \
|
||||
# data/fbank/${prefix}_cuts_${subset}.jsonl.gz \
|
||||
# data/fbank/${prefix}_cuts_validtest.jsonl.gz
|
||||
# lhotse subset --first 400 \
|
||||
# data/fbank/${prefix}_cuts_validtest.jsonl.gz \
|
||||
# data/fbank/${prefix}_cuts_valid.jsonl.gz
|
||||
# lhotse subset --last 400 \
|
||||
# data/fbank/${prefix}_cuts_validtest.jsonl.gz \
|
||||
# data/fbank/${prefix}_cuts_test.jsonl.gz
|
||||
lhotse subset --last 800 \
|
||||
data/fbank/${prefix}_cuts_${subset}.jsonl.gz \
|
||||
data/fbank/${prefix}_cuts_validtest.jsonl.gz
|
||||
lhotse subset --first 400 \
|
||||
data/fbank/${prefix}_cuts_validtest.jsonl.gz \
|
||||
data/fbank/${prefix}_cuts_valid.jsonl.gz
|
||||
lhotse subset --last 400 \
|
||||
data/fbank/${prefix}_cuts_validtest.jsonl.gz \
|
||||
data/fbank/${prefix}_cuts_test.jsonl.gz
|
||||
|
||||
# rm data/fbank/${prefix}_cuts_validtest.jsonl.gz
|
||||
rm data/fbank/${prefix}_cuts_validtest.jsonl.gz
|
||||
|
||||
n=$(( $(gunzip -c data/fbank/${prefix}_cuts_${subset}.jsonl.gz | wc -l) - 800 ))
|
||||
lhotse subset --first $n \
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
export PYTHONPATH=$PYTHONPATH:/home/yuekaiz/icefall_matcha
|
||||
|
||||
install_flag=false
|
||||
if [ "$install_flag" = true ]; then
|
||||
echo "Installing packages..."
|
||||
|
||||
pip install k2==1.24.3.dev20230524+cuda11.8.torch2.0.1 -f https://k2-fsa.github.io/k2/cuda.html
|
||||
# pip install k2==1.24.4.dev20241030+cuda12.4.torch2.4.0 -f https://k2-fsa.github.io/k2/cuda.html
|
||||
# lhotse tensorboard kaldialign
|
||||
pip install -r requirements.txt
|
||||
pip install phonemizer pypinyin sentencepiece kaldialign matplotlib h5py
|
||||
|
||||
apt-get update && apt-get -y install festival espeak-ng mbrola
|
||||
else
|
||||
echo "Skipping installation."
|
||||
fi
|
||||
|
||||
world_size=8
|
||||
#world_size=1
|
||||
|
||||
exp_dir=exp/f5
|
||||
|
||||
# pip install -r f5-tts/requirements.txt
|
||||
python3 f5-tts/train.py --max-duration 300 --filter-min-duration 0.5 --filter-max-duration 20 \
|
||||
--num-buckets 6 --dtype "bfloat16" --save-every-n 5000 --valid-interval 8000 \
|
||||
--base-lr 1e-4 --warmup-steps 5000 --average-period 200 \
|
||||
--num-epochs 10 --start-epoch 1 --start-batch 20000 \
|
||||
--exp-dir ${exp_dir} --world-size ${world_size}
|
||||
Loading…
x
Reference in New Issue
Block a user