File size: 6,064 Bytes
12da6cc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
import os
import pickle
from functools import partial
from typing import Deque
import fire
import jax
import jax.numpy as jnp
import jax.tools.colab_tpu
import matplotlib.pyplot as plt
import optax
from tqdm.auto import tqdm
from .acoustic_trainer import initial_state, loss_vag, val_loss_fn
from .config import FLAGS
from .data_loader import load_textgrid_wav
from .dsp import MelFilter
from .utils import print_flags
def setup_colab_tpu():
jax.tools.colab_tpu.setup_tpu()
def train(
batch_size: int = 32,
steps_per_update: int = 10,
learning_rate: float = 1024e-6,
):
"""Train acoustic model on multiple cores (TPU)."""
lr_schedule = optax.exponential_decay(learning_rate, 50_000, 0.5, staircase=True)
optimizer = optax.chain(
optax.clip_by_global_norm(1.0),
optax.adamw(lr_schedule, weight_decay=FLAGS.weight_decay),
)
def update_step(prev_state, inputs):
params, aux, rng, optim_state = prev_state
rng, new_rng = jax.random.split(rng)
(loss, new_aux), grads = loss_vag(params, aux, rng, inputs)
grads = jax.lax.pmean(grads, axis_name="i")
updates, new_optim_state = optimizer.update(grads, optim_state, params)
new_params = optax.apply_updates(params, updates)
next_state = (new_params, new_aux, new_rng, new_optim_state)
return next_state, loss
@partial(jax.pmap, axis_name="i")
def update(params, aux, rng, optim_state, inputs):
states, losses = jax.lax.scan(
update_step, (params, aux, rng, optim_state), inputs
)
return states, jnp.mean(losses)
print(jax.devices())
num_devices = jax.device_count()
train_data_iter = load_textgrid_wav(
FLAGS.data_dir,
FLAGS.max_phoneme_seq_len,
batch_size * num_devices * steps_per_update,
FLAGS.max_wave_len,
"train",
)
val_data_iter = load_textgrid_wav(
FLAGS.data_dir,
FLAGS.max_phoneme_seq_len,
batch_size,
FLAGS.max_wave_len,
"val",
)
melfilter = MelFilter(
FLAGS.sample_rate,
FLAGS.n_fft,
FLAGS.mel_dim,
FLAGS.fmin,
FLAGS.fmax,
)
batch = next(train_data_iter)
batch = jax.tree_map(lambda x: x[:1], batch)
batch = batch._replace(mels=melfilter(batch.wavs.astype(jnp.float32) / (2**15)))
params, aux, rng, optim_state = initial_state(optimizer, batch)
losses = Deque(maxlen=1000)
val_losses = Deque(maxlen=100)
last_step = -steps_per_update
# loading latest checkpoint
ckpt_fn = FLAGS.ckpt_dir / "acoustic_latest_ckpt.pickle"
if ckpt_fn.exists():
print("Resuming from latest checkpoint at", ckpt_fn)
with open(ckpt_fn, "rb") as f:
dic = pickle.load(f)
last_step, params, aux, rng, optim_state = (
dic["step"],
dic["params"],
dic["aux"],
dic["rng"],
dic["optim_state"],
)
tr = tqdm(
range(
last_step + steps_per_update, FLAGS.num_training_steps + 1, steps_per_update
),
desc="training",
total=FLAGS.num_training_steps // steps_per_update + 1,
initial=last_step // steps_per_update + 1,
)
params, aux, rng, optim_state = jax.device_put_replicated(
(params, aux, rng, optim_state), jax.devices()
)
def batch_reshape(batch):
return jax.tree_map(
lambda x: jnp.reshape(x, (num_devices, steps_per_update, -1) + x.shape[1:]),
batch,
)
for step in tr:
batch = next(train_data_iter)
batch = batch_reshape(batch)
(params, aux, rng, optim_state), loss = update(
params, aux, rng, optim_state, batch
)
losses.append(loss)
if step % 10 == 0:
val_batch = next(val_data_iter)
val_loss, val_aux, predicted_mel, gt_mel = val_loss_fn(
*jax.tree_map(lambda x: x[0], (params, aux, rng)), val_batch
)
val_losses.append(val_loss)
attn = jax.device_get(val_aux["acoustic_model"]["attn"])
predicted_mel = jax.device_get(predicted_mel[0])
gt_mel = jax.device_get(gt_mel[0])
if step % 1000 == 0:
loss = jnp.mean(sum(losses)).item() / len(losses)
val_loss = sum(val_losses).item() / len(val_losses)
tr.write(f"step {step} train loss {loss:.3f} val loss {val_loss:.3f}")
# saving predicted mels
plt.figure(figsize=(10, 10))
plt.subplot(3, 1, 1)
plt.imshow(predicted_mel.T, origin="lower", aspect="auto")
plt.subplot(3, 1, 2)
plt.imshow(gt_mel.T, origin="lower", aspect="auto")
plt.subplot(3, 1, 3)
plt.imshow(attn.T, origin="lower", aspect="auto")
plt.tight_layout()
plt.savefig(FLAGS.ckpt_dir / f"mel_{step:06d}.png")
plt.close()
# saving checkpoint
with open(ckpt_fn, "wb") as f:
params_, aux_, rng_, optim_state_ = jax.tree_map(
lambda x: x[0], (params, aux, rng, optim_state)
)
pickle.dump(
{
"step": step,
"params": params_,
"aux": aux_,
"rng": rng_,
"optim_state": optim_state_,
},
f,
)
if __name__ == "__main__":
# we don't use these flags.
del FLAGS.batch_size
del FLAGS.learning_rate
del FLAGS.duration_learning_rate
del FLAGS.duration_lstm_dim
del FLAGS.duration_embed_dropout_rate
print_flags(FLAGS.__dict__)
if "COLAB_TPU_ADDR" in os.environ:
setup_colab_tpu()
if not FLAGS.ckpt_dir.exists():
print("Create checkpoint dir at", FLAGS.ckpt_dir)
FLAGS.ckpt_dir.mkdir(parents=True, exist_ok=True)
fire.Fire(train)
|