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import ast
import logging
import os
import os.path as op
import sys
from argparse import Namespace
import numpy as np
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progress_bar
from omegaconf import DictConfig
# define function for plot prob and att_ws
def _plot_and_save(array, figname, figsize=(6, 4), dpi=150):
import matplotlib.pyplot as plt
shape = array.shape
if len(shape) == 1:
# for eos probability
plt.figure(figsize=figsize, dpi=dpi)
plt.plot(array)
plt.xlabel("Frame")
plt.ylabel("Probability")
plt.ylim([0, 1])
elif len(shape) == 2:
# for tacotron 2 attention weights, whose shape is (out_length, in_length)
plt.figure(figsize=figsize, dpi=dpi)
plt.imshow(array, aspect="auto")
elif len(shape) == 4:
# for transformer attention weights,
# whose shape is (#leyers, #heads, out_length, in_length)
plt.figure(figsize=(figsize[0] * shape[0], figsize[1] * shape[1]), dpi=dpi)
for idx1, xs in enumerate(array):
for idx2, x in enumerate(xs, 1):
plt.subplot(shape[0], shape[1], idx1 * shape[1] + idx2)
plt.imshow(x, aspect="auto")
plt.xlabel("Input")
plt.ylabel("Output")
else:
raise NotImplementedError("Support only from 1D to 4D array.")
plt.tight_layout()
if not op.exists(op.dirname(figname)):
# NOTE: exist_ok = True is needed for parallel process decoding
os.makedirs(op.dirname(figname), exist_ok=True)
plt.savefig(figname)
plt.close()
# define function to calculate focus rate
# (see section 3.3 in https://arxiv.org/abs/1905.09263)
def _calculate_focus_rete(att_ws):
if att_ws is None:
# fastspeech case -> None
return 1.0
elif len(att_ws.shape) == 2:
# tacotron 2 case -> (L, T)
return float(att_ws.max(dim=-1)[0].mean())
elif len(att_ws.shape) == 4:
# transformer case -> (#layers, #heads, L, T)
return float(att_ws.max(dim=-1)[0].mean(dim=-1).max())
else:
raise ValueError("att_ws should be 2 or 4 dimensional tensor.")
def main(cfg: DictConfig):
if isinstance(cfg, Namespace):
cfg = convert_namespace_to_omegaconf(cfg)
assert cfg.common_eval.path is not None, "--path required for generation!"
assert (
cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw"
), "--replace-unk requires a raw text dataset (--dataset-impl=raw)"
if cfg.common_eval.results_path is not None:
os.makedirs(cfg.common_eval.results_path, exist_ok=True)
return _main(cfg, sys.stdout)
def _main(cfg: DictConfig, output_file):
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=output_file,
)
logger = logging.getLogger("speecht5.generate_speech")
utils.import_user_module(cfg.common)
assert cfg.dataset.batch_size == 1, "only support batch size 1"
logger.info(cfg)
# Fix seed for stochastic decoding
if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
np.random.seed(cfg.common.seed)
utils.set_torch_seed(cfg.common.seed)
use_cuda = torch.cuda.is_available() and not cfg.common.cpu
if not use_cuda:
logger.info("generate speech on cpu")
# build task
task = tasks.setup_task(cfg.task)
# Load ensemble
logger.info("loading model(s) from {}".format(cfg.common_eval.path))
overrides = ast.literal_eval(cfg.common_eval.model_overrides)
models, saved_cfg = checkpoint_utils.load_model_ensemble(
utils.split_paths(cfg.common_eval.path),
arg_overrides=overrides,
task=task,
suffix=cfg.checkpoint.checkpoint_suffix,
strict=(cfg.checkpoint.checkpoint_shard_count == 1),
num_shards=cfg.checkpoint.checkpoint_shard_count,
)
logger.info(saved_cfg)
# loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
# optimize ensemble for generation
for model in models:
if model is None:
continue
if cfg.common.fp16:
model.half()
if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
model.cuda()
model.prepare_for_inference_(cfg)
# load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(cfg.dataset.gen_subset),
max_tokens=cfg.dataset.max_tokens,
max_sentences=cfg.dataset.batch_size,
max_positions=None,
ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
seed=cfg.common.seed,
num_shards=cfg.distributed_training.distributed_world_size,
shard_id=cfg.distributed_training.distributed_rank,
num_workers=cfg.dataset.num_workers,
data_buffer_size=cfg.dataset.data_buffer_size,
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
)
for i, sample in enumerate(progress):
if "net_input" not in sample:
continue
sample = utils.move_to_cuda(sample) if use_cuda else sample
outs, _, attn = task.generate_speech(
models,
sample["net_input"],
)
focus_rate = _calculate_focus_rete(attn)
outs = outs.cpu().numpy()
audio_name = op.basename(sample['name'][0])
np.save(op.join(cfg.common_eval.results_path, audio_name.replace(".wav", "-feats.npy")), outs)
logging.info(
"{} (size: {}->{} ({}), focus rate: {:.3f})".format(
sample['name'][0],
sample['src_lengths'][0].item(),
outs.shape[0],
sample['dec_target_lengths'][0].item(),
focus_rate
)
)
if i < 6 and attn is not None:
import shutil
demo_dir = op.join(op.dirname(cfg.common_eval.results_path), "demo")
audio_dir = op.join(demo_dir, "audio")
os.makedirs(audio_dir, exist_ok=True)
shutil.copy(op.join(task.dataset(cfg.dataset.gen_subset).audio_root, sample['tgt_name'][0] if "tgt_name" in sample else sample['name'][0]), op.join(audio_dir, audio_name))
att_dir = op.join(demo_dir, "att_ws")
_plot_and_save(attn.cpu().numpy(), op.join(att_dir, f"{audio_name}_att_ws.png"))
spec_dir = op.join(demo_dir, "spec")
_plot_and_save(outs.T, op.join(spec_dir, f"{audio_name}_gen.png"))
_plot_and_save(sample["target"][0].cpu().numpy().T, op.join(spec_dir, f"{audio_name}_ori.png"))
def cli_main():
parser = options.get_generation_parser()
args = options.parse_args_and_arch(parser)
main(args)
if __name__ == "__main__":
cli_main()
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